Air Transport Resilience, Tourism and Its Impact on Economic Growth

Abstract

The aims of this study are to evaluate the influence of air transport and tourism on economic growth in selected Southeast Asian countries such as Thailand, Philippines, Vietnam, Indonesia, Malaysia, and Singapore in the period 1970 to 2021. The study applies the ordinary least squares (OLS), fixed effects (FEM), and random effects (REM), especially to robustness test of the research results by deploying the DOLS, and IV-GMM regression for endogeneity and autocorrelation analysis. The research results confirmed that air transport has a significant and positive impact on economic growth, especially because the positive impact increased in normal economic conditions and decreased during the COVID-19 pandemic. Therefore, if the air transport recovers, it is likely to boost economic development. In addition, there is no impact of tourism on economic growth. The research results also confirmed the positive impact of foreign direct investment and international trade on the economic growth of Southeast Asian countries; however, there is a negative impact of renewable energy consumption on economic growth.

1. Introduction

Socio-economic development in each country always requires meeting travel needs, facilitating trade and goods exchange, tourism, and investment, as well as economic development. Therefore, minimizing transportation costs significantly contributes to reducing logistics costs and improving the operational efficiency of enterprises in particular and the economy in general which is considered a driving force for economic growth. Further, countries always promote investment in essential infrastructure to meet travel and economic needs, including investment in road, and especially in air transport.

Transport by air is more convenient than transportation by land and sea, thanks to its fast speed and capacity to quickly respond to all needs in the economy. The aviation industry is no longer able to meet domestic or international travel demands. It can be said that the tourism industry has significantly developed; the contribution of the aviation industry is indispensable. Therefore, a country that needs to have a developed aviation industry can create many conditions for domestic and foreign tourists to have great travel experiences in different locations with fast connection times. In addition, the tourism sector is gradually becoming a clean industry and contributes to economic growth in most countries (Kyara et al. 2021; V. C. Nguyen 2023).

Since the end of 2020, the world has experienced the negative impact of the COVID-19 pandemic which has crippled the aviation industry, in particular, and many economies. The COVID-19 pandemic has affected travel activities as many countries have implemented social distancing and movement control policies to limit the spread of the disease. This process makes it impossible for tourists to travel; thus greatly affects the aviation industry of most countries, and this partly affects economic growth in the period from 2020 to the present. Although the pandemic has been controlled and the economies of many countries have begun to recover, it is still not able to recover to their level pre-pandemic period.

Research on the influence of air transport on economic growth have been conducted through a number of studies and most of them confirm the positive impact of air transport on the economy. They confirm the correctness of policy of governments of countries in investing in transport infrastructure and the aviation industry to meet the requirements of economic development. However, research on this relationship has not been conducted due to the effects of the COVID-19 pandemic as it has fundamentally altered economic development. In fact, the COVID-19 pandemic has negatively affected other economic performance, but Monteiro et al. (2021) showed that the tourism industry have the greatest negative impact. Indeed, the tourism industry is associated with travel from one region to another or one country to another, so the pandemic can spread quickly between geographical areas. According to European Parliament (2019), the aviation industry is closely related to the tourism industry, in which the proportion of movements through air travel increased rapidly from 46% in 2000 to 58% in 2018, while the movement on roads, railways and seas/water decreased sharply. Therefore, the COVID-19 pandemic simultaneously has the strongest negative impact on air travel and the tourism industry while it also affects other industries to a lesser extent (Monteiro et al. 2021).

Southeast Asia is located on an international sea route and is considered as a dynamic level of economic development and deep integration. According to Misrahi (2017), Southeast Asia’s tourism industry has experienced outstanding growth and always maintains a growth rate of over 8% annually. Countries that are likely to attract more domestic and international tourists are Singapore, Indonesia, Vietnam, Malaysia, and Thailand while other countries such as the Philippines, Myanmar or Laos have slower development. The edge Southeast Asia has over other region is possessing rich and diverse natural resources and making diverse and friendly tourism development policies. Despite being affected by the pandemic, Linh (2023) believed that the smokeless industry in Southeast Asia still maintains impressive development along with the pandemic economic recovery. In addition, Southeast Asia also develops its aviation industry with many airlines from Singapore, Thailand, Indonesia, Vietnam, and Malaysia growing rapidly and turning this place into one of the busiest areas in the region.

Previous studies by Q. H. Nguyen (2023), Law et al. (2022), and Ali et al. (2023) show that the aviation industry should be considered as an important sector for economic growth along with an open-door dispostion through a friendly visa policy. It has huge potential to boost domestic aviation industry and international economic development. Another possibility, Kyara et al. (2021) and Singh and Alam (2024) also confirmed that the aviation industry is closely linked to the tourism industry. Developing the tourism industry can add huge benefits to the economy due to its contributions to employment, promoting consumption of domestic goods and the economy.

Therefore, this study aims to assess the influence of air transport on economic growth and take into account the impact of the pandemic. The novelty of the study lies in the fact that there has been no similar study to assess the moderating role of the pandemic on the tourism and aviation industry and its impact on economic growth in Southeast Asia. Secondly, the study also evaluated the long-term impact through DOLS (Dynamic ordinary least squares) to assess the relationship of the pandemic on the tourism and aviation industry and by extention on economic growth in the long term. Therefore, research results can provide policy implications for governments in dealing with challenges and shocks affecting the economy in the future.

In addition to the above introduction, the rest of the study is as follows: the next section discusses the literature review, then the study addresses data collection and research methodology, and finally, the study discusses results and conclusions.

2. Literature Review

2.1. Air Transport and Economic Growth

Economic growth is an increase in gross domestic product, or gross national product, or possibly an increase in per capita income. Achieving economic growth is always on the agenda of most countries, as an economic development target and on the path to shared prosperity. To achieve economic growth, economies need to increase the contribution of capital and labor resources, and especially improve the level of technology.

Economic performance always requires the reduction of logistics costs in order to reduce the cost on the economy and improve the efficiency of enterprises. Air transport is a mode of transport that makes an important contribution to the economic growth of countries by transporting goods for industries, promoting trade and investment, tourism and creating jobs, and at the same time contributing to the socio-economic development in remote areas. In addition, developed economic performance has created demand for air transport while improving the infrastructure and business environment to develop the aviation industry. Therefore, looking closely at the relationship between air transport and economic growth has always attracted the attention of scholars to serve as a basis for policymaking at the micro and macro levels. Previous research results confirm the positive impact of air travel on economic growth, such as studies by Khalil et al. (2007), Q. H. Nguyen (2023), Law et al. (2022) and Ali et al. (2023), and Ansari (2024). Research by Law et al. (2022) affirms that improving the transport system is a key factor for economic growth. Air connectivity not only contributes to economic growth, but also creates jobs, trade, tourism, and sustainable development. Law et al. (2022) argued that the open aviation policy in Cambodia, Laos, Myanmar and Vietnam in recent years has facilitated the development of the aviation industry and attracted foreign direct investment. Indeed, these four countries share a common history, quite similar in aviation industry development and economic situations, the research results suggested that there is a two-way causal relationship between air passenger traffic and economic growth in the long-run. Furthermore, domestic tourism has a significant impact on air transport demand in the long-run but no significant relationship in the short run. This suggests that countries should develop sustainable tourism and create a driving force for economic growth in the long term rather than short-term benefits. The study also suggests that creating facilitation for the aviation industry is likely to have a positive impact on traffic and this has a very positive effect on the economy. In addition, Q. H. Nguyen (2023) conducted research on a number of Asian economies in the period from 1975 to 2019 and used the analysis of an autoregressive- distributed lag (ARDL). The research results confirmed the bidirectional in most of the studied countries. However, Q. H. Nguyen (2023) argued that there is only a one-way relationship between economic growth and air transport in some South Asian countries but no relationship between air travel and economic growth. The study also confirms a long-term causal relationship between air transport and growth, which suggests that countries should develop the aviation industry to create momentum for long-term economic growth.

Although, there is a relationship between air transport demand and economic development, false signals that provided to policymakers, airline managers, logistics and tourism companies may have a certain influence on the industry development strategy. Thus, properly assessing this relationship can provide appropriate policy implications for countries in their socio-economic development strategies. Hakim and Merkert (2016) studied South Asia between 1973 and 2014 and suggested that there is a one-way Granger causality in the long-run from GDP to air passenger traffic and transport volume, but not in the opposite direction. The study also suggests that there is a definite 3–4-year lag to guide airline companies and policymakers to prepare essential infrastructure to support aviation industry growth, creating leverage for socioeconomic development. In another study by Marazzo et al. (2010) for Brazil in the context of 2006, noted that the country had 40 million passengers, an increase of 150% compared to 1996, representing the impressive growth of the Brazilian aviation industry, which is supported by the stable monetary and policy related to aviation. Furthermore, the new competitive business model has established air transport with a focus on cost and price differentiation, expanding markets and improving competition. Marazzo et al. (2010) also affirmed that economic growth and air transport demand have a relationship in the long-run. The authors argued that the demand for air transport responds positively to the change of economic growth, and the economic growth responds to the change in air transport but to a slower extent. These results can be considered as an explanation of the impact of air transport on the economy; it is necessary to accumulate in terms of time to help the aviation industry have a more positive impact on the economy.

However, some other studies suggested that there are also negative impacts of air transport on the economy, such as negative impacts on the air environment and noise, causing traffic congestion and thereby negatively affecting economic growth (Janić 1999). Similarly, Calderon-Tellez and Herrera (2021) suggested that countries should reduce the negative impacts of the aviation industry to harmonize economic development and environmental protection in order to promote sustainable development policies. Furthermore, Khanal et al. (2022) suggested that there may be negative shocks in the aviation industry, and these shocks also affect economic growth and development.

2.2. Tourism and Economic Growth

The tourism industry is considered a smokeless industry to meet the consumption and relaxation needs of people around the world. Each country creates legal conditions for clear immigration, investment in destinations and tourism services to develop the tourism industry and create more jobs and develop the economy. Singh and Alam (2024) and Ansari (2024) believed that the tourism industry is growing to become an important industry with strong promises of development locally, regionally and nationally. Another possibility, tourism has become important in less developed economies due to the tourism-led economic growth hypothesis and the tourism-led export-led growth hypothesis. Kyara et al. (2021) indicate that the tourism is a fast-growing industry globally and contributes over 10% to the global gross domestic product, about 330 million jobs and about 10% of the global employment. The growth rate of the tourism industry is considered to be always higher than the global economic growth, confirming the increasingly greater contribution of the tourism industry to the economy. The tourism industry not only creates jobs, reduces poverty, and increase foreign exchange, it also promotes indigenous goods and economic development.

Research by Singh and Alam (2024) confirms that tourism development will stimulate economic growth, so the economy needs to allocate resources to the tourism industry to invest in generating income for the workforce, and opportunities related to economic sectors. In addition, Singh and Alam (2024) researched in India and indicated that investment in tourism has a two-way relationship with economic growth, meaning investment in tourism promotes growth and growth promotes investment in tourism. The study also suggests that foreign tourist arrivals have the potential to expand economic growth. Therefore, the study supports the hypothesis of long-term growth of tourism for economic development, therefore, governments need to support private enterprises to invest in tourism industry to develop the country’s economy. In another case, Kyara et al. (2021) studied the case of Tanzania and confirmed the relationship between tourism development and economic growth, and this requires the government of this country to focus on strategies to encourage sustainable tourism development as a driving force for economic growth.

However, the relationship between tourism development and economic growth cannot be clearly affirmed, especially in the short term. Dhungel (2015) believed that the tourism industry does not contribute much to the Nepali economy, but a small change in income from the tourism industry can bring a great economic performance. The casual analysis also shows that there is no short-term causal relationship between tourism development and economic growth, but there may be a long-term causal relationship, thereby affirming that the Nepal government should have a solution to develop tourism in the long term in order to optimize socio-economic benefits. The results of this study are similar to Oh (2005), which suggests that the tourism-led economic growth hypothesis is not found in Korea, especially in the short-term impact. Wijesekara et al. (2022) research on 105 countries within the period 2003 to 2020 also confirmed that tourism significantly contributes to economic growth, while economic growth promotes investment in tourism and basic infrastructure, thereby developing the tourism industry. In addition, Cárdenas-García et al. (2024) also confirmed a one-way causal relationship between countries with low levels of specialization and tourism development, which confirms that tourism development brings great benefits to economic growth in countries with low levels of tourism development and this benefit gradually decreases in countries with high levels of development.

Some other studies also suggested that there are negative impacts of tourism development on the environment and socio-economy (Baloch et al. 2023). Although tourism development leads to socio-economic development when more people are able to have jobs, conduct business and develop tourism infrastructure, the impact of tourism on the environment is threatening environmental quality. In addition, social damage due to overuse of land, cultural intrusion from abroad, air and environmental pollution, accumulation of solid waste, wastewater, and carbon emissions have negative impacts on sustainable development (Baloch et al. 2023; Postma and Schmuecker 2017). Another possiblity, Dhungel (2015) believed that the tourism industry does not contribute much to the Nepali economy in the short term.

2.3. Air Transport, Tourism and Economic Growth in the Pandemic

Air transport is one of the most important inventions for mankind to meet the needs of fast and time-saving freight transportation, and to increase economic activities. Air transport promotes economic growth through the development of other sectors, which reflects the very positive impact of aviation on the economy in the short and long term. Ali et al. (2023) conducted a survey in BRICS countries using balance sheet data within the period 1993 to 2019 and affirmed that air transport is a driver of economic activity. In particular, there is a one-way long-term relationship from the process of air transport through the provision of passenger travel needs to the economic growth of the BRICS countries. Moreover, there is a one-way short-term causal relationship between air transport to economic growth, confirming that the aviation industry always has practical benefits for the economy in both the short and long term. Increasing the supply of aviation products and services is the driving force for economic development.

As can be seen, previous studies of Marazzo et al. (2010), Law et al. (2022), and Ali et al. (2023) all confirmed the positive impact of air transport on economic growth. Research results have been confirmed their finding in BRICS, South Asia and several other countries. In addition, Manzoor et al. (2019), Rasool et al. (2021), De Siano and Canale (2022) suggested that the tourism industry may be the third largest export industry after petroleum and chemicals, and tourism development has a reciprocal influence on economic growth, which posits the role of the tourism industry for the economic development of many countries. Furthermore, Ozer Balli et al. (2019) also argued that air travel is often associated with tourism demand, especially international tourism because air travel has the ability to provide timely transportation services to customers, and thus the development of this industry. Aviation significantly brings benefits to the tourism industry and developing the tourism industry enhances certain benefits to the aviation industry. In addition, previous studies of Singh and Alam (2024), Kyara et al. (2021) also confirmed that tourism has a two-way relationship with economic growth, meaning that tourism promotes economic growth while economic growth promotes tourism.

However, countries have implemented social distancing measures and limited movement in the COVID-19 pandemic, which has affected both the aviation and tourism industry, causing these industries to be affected heavily, including Southeast Asian countries. Gagnon et al. (2023) indicated that the pandemic has a negative impact on economic activities, however, the degree of impact of the pandemic is different and depends on each country. In particular, the lockdown policy has a negative and greater impact on emerging and developing countries than on other countries. Xiang et al. (2021) highlighted the negative impact of the pandemic on the overall economy, especially health-related issues and medical costs. Therefore, the government needs to balance pandemic prevention and control costs and follow national health policy. Once the pandemic is under control, the economy needs economic packages to restore production and consumption. Nandi and Chauhan (2022), Ghecham (2022) also affirmed that the pandemic caused a shock to the global economy and sustainable development goals. However, taking advantage of the technological revolution to restore the economy and put it on a growth trajectory.

However, there has been no research evaluating this relationship during the COVID-19 pandemic when the pandemic has had many negative impacts on the aviation industry and economic growth of many countries, especially in Southeast Asia. The COVID-19 pandemic has brought about social impacts due to the implementation of travel restrictions and especially social distancing that has crippled the aviation industry for a long time. Moreover, the economic growth of many countries fell into recession and it takes a while to recover like the pre-pandemic period. Therefore, assessing the effects of aviation on economic growth in Southeast Asia is the main objective and creates novelty in this study.

3. Hypotheses Development

H1. 

The air transport has a positive and significant impact on economic growth in selected Southeast Asian countries.

Southeast Asian countries have rapidly growing aviation markets, especially Singapore, Thailand or Malaysia are becoming regional aviation hubs while aviation industry in Vietnam, Indonesia or Philippines is also grown. Research by Q. H. Nguyen (2023), Law et al. (2022) and Ali et al. (2023) argued for the positive impact of air travel on economic growth. Further, Law et al. (2022) also confirmed that improving the transport system is a key factor for economic growth, and creates jobs, trade, tourism, and sustainable development. Another possibility, Marazzo et al. (2010) also affirmed that the aviation industry has a positive relationship with economic growth, therefore developing the aviation industry brings many benefits to the economy. Further, many countries significantly implement open-sky policies to develop the aviation industry and the ability to attract international capital flows while improving the transportation infrastructure system and creating a foundation for economic development in the long term. Normally, investing in infrastructure requires large capital investment and a long payback period, so this long-term investment has the ability to promote economic efficiency in the long term, usually 3 to 4 years after investment (Hakim and Merkert 2016).

H2. 

The tourism industry has a positive and significant impact on economic growth in selected Southeast Asian countries.

With the advantage of geographical location and diversification of natural resources, Southeast Asian countries are considered to have a fast-growing tourism industry and are capable of maintaining an annual growth rate of 8% (Misrahi 2017). Research by Singh and Alam (2024) and Kyara et al. (2021) supported that the tourism industry has a positive impact on growth. Therefore, developing the tourism industry has the ability to create leverage for economic growth. Recently, the tourism industry has witnessed rapid development and has become a smokeless industry contributing up to 10% of global GDP and more than 300 million jobs (Kyara et al. 2021). Therefore, developing tourism brings more economic growth. In addition, Singh and Alam (2024) also confirmed the positive impact of tourism on growth through its ability to create jobs and output in the economy. In particular, international tourists bring foreign currency and spend it domestically, and are therefore considered as an additional source of foreign currency and consumption of domestic products. Furthermore, this relationship becomes stronger in the long term while the short-term impact may not be clear (Dhungel 2015).

H3. 

The pandemic influences on the relationship between air transport, tourism and economic growth in selected Southeast Asian countries.

The COVID-19 pandemic negatively affected the travel and aviation industry (Monteiro et al. 2021). However, Gagnon et al. (2023) indicated that the pandemic has a negative impact on economic performance, but the degree of impact of the pandemic is different. Ozer Balli et al. (2019) significantly argued that air travel is often associated with tourism demand, thus, it has a huge influence on economic growth. In the case of Southeast Asia, the COVID-19 pandemic negatively affected both the aviation and tourism industry and economic growth (Gagnon et al. 2023). Therefore, assessing the impact of the pandemic on the relationship between air transport, tourism and economic growth in selected Southeast Asian countries becomes urgent.

4. Data and Methodology

We use data from selected Southeast Asian countries for this analysis. Selected countries have large economic scale, and have an aviation market that is assessed to be developed. These countries include Thailand, Philippines, Vietnam, Indonesia, Malaysia, and Singapore.

For this study, we use data from the World Bank. Some data we collected from each country’s Bureau of Statistics, are published annually. Data collection period is from 1970 to 2021. Before using data in analysis, we corrected errors of data.

The study inherits from the previous work of Ali et al. (2023). We have adjusted for this study, especially, since Sijabat (2023) suggested that FDI has the ability to spread productivity, innovation and technological transfer to the recipient country while promoting international trade and increasing benefits to the economy. Further, Zangoei et al. (2021) argued that using appropriate energy sources makes the economy achieve high growth efficiency. Therefore, in addition to the main factors of AIR, TOURISM, this study extends the control variables TRADE, FDI and REN, then the regression equation as follows:

The first equation:

$${GROW}_{it}={\beta }_0+{\beta }_1{AIR}_{it}+{\beta }_2{TOURISM}_{it}+{\beta }_3{TRADE}_{it}+{\beta }_4{FDI}_{it}+{\beta }_5{REN}_{it}+{\epsilon }_{it}$$

(1)

Specifically, the regression coefficients of AIR and TOURISM at Equation (1) have a positive sign because the aviation industry as well as the tourism industry has made a huge contribution to the socio-economic development in Southeast Asia as discussed by Linh (2023).

The second equation:

$${GROW}_{it}={\beta }_0+{\beta }_{11}{AIR}_{it}\ast {COVID}_t+{\beta }_2{TOURISM}_{it}+{\beta }_3{TRADE}_{it}+{\beta }_4{FDI}_{it}+{\beta }_5{REN}_{it}+{\epsilon }_{it}$$

(2)

Therefore, the third equation could be written as follows:

$${GROW}_{it}={\beta }_0+{\beta }_{12}{TOURISM}_{it}\ast {COVID}_t+{\beta }_3{TRADE}_{it}+{\beta }_4{FDI}_{it}+{\beta }_5{REN}_{it}+{\epsilon }_{it}$$

(3)

Due to the negative impact of the COVID-19 pandemic, the estimated coefficients of the interaction effect between tourism, aviation section and the pandemic have a negative sign at Equations (2) and (3).

The variables’ description is shown in the following

Table 1. Variables description.

Variable	Explanation	Measurement	Expected Sign	Previous Studies	Source
Dependent variable
GROW	Economic growth	Per capita income			WDI
Independent variable
AIR	The development of the aviation industry	The annual number of passengers	+	Law et al. (2022); Q. H. Nguyen (2023); Ali et al. (2023)	WDI
TOURISM	The level of tourism development	The annual number of tourists	+	Kyara et al. (2021); Singh and Alam (2024)	WDI
TRADE	Trade openness	Trade (%GDP)	+	Keho and Grace Wang (2017)	WDI
FDI	Foreign direct investment	Foreign direct investment, net inflows, % GDP	+	Sijabat (2023)	WDI
REN	Renewables’ development	The share of renewable energy consumption in total energy demand	-	Zangoei et al. (2021)	WDI

Source: Authors’ compilation.

.

The dependent variable GROW: is a measurement of the level of economic development of each country on an annual basis. According to Harvie et al. (2009), or Sharma (2018), Ali et al. (2023) this indicator can be measured through per capita income.

As for the dependent variable AIR, it is measured for the level of development of the aviation industry, and measured by the annual number of passengers (Gunter and Zekan 2021). Similarly, Ali et al. (2023) also proposed this measurement in BRICS countries.

For the dependent variable TOURISM, it measures the level of development of the tourism industry and is valued by the annual number of tourists (Godovykh and Ridderstaat 2020). Ozer Balli et al. (2019) also believed that the annual number of tourists is a good quantitative indicator to measure the level of tourism industry development because this indicator can be measured accurately.

For the dependent variable, TRADE, it measures the level of trade openness of the economy (Fatima et al. 2020). For FDI, it measures foreign direct investment as discussed by Sijabat (2023). For REN, which measures renewable energy consumption, and is discussed by Sahlian et al. (2021).

The research uses quantitative analysis based on some advanced techniques. Firstly, the traditional quantitative regression should be performed such as ordinary least squares (OLS), fixed effects (FEM) and random effects (REM). The study also implements F-test and Hausman tests, at the same time the heteroskedasticity and autocorrelation. When there are defects, the FGLS regression method should be performed. Indeed, Nguyen and Huynh (2023) argued that FGLS regression helps the model avoid the heteroskedasticity and autocorrelation problems and thus the model achieves the best results. In addition, to test the robustness of the research results, we use regression analysis according to DOLS.

To assess invariance across countries over time, the study uses FEM regression that is fixed by year or by country. Specifically, the countries in Southeast Asia selected in this study have similar cultural characteristics, economic conditions and a high degree of dependence on each other. Therefore, countries with yearly economic fluctuations often have similar characteristics. Therefore, the study performed country or year-fixed effects regression to further clarify the similarities in culture and economic conditions among countries in Southeast Asia as discussed by Yang et al. (2019).

The development of tourism and aviation in Southeast Asia has occurred continuously in recent times and is likely to grow in the long term, therefore, research should evaluate the role of tourism and aviation in boosting long-term economic growth. To analyze the long-term relationship, the study uses the DOLS (Dynamic ordinary least squares). Kao and Chiang (2001) indicated that the estimator gives the long-run elasticities, and the method is based on the cointegration relationship between the variables analyzed in the model. Further, the results based on DOLS is considered the robustness of the study. Sijabat (2023) also argued that DOLS is also appropriate in the case of endogeneity and autocorrelation, thus making the estimation results more effective. Rahman et al. (2021) believed that DOLS regression is capable of assessing long-term impacts and it is possible to predict the long-term impacts of factors when co-integration occurs. Typically, the effects of tourism and aviation on economic growth are felt over a number of years, so this estimate is necessary. Another possibility, Gagnon et al. (2023) believed that it is able to take a few more years for the world economy to return to its pre-pandemic growth trajectory, so economic growth is likely to recover after a certain period.

Because the panel data has a fairly long period of time T, while the number of countries is only limited to the Southeast Asia region, so T > N, the study does not have to consider endogeneity in the estimation model as discussed by Roodman (2009). However, this study will also use GMM regression to evaluate the robustness of the study as discussed by Nguyen and Huynh (2023). According to Conley (1999), GMM regression is also suitable for cross-sectional dependence involving time-series data or studies involving time. In addition, Hong et al. (2023) and Wang et al. (2022) confirmed that GMM estimation becomes efficient with short temporal and large spatial data, so cross-data dependence can occur stronger than temporal dependence.

5. Results

5.1. Descriptive Statistics

Table 2. Descriptive statistics.

Variable	Mean	Std. Dev.	Min	Max
GROW	6357.91	12,851.25	78.86	77,710.07
AIR	1.61 × 107	2.00 × 107	6000	1.15 × 108
TOURISM	1.04 × 107	8,263,575	1,351,000	3.99 × 107
TRADE	135.76	107.05	18.95	437.32
FDI	4.42	5.74	−2.75	32.69
REN	25.03	19.64	0.19	75.91

Source: Authors’ analysis.

depicts the description of the variables used in the estimation. For GDP per capita, the average value is 6357.91 USD/person/year, however, the income level among countries in the sample is relatively different. Singapore has a high per capita income, while Vietnam and the Philippines are the two countries with the lowest per capita income. Regarding AIR, this coefficient shows that the ability of countries to carry passengers is high, but there was a large decrease during the pandemic period due to the impact of travel restrictions and social distancing policies of many countries. At the same time, Southeast Asia is now capable of attracting international tourists with the top destinations being Singapore and Thailand, but during the pandemic, it also affects tourist attraction to the area. In terms of trade openness, Singapore is a country with a high degree of openness when foreign trade used to reach 437.3267% of GDP, much higher than the Philippines and other countries. Similarly, FDI is also more successful than other countries in attracting foreign direct investment. Other countries, such as Vietnam, Malaysia, and Thailand, are also emerging as foreign direct investment destinations. Regarding renewable energy consumption, this energy source has a decreasing contribution to the energy demand in the region, especially Singapore has almost no renewable energy sources.

5.2. Correlation Matrix

Table 3. Correlation analysis.

Variable	GROW	AIR	AIRCOVID	TOURISM COVID	TOURISM	TRADE	FDI	REN
GROW	1.0000
AIR	0.5147	1.0000
AIRCOVID	0.0538	0.2978	1.0000
TOURISMCOVID	0.6003	0.9407	0.3771	1.0000
TOURISM	0.6275	0.7799	0.4089	0.9452	1.0000
TRADE	0.7884	−0.0015	0.0195	0.1511	0.2960	1.0000
FDI	0.6939	−0.0028	−0.0182	0.0762	0.1494	0.8380	1.0000
REN	−0.8927	−0.4376	−0.0472	−0.5645	−0.6407	−0.7193	−0.5020	1.0000

Source: Authors’ analysis.

shows the correlation analysis of the variables used in the regression model. Correlation analysis results show that most of the independent variables have low correlation, so there is no possibility of multicollinearity. However, the pandemic-related variables should be regressed separately to avoid multicollinearity.

5.3. Panel Unit Root Test and Panel Cointegration Test

The results of

Table 4. Panel unit root test.

Variable	Order of Integration	IPS Test	IPS Test with Trend	Hypothesis
GROW	I(0) I(1)	−1.4800 * −7.7489 ***	−2.1466 ** −8.1496 ***	Not Rejected Rejected
AIR	I(0) I(1)	−1.5678 * 4.1760 ***	−5.6362 *** −6.4654 ***	Not Rejected Rejected
TOURISM	I(0) I(1)	0.5530 −1.0146 *	0.3291 −0.3750	Not Rejected Not Rejected
TRADE	I(0) I(1)	−0.9400 −9.5557 ***	−0.7106 −9.7744 ***	Not Rejected Rejected
REN	I(0) I(1)	−0.8387 −6.2566 ***	−1.6035 * −6.8049 ***	Not Rejected Rejected

Note: *, **, *** significance at 10%, 5%, 1%.

and

Table 5. Panel cointegration test.

Method	Cointegration Test	Statistics	Hypothesis
Pedroni	Modified Phillips-Perron t Phillips-Perron t Augmented Dickey-Fuller t	2.6753 *** 1.3614 * 1.2500	Rejected Rejected Not Rejected
Kao	Modified Dickey-Fuller t Dickey-Fuller t Augmented Dickey-Fuller t Unadjusted modified Dickey-Fuller t Unadjusted Dickey-Fuller t	−2.5925 *** −0.8568 −0.5545 −4.0646 *** −1.3865 *	Rejected Not Rejected Not Rejected Rejected Rejected

Note: *, *** significance at 10%, 1%.

show that the variables are stationary at the I(0) or I(1), and according to the Pedroni and Kao tests, it shows that cointegration is likely to occur, and therefore, there is likely to be a long-term impact between factors.

5.4. Regressions and Discussions

Through the regression results of panel data regression using traditional methods, such as FEM, REM and OLS, the study also evaluates the robustness of the research results through DOLS to estimate the long-run relationship between variables, FE year and FE country, as well as GMM regression to assess the possibility of endogeneity, the general results of the specific study are as follows:

Table 6. Estimation results (Equation (1)).

Variable	OLS	FEM	REM	FGLS	FE Year	FE Country	DOLS	IV-GMM
AIR	0.5831 *** (0.000)	0.5869 *** (0.000)	0.5831 *** (0.000)	0.5831 *** (0.000)	0.1682 *** (0.000)	0.5869 *** (0.000)	0.5809 *** (0.000)	0.5831 *** (0.000)
TOURISM	−0.1251 ** (0.049)	0.0062 (0.936)	−0.1251 ** (0.047)	−0.1251 ** (0.043)	0.0738 (0.169)	0.0062 (0.935)	−0.1313 * (0.078)	−0.1251 ** (0.043)
TRADE	0.0015 *** (0.000)	−0.0008 ** (0.023)	0.0015 *** (0.000)	0.0015 *** (0.000)	0.0001 (0.823)	−0.0008 (0.022)	0.0013 *** (0.005)	0.0015 *** (0.000)
FDI	0.0206 *** (0.000)	0.0039 (0.154)	0.0206 *** (0.000)	0.0206 *** (0.000)	−0.0001 (0.923)	0.0039 (0.152)	0.0262 *** (0.000)	0.0206 *** (0.000)
REN	−0.0147 *** (0.000)	−0.0078 *** (0.000)	−0.0147 *** (0.000)	−0.0147 *** (0.000)	−0.0096 *** (0.000)	−0.0078 *** (0.000)	−0.0125 *** (0.000)	−0.0147 *** (0.000)
_cons	0.1773 (0.623)	−0.4587 (0.156)	0.1773 (0.622)	0.1773 (0.615)	1.9701 *** (0.000)	−0.8682 *** (0.000)		0.1773 (0.615)
Prob > F	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000		0.0000
country						Yes
year					Yes
F-test	F(5, 144) = 35.94 Prob > F = 0.0000
Hausman test		Chi2(5) = 82.72 Prob > Chi2 = 0.0000
Wooldridge test for autocorrelation in panel data		F(1, 5) = 34.436 Prob > F = 0.0020
Modified Wald test for groupwise heteroskedasticity in fixed effect regression model		Chi2 (6) = 28.05 Prob > Chi2 = 0.0001

Note: ***, **, and * are significance levels for 1%, 5% and 10% respectively. Source: Authors’ analysis.

presents the regression results according to Equation (1), the F-test shows that FEM is better than OLS, and the Hausman test shows that FEM is better than REM, so FEM gives the best choice.

Table 7. Estimation results (Equation (2)).

Variable	OLS	FEM	REM	FGLS	FE Year	FE Country	DOLS	IV-GMM
AIR × COVID	−0.0291 ** (0.023)	−0.0616 *** (0.000)	−0.0291 ** (0.022)	−0.0291 ** (0.019)	−0.1988 *** (0.000)	−0.0616 *** (0.000)	0.5885 *** (0.000)	−0.0291 ** (0.019)
TOURISM	0.4079 *** (0.000)	0.8143 *** (0.000)	0.4079 *** (0.000)	0.4079 *** (0.000)	0.1019 * (0.049)	0.8143 *** (0.000)	−0.1348 (0.398)	0.4079 *** (0.000)
TRADE	0.0001 (0.702)	−0.0013 *** (0.000)	0.0001 (0.702)	0.0001 (0.696)	−0.0001 (0.937)	−0.0013 *** (0.000)	0.0014 ** (0.043)	0.0001 (0.696)
FDI	0.0316 *** (0.000)	0.0062 ** (0.025)	0.0316 *** (0.000)	0.0316 *** (0.000)	0.0001 (0.981)	0.0062 ** (0.023)	0.0254 *** (0.000)	0.0316 *** (0.000)
REN	−0.0175 *** (0.000)	−0.0119 *** (0.000)	−0.0175 *** (0.000)	−0.0175 *** (0.000)	−0.0094 *** (0.000)	−0.0119 *** (0.000)	−0.0125 * (0.010)	−0.0175 *** (0.000)
_cons	1.1901 * (0.010)	−1.1470 *** (0.002)	1.1901 *** (0.009)	1.1901 *** (0.007)	1.5734 *** (0.001)	−1.3461 *** (0.000)		1.1901 *** (0.007)
Prob > F	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000		0.0000
country						Yes
year					Yes
F-test	F(5, 144) = 75.82 Prob > F = 0.0000
Hausman test		Chi2(5) = 82.72 = 202.22 Prob > chi2 = 0.0000
Wooldridge test for autocorrelation in panel data		F(1, 5) = 97.252 Prob > F = 0.0002
Modified Wald test for groupwise heteroskedasticity in fixed effect regression model		chi2 (6) = 48.12 Prob > chi2 = 0.0000

Note: ***, **, and * are significance levels for 1%, 5% and 10% respectively. Source: Authors’ analysis.

presents the regression results according to Equation (2), the F-test shows that FEM is better than OLS, and the Hausman test shows that FEM is better than REM, so FEM gives the best choice.

Table 8. Estimation results (Equation (3)).

Variable	OLS	FEM	REM	FGLS	FE Year	FE Country	DOLS	IV-GMM
AIR	0.5472 *** (0.000)	0.6932 *** (0.000)	0.5472 *** (0.000)	0.5472 *** (0.000)	0.1704 *** (0.000)	0.6932 *** (0.000)	0.5689 *** (0.000)	0.5472 *** (0.000)
TOURISM*COVID	−0.0345 *** (0.000)	−0.0422 *** (0.000)	−0.0345 *** (0.000)	−0.0345 *** (0.000)	0.0782 (0.108)	−0.0422 *** (0.000)	−0.1021 *** (0.000)	−0.0345 *** (0.000)
TRADE	0.0017 *** (0.000)	−0.0002 (0.442)	0.0017 *** (0.000)	0.0017 *** (0.000)	0.0001 (0.887)	−0.0002 (0.441)	0.0013 *** (0.005)	0.0017 *** (0.000)
FDI	0.0202 *** (0.000)	0.0029 (0.220)	0.0202 *** (0.000)	0.0202 *** (0.000)	−0.0001 (0.926)	0.0029 (0.218)	0.0269 *** (0.000)	0.0202 *** (0.000)
REN	−0.0133 *** (0.000)	−0.0032 * (0.055)	−0.0133 *** (0.000)	−0.0133 *** (0.000)	−0.0096 *** (0.000)	−0.0032 * (0.053)	−0.0121 *** (0.000)	−0.0133 *** (0.000)
_cons	−0.2380 (0.467)	−1.0959 *** (0.000)	−0.2380 (0.466)	−0.2380 (0.457)	1.9281 *** (0.000)	−1.5470 *** (0.000)		−0.2380 (0.457)
Prob > F	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000		0.0000
country						Yes
year					Yes
F-test	F(5, 144) = 52.11 Prob > F = 0.0000
Hausman test		Chi2(5) = 178.46 Prob > chi2 = 0.0000
Wooldridge test for autocorrelation in panel data		F(1, 5) = 17.883 Prob > F = 0.0083
Modified Wald test for groupwise heteroskedasticity in fixed effect regression model		chi2 (6) = 10.27 Prob > chi2 = 0.1138

Note: ***, and * are significance levels for 1%, and 10% respectively. Source: Authors’ analysis.

presents the regression results according to Equation (3), the F-test shows that FEM is better than OLS, and the Hausman test shows that FEM is better than REM, so FEM gives the best choice.

For the AIR variable, Table 6 and Table 8 all showed that the regression coefficient of AIR is positive and statistically significant. For the TOURISM variable, Table 6 showed that the regression coefficient of TOURISM is negative and statistically significant, but it is positive and statistically significant in Table 7, so it is clear that the result of TOURISM on growth is not stable.

For the interaction effect of the pandemic, it shows that the regression coefficient of this effect on AIR is negative but this effect changes to positive in the long-run (see Table 7). However, the interaction effect on TOURISM is negative in both the short and long-run (see Table 8). The regression results showed that the regression coefficients of TRADE and FDI are positive and statistically significant (see Table 6, Table 7 and Table 8) while the regression coefficient of REN is negative and statistically significant, and this result does not show a positive impact of REN on growth (see Table 6, Table 7 and Table 8).

Regression results in Table 6, Table 7 and Table 8 show that: the variable AIR has a positive and statistically significant regression coefficient, which reflects the positive impact of aviation development on economic growth. Therefore, we reject the null Hypothesis H1 and accept the alternative Hypothesis H1. This means that the development of the aviation industry can bring positive benefits to the economy. This implies that the socio-economic development requires countries to be able to meet travel and freight needs, while the aviation industry is capable of increasing connectivity and mobility, reducing connection times and thus positively benefiting economies in Southeast Asia. This research result is supported by Q. H. Nguyen (2023) who argued that there is a long-term causal relationship between air transport and growth, which suggests that countries should develop the aviation industry in order to create momentum for air travel and eventually economic growth in the long-run and increase the national interest. Further, Law et al. (2022) once affirmed that improving the transport system is a key factor for economic growth. Additionally, Ali et al. (2023) also affirmed that investing in the aviation industry could support benefits to the economy in both the short and long term. It has greatly contributed to the benefits of a country that has ability to promote tourism industry and the economy. However, the aviation industry is negatively affected by the impact of the pandemic and this significantly affects the economic growth of Southeast Asian countries. This evidence once again confirms that countries’ travel restriction policies during the pandemic have negatively affected the aviation industry and therefore negatively affected economic growth. Furthermore, Southeast Asian countries have developed tourism and aviation industries and maintain high levels of growth every year. Therefore, countries implementing policies to open the aviation industry and loosen visas to promote economic development (ADB 2023), are partly confirming the correctness of Southeast Asian countries implementing aviation policies like today.

The study also suggests that the regression coefficient of TOURISM has a negative sign and is statistically significant (according to Table 6), but this result has a positive sign and is statistically significant (see Table 7), showing that there cannot be a definite relationship between tourism and economic growth. In addition, Table 7 also shows that the regression coefficient of TOURISM when regressed against DOLS has a negative sign and is not statistically significant, confirming that there is no impact of tourism on economic growth. Therefore, we fail to reject the null Hypothesis H2. The results of this study imply that Southeast Asian countries have different tourism infrastructure and tourism development conditions.Thailand and Singapore have the ability to attract a high number of tourists, but Indonesia and the Philippines tourism’s contribution to the economy is still limited. Therefore, there are differences in each country’s tourism and its contribution to economic growth. Furthermore, there is not enough evidence to confirm the impact of tourism on economic growth. The results of the study are also supported by the findings of Dhungel (2015) in the case of Nepal which revealed that the tourism industry does not contribute much to the Nepali economy in the short term. This result indicates that the tourism industry is developing in Nepal and this industry has not yet made a major contribution to the country’s economy, so the impact of tourism development on growth has not been confirmed.

The research results showed that the regression coefficient of the interaction effect between aviation industry and the pandemic is either positive or negative and statistically significant. Specifically, the pandemic has a negative effect on the aviation industry’s contribution in the short term, but this negative effect is unlikely to last in the long term. Therefore, we accept hypothesis H3 and conclude that the pandemic influences on the relationship between air transport, tourism and economic growth in selected Southeast Asian countries. In fact, the results showed that the long-term recovery of the aviation industry has the potential to stimulate economic growth again. Research results also show that the tourism industry is more severely affected by the impact of the pandemic and needs a longer period of time to recover and its contribution to economic development. In fact, the tourism and aviation sector are closely related and together drive economic growth. The results of this study confirm the correct policy of Southeast Asian countries in quickly reconnecting flights after the pandemic to restore tourism industry and economic development. Portella-Carbó et al. (2023) explained that countries should have appropriate macro-tourism policies and reallocate tourism in the direction of increasing productivity so that it can generate greater benefits in economic growth. Another possibility, Kumar and Patel (2023) argued that tourism development needs to ensure the stability of the number of passengers in the airline industry in order to generate economic growth in the long-run because economic growth is relatively sensitive to fluctuations in the tourism and aviation industries, which also explains the case of the pandemic years, the strong effects on the tourism and aviation industries that have made economic growth may fall into recession.

Research results in Table 6, Table 7 and Table 8 show that the regression coefficient of TRADE is positive and statistically significant, meaning that there is a positive impact on international trade and economic growth. Simultaneously, the regression coefficient of FDI is positive and statistically significant, confirming the positive impact of FDI attraction and economic growth. In fact, FDI and international trade are likely to go hand in hand. FDI enterprises coming to Southeast Asia in addition to the goal of exploiting the domestic market, also want to promote foreign trade. Countries with favorable trade policies often have an advantage in attracting international capital flows. Therefore, these policies have a very positive impact on economic growth. This research result is consistent with the research of Sijabat (2023) and suggests that attracting foreign direct investment is an additional source of economic development of the country. In addition, FDI has the ability to spread productivity, innovation and technological transfer to the recipient country while promoting international trade and increasing benefits to the economy.

Research results in Table 6, Table 7 and Table 8 suggest that renewable energy consumption has not yet brought about a positive impact on the growth of Southeast Asian countries. This can explain why renewable energy sources have not played a key role in the economic development of Southeast Asian countries in recent times. Renewable energy has never appeared in Singapore while other countries such as Thailand, Vietnam or Malaysia are still dependent on fossil energy sources that are cheaper to produce and relatively available. Therefore, countries in the short term continue to use fossil energy to meet the increasing energy demand while renewable energy sources are difficult to meet the current energy demand, especially difference in the short term. Therefore, it can be seen that the contribution of renewable energy sources to the economic growth of Southeast Asian countries is not really much, and increasing the use of renewable energy is still a desire that any country aims to achieve long-term sustainable development. The results of this study are supported by Zangoei et al. (2021) arguing that the economy has a low level of development, fossil energy consumption can significantly reduce production costs and thus boost high economic benefits, especially in the short term. This is also consistent with the case of many Southeast Asian countries entering a period of high growth while fossil energy sources are available and cheaper than renewable energy sources.

6. Conclusions

The aviation industry was born later but has made practical contributions to economic growth, especially the advantages of time travel to increase connectivity in economic performance. Under normal economic conditions, the development of the aviation industry is also a driving force that promotes the development of the tourism industry and therefore has an impact on the economy in general. In practice, the pandemic has affected the aviation industry because countries have implemented travel restrictions and social distancing policies to limit the spread of the disease, thus affecting the airline industry, and the development of the tourism industry. Therefore, the influence of aviation industry development on economic growth is quite different under normal economic conditions and under epidemic conditions. This research studied the influence of aviation on economic growth in selected Southeast Asian countries includingThailand, Philippines, Vietnam, Indonesia, Malaysia, and Singapore from 1997 to 2021; using quantitative regression analysis traditional methods such as ordinary least squares, fixed effects and random effects. The study also implements F-test and Hausman tests, and at the same time, the testis based on heteroskedasticity and autocorrelation. In the case of defects, the FGLS regression method should be performed, and at the same time re-test the robustness of the research results according to DOLS. The research results confirm that the aviation industry has a positive impact on economic growth, especially the positive impact increases in normal economic conditions and decreases in the COVID-19 pandemic. The study confirms the positive impact of foreign direct investment and foreign trade on the economic growth of Southeast Asian countries, thereby reflecting the benefits of attracting FDI and expanding foreign trade in Southeast Asian countries today. However, there has been a negative impact of renewable energy consumption on economic growth, partly showing the important role of fossil energy consumption in the socio-economic development of countries.

The study has some policy implications for Southeast Asian countries in particular and other countries in general. Firstly, countries in Southeast Asia need to take appropriate measures to minimize the risk of the impact of the pandemic, or shocks affecting the aviation, tourism sectors, or economic growth. In the future, other similar shocks are entirely possible and will affect the aviation industry, tourism and the economy. Measures to reduce risks come from the Government’s adaptability in operations and businesses using risk prevention measures and adapting to shocks. Secondly, countries in Southeast Asia should continue to develop the infrastructure system for the aviation, and tourism industries in order to boost the engine of the economic growth. Indeed, countries such as Thailand, Malaysia, Singapore are considered as top tourism destinations while countries such as Vietnam, Indonesia, Philippines, and Laos have great potential to develop the aviation and tourism industries to increase their contribution to socio-economic development. Thirdly, countries need to have solutions to adapt to shocks that affect the aviation industry, the tourism industry such as pandemics, natural disasters, or unpredictable shocks, and highly adaptive to negative effects on the economy. Fourthly, countries should continue to create a favorable business environment to attract international capital flows and implement economic reforms, and trade liberalization to create advantages in economic development. Finally, governments should make policies geared towards developing renewable energy sources to meet the increasing energy demand in the economy, gradually reducing dependence on fossil energy sources to create a driving force for economic development in the long term.

The study has several limitations. Firstly, the study confirms the positive relationship of aviation development to economic growth; however, this study is only conducted in Southeast Asia. Secondly, the study evaluates the effects of the tourism and aviation industries on economic growth and the interaction of the pandemic in this relationship. However, other factors that also impact economic growth have not been considered in this study. Thirdly, there are many shocks that affect the aviation industry, tourism and economic growth, of which the COVID-19 pandemic is just one of them. Fourthly, the study has not estimated the impact of tourism and aviation development on climate change. Indeed, there exists the positive impact of the tourism and aviation industry on economic growth, however, there can also be a negative impact on environmental quality because aviation and tourism have the potential to emit carbon, or waste released into the environment and negatively impact climate change and sustainable development.

Future research may be expanded as follows: Firstly, future research should be expanded on a larger sample size to better clarify the impact of the pandemic on the relationship between tourism, aviation and growth. Secondly, future research can add new variables that also affect economic growth such as institutional quality, technological enhancement, or human capital. Thirdly, future research can be evaluated by additional shocks and their impact on aviation, tourism, and economic growth. Fourthly, future research evaluates the impact of aviation and tourism on climate change.