Does Tourism Affect Sustainable Development in MINT Countries?

Abstract

Following the failure to achieve Millennium Development Goals by most countries in the world, Sustainable Development Goals are now at the center of developmental issues. Consequently, this study aims to examine if tourism can be an ally to sustainable development in MINT (Mexico, Indonesia, Nigeria, Turkey) countries between 1995 and 2018. The study uses adjusted net saving and international tourism receipts in these countries as measures of sustainable development and tourism, respectively. In achieving its objectives, the study employs the augmented mean group (AMG) estimation technique to estimate the long-run parameters. Besides, mean group (MG) and common correlated effects MG techniques are employed to check the robustness of the estimates obtained via the AMG approach. The results from the three estimators show that tourism is indeed an ally to sustainable development in MINT countries since there is a significant positive relationship between tourism and sustainable development. In other words, tourism can put the MINT countries on the path to sustainable development. This implies that any policy that will enhance the performance of the tourism industry will ensure sustainable development in MINT countries. Thus, the governments of MINT countries should focus basically on achieving sustainable tourism development, as this will translate to sustainable development in their countries.

Keywords

Tourism sustainable development MINT countries heterogeneous panel approach

Introduction

The impact of tourism on economies and countries as a whole transcends economic influences and increased national income. Tourism contributes to economic growth, stimulates the process of development, furthers multicultural peace and understanding, enhances diffusion of cultures, and promotes the technological spread and managerial know-how (Phiri, 2016). Zortuk (2009) predicts international tourist movements to reach 1,602 million at the end of 2020, with receipts soaring to a mammoth US$200 billion. These gains from tourism have led to the inclusion of the industry in the national development plans of countries across the world. Tourism in more developed countries is viewed as a means to maintain their high-income and highly-developed status, while in less developed countries, tourism represents a beacon to guide the way out of the depths of poverty.

Tourism can be referred to as an industry with extensive tentacles in several other industries and sectors. In itself, it is a highly profitable, ever-increasing, and all-compassing sector that touches every sphere of life. With the well-detailed economic benefits and rising number of worldwide yearly tourists, it is inevitable that a sector of such diverse magnitude and significant influence across continents would exhibit no imprints on the environment.

With this in mind, the United Nations (UN) created the Sustainable Development Goals in 2015, comprising 17 interrelated goals to ensure a more sustainable future achieved by 2030. Incidentally, promoting beneficial and sustainable tourism features as a target in goals 8 and 11, which aim to ensure affordable and clean energy and sustainable cities and communities. The concept of sustainability has been deeply touted in economic discourse as a means to overcome the conundrum of rapidly depleting non-renewable resources and the relatively slow development of renewable resources. Nonetheless, though the concept has its roots embedded in environmentalism, which refers to ethical concerns regarding the usage of the environment and the subsequent aftershocks on the ecology, it has spread to other fields, which is not surprising, as virtually all of our production activities are carried out with effects on the environment. According to Sharpley (2000), tourism, beyond serving a higher function of contributing to economic growth, can be a tool to spur sustainable economic development.

In essence, the concept of sustainability can be broken down into two basic aims—preservation and continuity. As a result, sustainable development is regarded as development carried out in a manner that preserves the environment and ensures its continuity for future generations. The broad meaning of development, which includes the social and political freedoms, quality of life, and better standards of living means sustainable development has spread its tentacles beyond environmental concerns. As such, the concept of sustainable tourism development is broadly regarded as tourism carried out in a manner that preserves not only the environment and ecological sphere but also the cultural identity and socio-economic needs of the recipient tourist center. In line with this view, Liu (2003) and Eusebio et al. (2014) consider tourism to contribute to the various yardsticks and development indices that constitute sustainable development such as quality of life, employment of locals, long-term linkages between industries, and protection of natural heritage for future generations. The question of whether tourism can be sustainable and just how sustainable it can be, has been debated by some researchers. According to Liu (2003), sustainable tourism is twofold, with the first branch pointing at the sustainable use of resources and environment in tourism activities, and the larger picture, which is sustainable growth of tourism to economic growth.

To ensure sustainable development aims and targets are achieved in a country, all sectors should be run with sustainability in mind. Subsequently, a concerted effort must be made with the expected result being that sustainable development is the superset of subsets of individual sustainable development activities in each sector of the economy (Telfer & Sharpley, 2015).

While the effect of tourism on economic growth is well detailed in the literature, studies examining the relationship between tourism and sustainable development are somewhat scanty. Though tourism brings with it a wide range of unsavory externalities, such as environmental hazards, congestion, litter, increased carbon emissions, and wildlife depletions, it comes bearing benefits to the environment too, including the sustainability of an economy at large, in form of better leisure facilities and infrastructural development. This research aims to examine if tourism can be an ally to sustainable development aims in MINT countries. MINT countries consist of Mexico, Indonesia, Nigeria, and Turkey; and are so-grouped due to their similar economic characteristics, growth trajectories, natural and human resource abundance, and demographic commonalities (Osinubi, 2020a, 2020b). Beyond focusing on trying to find a theoretical backing and link between tourism and sustainable development and the dynamics between sustainable tourism development and sustainability in specific tourist towns, this research finds peculiarity in the fact that it has a group of countries under consideration. Also, this study fills a methodological gap by applying an augmented pooled mean group (PMG) estimation technique in that the method takes care of cross-sectional dependencies that may arise among the cross-sections.

The rest of this article is organized as follows. The next section focuses on the trend of tourism and sustainable development in MINT countries. Subsequent sections provide a review of literature pertinent to this work, followed by the methodology of the study, the results and concluding remarks, respectively.

Overview of Tourism and Sustainable Development in MINT Countries

This section focuses on the trend of tourism and sustainable development in MINT countries.

Tourism in MINT Countries

These countries are not exempted in terms of tourism performance as measured by international tourism receipts (ITR). All the MINT countries experience an increase in ITR throughout the study period, except for Nigeria where ITR fluctuates as shown in Table 1. Then, what have these countries done differently?

According to the World Tourism Organization (2018), Mexico is one of the most visited countries in the world and the second-most visited country in the Americas, after the United States. This has earned her a second position in the ITR. In Mexico, tourism plays a major role in the economic sector by providing jobs, contributing positively to the balance of payment, and hence, spurring economic growth (Organization for Economic Co-Operation and Development [OECD], 2017). To achieve this, the Mexican government places tourism high on the policy agenda as it is recognized to be one of six priority economic sectors in the National Development Plan 2013–2018. The country has a remarkable set of well-articulated tourism plans and programs, according to the OECD (2017), which aim to spur investment and economic growth, foster balanced regional development, and stimulate more productive, inclusive, and sustainable growth. The implication is that tourism has an important role to play in ensuring sustainable development in Mexico. Mexico has a well-established tourism development model that has drawn both private and public investment to successfully exploit the country’s natural environment and coastline endowment, in addition to its impressive tourism performance (OECD, 2017). The tourism industry’s success in Mexico is unsurprising, given that the country is ranked 19th out of 140 countries, with a score of 4.7 on a scale of 7.0 (World Economic Forum [WEF], 2019).

It is gathered that tourism is a significant sector in Indonesia as it contributes to the economic growth by increasing the country’s foreign exchange revenues (Cholik, 2017). This is reflected in the Indonesian tourism receipts since it increases over the period. This could be as a result of numerous factors put in place by the Indonesian Government. For instance: (a) the setting of 10 tourism destinations for a development priority since 2016 (Cholik, 2017); (b) the government since 2016 has been investing more in the tourism industry by attracting more foreign investors; and (c) WEF (2019) ranks the country 40th out of 140 countries in the world. The country scores 4.3 which makes it second in the MINT countries in reference to tourism performance.

Turkey’s tourism industry has been rapidly expanding, as shown by its tourism receipts in Table 1. Since 2004, the Turkish government has backed a slew of local and foreign ventures aimed at luring visitors from all over the world. However, the country suffered significant declines in both tourist numbers and revenue, dubbed a “year of catastrophic losses” by Turkish tourism companies, who said they “cannot recall a worse time in the industry.” This is demonstrated by the tourism receipts for the sub-period 2016–2018, as shown in Table 1. In 2017, the number of international visitors began to recover, with 37.9 million visitors being recorded. The recovery was aided in part by extensive protection and publicity campaigns. As a result, the country is one of the top ten tourist destinations in the world (World Tourism Organization, 2018). In addition, WEF (2019) gives the nation a score of 4.2, placing it 43rd out of 140 countries.

Nigeria is an outlier among the MINT countries as the tourism receipts is unstable and less than that of other countries. As a result, the country scores badly (2.2) in the Travel & Tourism Competitiveness Report ranking 129th out of 140 countries (WEF, 2019). Notwithstanding, there are various tourism potentials in Nigeria which include waterfalls, mountains, hills, springs, lakes, caves, parks, festivals, and carnivals (Amalu et al., 2020; Osinubi & Osinubi, 2020; Osinubi et al., 2021). These potentials have helped in generating revenue for the Nigerian government as shown in Table 1. However, it is surprising to note that irrespective of the prospects of the tourism industry in the country, the industry has continued to struggle to contain and sustain itself without necessarily contributing enough to the economic growth of the country (Ajake et al., 2016). These could be a result of religious indifference, poor administration and planning, corruption, poor infrastructure, terrorism and insecurity, poor funding and investment, increased crime rate, and political unrest (Alola et al., 2019; Amalu et al., 2019; Ayeni, 2013; Olatunji & Falabi, 2014; Takon & Amalu, 2013). If these resources are fully harnessed without these difficulties, then achieving sustainable development will not be impossible.

Sustainable Development in MINT Countries

In this study, we proxy sustainable development by adjusted net saving. World Bank (2012, p.1) defines adjusted net saving as “the true rate of saving in an economy after taking into account investments in human capital, depletion of natural resources and damages caused by pollution.” Informally, adjusted net saving is known as genuine saving and it helps in assessing an economy’s sustainability based on the concept of extended national accounts. Explicitly, adjusted net saving is calculated below following the World Bank (2012):

Table 1.

Tourism Receipts in Mexico, Indonesia, Nigeria, and Turkey (1995–2018).

Country	Rank*	Sub-periods
Country	Rank*	1995–1998	1999–2002	2003–2006	2007–2010	2011–2014	2015–2018
Mexico	19th	7.7E + 09	8.98E + 09	1.2E + 10	1.38E + 10	1.4E + 10	2.15E + 10
Indonesia	40th	5.58E + 09	5.13E + 09	4.93E + 09	6.93E + 09	1.01E + 10	1.4E + 10
Nigeria	129th	67250000	1.8E + 08	1.14E + 08	7.08E + 08	6.4E + 08	1.54E + 09
Turkey	43rd	6.2E + 09	8.7E + 09	1.73E + 10	2.5E + 10	3.43E + 10	3.3E + 10

Source: Authors’ compilation.

Note: * indicates the Travel & Tourism Competitiveness Index in 2019.

“Gross national saving – consumption of fixed capital = net national saving”

“Net national saving + education expenditure – energy depletion – mineral depletion – net forest depletion – damage from carbon dioxide emissions – damage from particulate emissions = adjusted net saving”

Figure 1 reveals the adjusted net savings (ADNS) in each of the MINT countries. According to Koirala and Pradhan (2019, p. 41), a positive trend of ADNS implies expansion of stocks of physical and human capital goods as opposed to the depletion of national resources, while a negative trend implies unsustainable paths in that the economies are being fueled by the depletion of national resources without converting that natural capital into other forms of capital. As depicted in the figures, Mexico and Nigeria exhibit a negative trend, while ADNS reveals an increasing trend for Indonesia and Turkey. This suggests that the formal countries are not on the path to sustainable development, whereas the latter countries are on the path to sustainable development.

Figure 1.

Source: Authors’ compilations from World Development Indicators.

Literature Review

Concept of Sustainable Development

Sustainable development has been the subject of much debate among researchers, with several papers taking diverse positions on the concept. Though the general definition is not disputed, many researchers have been left scratching their heads regarding just how it is carried out and what the defining benchmarks are. To some, sustainable development is an oxymoron, as both sustainability and development work in opposite directions. Sustainability denotes permanence while development points to a change from the norm. This disparity makes the concepts mutually exclusive and causing a trade-off, according to Wall (2007). Sharpley (2000) shares this opinion and is of the same view that the compatibility of economic development and resource conservation is virtually impossible since the latter cannot be attained if the former is to be achieved. This raging debate makes it difficult to accurately lay a glove on a precise description of sustainable development. Telfer and Sharpley (2015) submit that the conundrum behind defining sustainable development makes it even more difficult in determining its indices and measuring it. Amidst the differing views, Murphy and Price (2005) put forward that sustainable development comprises a wide spectrum of conservation techniques reaching far beyond environmental concerns, citing examples of redistribution of economic resources, economic viability, and population control.

However, despite the various doubts as to whether sustainable development can be successfully carried out, several researchers have been able to examine its multi-faceted aspects and surrounding principles and aims. Though environmentalism was a key precursor in the birth of sustainable development, it has been opined that sustainable development can be applied to economic, social, and cultural aspects of life. Murphy and Price (2005) and Telfer and Sharpley (2015) posit that the concept revolves around a set of principles crafted toward a definite coexistence of economic, social, and cultural objectives. However, they agree that a discrepancy exists between the theory and practice of sustainable development. Nevertheless, Telfer and Sharpley (2015) go a step further by pointing out that sustainable development should be guided by 3 principles—a holistic perspective that views the economy and sectors as a single entity, a futuristic perspective that ensures development is carried out with an eye on the future and equity for all in the present. This standpoint is echoed by Creaco and Querini (2003) that development rests on a tripod of economic, social, and environmental concerns, and sustainable development is attained when the core characteristics of sustainability are met, which include equity, inclusion, social cohesion, biodiversity, and efficiency.

Tourism and Sustainable Development

Given its varied and numerous economic impacts, the tourism industry has been advised to contribute to the achievement of sustainable development. On one hand, several studies (Dogru & Sirakaya-Turk, 2017; Shahzad et al., 2017; Tugcu, 2014) establish that tourism promotes economic growth by creating jobs, growing foreign reserves, generating government revenue, and promoting international cooperation. On the other hand, recent studies such as Ristića et al. (2019) have concluded that tourism’s contribution to an economy extends beyond the limits of economic growth by contributing to sustainable growth and/or development. This indicates that tourism has a critical role to play in achieving sustainable development.

In the quest to attain sustainable development, all sectors have to be geared toward the goal in mind. That said, tourism is not left out of the discourse, and even more so, given the ever-rising number of yearly tourists and income the industry pulls in annually. To achieve sustainable development in the tourism industry, tourism has to be carried out with development objectives in mind and run with development principles (Telfer & Sharpley, 2015). This has given rise to what is termed “sustainable tourism development.” Often intertwined and mentioned in the same breath as sustainable tourism, these terms have also been hotly disputed when related to sustainable development. Creaco and Querini (2003) posit that these differing views are due to diverse disciplinary views, various political interests, and value systems.

Several publications focus their studies on the impact of tourism on sustainable development in specific provinces, tourist destination cities, and rural areas within specific countries. Ristića et al. (2019) use a strengths, weaknesses, opportunities, and threats (SWOT) analysis to investigate the effect of tourism on the sustainable development of rural settlements in Serbia. The authors conclude that tourism leads to the increased sustainable development of rural settlements. Similarly, Ibănescu et al. (2018) examined the impact of tourism on the sustainable development of Romanian rural areas using the Mann-Whitney U-test. The results from the test show that tourism and sustainable development in Romanian rural areas go hand in hand.

Based on partial use of a modified sustainable development indicator developed by the Voluntary Initiative for Sustainability in Tourism, Konakoglu et al. (2019) compared sustainable development in the cities of Amasya (Turkey) and Zakopane (Poland). According to the findings, sustainable tourism growth is more consistent in Amasya than in Zakopane, as evidenced by the significantly lower negative environmental effect in Amasya than in Zakopane. Ekinci (2014), on the other hand, uses the Cittaslow philosophy to assess Turkey’s long-term tourism growth. The Cittaslow theory is focused on its parameters for sustainable tourism development, which are the socio-cultural, historical, natural, and touristic structure of the destinations. Nonetheless, the study found that majority of the cities in Turkey’s western area met these requirements, encouraging other cities to follow suit in order to correct the country’s regional imbalance and foster economic sustainability.

SWOT analysis is also used by Ristića et al. (2019), and Ates and Ates (2019) to assess the potential for geotourism in Turkey’s Marcik Valley. Based on the findings, the presence of unique geological features in the area fuels the region’s potential for ecotourism, which can provide many economic benefits to the province, including sustainable jobs, improved people’s quality of life, and provision for the next generation. Similarly, Kisi (2019) uses the analytic hierarchy process (AHP) in SWOT analysis (AWOT) framework (a combination of AHP and SWOT) to study a strategic approach to sustainable tourism development in Zonguldak (Turkey). When the area’s strengths such as natural beaches, caves, and fauna, are exploited, and its shortcomings such as poor infrastructure and the like are addressed, the result shows that the area has the potential to achieve sustainable tourism development and attract visitors.

Gronau and Kaufman (2009), in their study of a resort town in Cyprus, seek to inquire whether tourism can support sustainable development rather than the sustainability of tourism itself and discovered that tourism acts both in opposition to and in line with sustainable development in three ways—inherent inequality between the coastal tourism regions and rural areas of the hinterland which is exacerbated by the increasing privatization of the tourism industry due to entrepreneurship and a disparity between overall sustainability goals and individual tourist attitudes. However, tourism contributes to sustainable development by the provision of financial and economic capital and social capital. Social capital involves creating identities, tangible community resources such as buildings and environmental capital (resource sustainability). Eusebio et al. (2014) submit glowing reviews regarding the discourse, believing the tourism industry to spur sustainable development through increased employment, creation of long-term linkages among industries, preservation of natural heritage for future generations, and improved quality of life. They consider tourism-sustainable development nexus to be broader than economic and environmental dimensions, adding both cultural and social dimensions.

According to Liu (2003), sustainable tourism and sustainable development are interwoven, as changed tourism processes result in improved economic and development conditions. Liu (2003) further submits that sustainable tourism is more fueled and subject to demand management, as tourism demand is more subject to shocks over tourism supply.

A common theme in the sustainable tourism discourse has been the focus on the recipient tourist destination, with less attention paid to the incoming tourist population. Creaco and Querini (2003) take a wider view of sustainable tourism, splitting it into ecological and anthropological aspects. Apart from the economic and environmental benefits tourism should confer on the tourist center, it should be carried out with not only benefits for the recipient tourist center but also meet the needs of the tourist population. Sustainable tourism and sustainable development are guided by the same principles, as sustainable tourism is a subset of sustainable development.

Research Gap

This research finds uniqueness in not only the scope of the study (MINT countries) but in the aspect of sustainable development under consideration.

MINT countries (Mexico, Indonesia, Nigeria, and Turkey) are emerging economies with significant tourism development potentials due to the abundance of tourist attractions in the form of historical sites, wildlife, cultural attractions, and landscapes. Tourism growth is receiving more attention in these countries, owing to the realization that fully utilizing the potentials that have yet to be fully exploited will possibly propel these countries to greater economic heights. On the other hand, if not properly managed, this growth may have negative environmental consequences and result in sustainability issues, both in terms of environmental and tourism sustainability. Beyond trying to discover and proffer a theoretical backing for the relationship between tourism and sustainable development, this article seeks to detail the impact of tourism on sustainable development in MINT countries and how sustainable tourism development can enhance sustainable development in these countries.

The relationship of tourism with economic, social, and environmental branches of sustainable development has been roundly discussed, although the vast majority of the papers are descriptive in nature, lacking any empirical test. Furthermore, following the work of Koirala and Pradhan (2019) on determinants of sustainable development, the use of ADNS as a measure of sustainable development is adopted in this research. It is widely accepted as an inclusive metric of sustainability as it takes account of national savings, educational expenditure, and ecological damage and depletions. Also, according to the World Bank (2012), adjusted net saving is seen as an essential aspect of development.

Methodology

Theoretical Framework and Model Specification

The theoretical foundation underlying this study is found in the work of Koirala and Pradhan (2019) which is based on the extended version of the standard economic growth model. The growth model is modified in such a way that the level of savings includes the depletion of natural resources that shows how natural capital reduction would affect the level of saving for future generations as shown in equation (1). This model, therefore, can be viewed as a model for sustainable development. From equation (1), ADNS is shown as a function of several variables (see Koirala & Pradhan, 2019).

ADNS = f (X_{s})

(1)

ADNS stands for adjusted net saving (a measure of sustainable development; see Van Zeijl-Rozema et al., 2008; Koirala & Pradhan, 2019; Güney, 2017a, 2017b, 2019, 2021; Güney & Kantar, 2020) and Xs are the explanatory variables. For the purpose of this study, we include tourism as a determinant of sustainable development since tourism is a potent tool for sustainable development through lowering inequality within and among economies (Balli et al., 2015; Ibănescu et al., 2018; Ohlan, 2017). Gross domestic product (GDP) per capita, according to Koraila and Pradhan (2019) is an important driver of sustainable development. Similarly, the inclusion of inflation stems from the reality that inflation is inextricably related to national savings, as it can either increase or decrease the saving level (Koirala & Pradhan, 2019). In its panel version, equation (1) can be re-specified as follows:

{ADNS}_{i t} = γ_{i} + α_{1} {ITR}_{i t} + α_{2} {GDP}_{i t} + α_{3} {INF}_{i t} + μ_{i t}

(2)

where i = 1, 2, …, N and t = 1, 2, …, T. $α_{1}, α_{2}, a n d α_{3}$ are country-specific slope parameters. Individual parameters that are time-variant are denoted by γi. International tourism receipts (a measure of tourism), GDP per capita, and inflation rate are all abbreviated as ITR, GDP, and IN, respectively. $μ_{i t}$ is the error term, which is independently and identically distributed.

Except for ADNS and INF, all variables are transformed into their natural logarithms to reduce non-normality. This is stated in equation (3). The ADNS is in its original metric because it contains some negative values for which logarithmic values cannot be established, while inflation is already in rate.

A D N S_{i t} = γ_{i} + α_{1} L I T R_{i t} + α_{2} L G D P_{i t} + α_{3} I N F_{i t} + μ_{i t}

(3)

Method of Analysis

Cross-sectional Dependence Test

As a result of substantial inter-economic interconnections among variables or nations, it is required to test for cross-sectional dependence when dealing with cross-country time-series data (panel data; Tachie et al., 2020). The issue of cross-sectional dependence must be addressed in order to avoid erroneous and incorrect estimates. To do this, the study employs Pesaran’s (2004) cross-sectional dependence test which is a better version of Breusch and Pagan’s Lagrangian Multiplier test. The CD test according to Pesaran (2004) is as follows:

C D = \sqrt{\frac{2 T}{N (N - 1)} \sum_{i = 1}^{N - 1} \sum_{j = i + 1}^{N} \frac{(T - p) {\overset{⌢}{θ}}_{i j}^{2} - E [(T - p) {\overset{⌢}{θ}}_{i j}^{2}]}{var [(T - p) {\overset{⌢}{θ}}_{i j}^{2}]}}

(4)

where N is the size of the sample, p indicates the time period, ${\overset{⌢}{θ}}_{i j}^{2}$ is the pair-wise correlation coefficient obtained from the residual terms of the ordinary least squares (OLS) estimation for each cross-section dimension, i.

From equation (4), the null hypothesis of no cross-sectional dependency and the alternative hypothesis of residual cross-sectional dependence can be expressed as:

H_{0} : θ_{i j} = θ_{j i} = c o r (μ_{i t}, μ_{j t}) = 0 for i \neq j

(5a)

H_{1} : θ_{i j} = θ_{j i} = c o r (μ_{i t}, μ_{j t}) \neq 0 for some i \neq j

(5b)

The existence of residual cross-sectional dependency among the variables of interest suggests that the null hypothesis is rejected.

Slope Homogeneity Test

If the panel data is heterogeneous, it is necessary to determine whether or not slope homogeneity exists. According to Breitung (2005), the assumption of slope homogeneity in the face of heterogeneity might lead to erroneous regression. Hence, the study relies on Pesaran and Yamagata’s (2008) models to accomplish this. Equations (6a–c) describe the models.

\overset{⌢}{Δ} = \sqrt{N} (\frac{N^{- 1} \tilde{R} - k}{\sqrt{2 k}})

(6a)

{\overset{⌢}{Δ}}_{a d j} = \sqrt{N} (\frac{N^{- 1} \tilde{R} - k}{\sqrt{\frac{2 k (T - k - 1)}{T + 1}}})

(6b)

\tilde{R} = \sum_{i = 1}^{N} {({\overset{⌢}{α}}_{ı} - {\tilde{α}}_{WFE})}^{I} \frac{X_{i}^{l} M_{τ} X_{i}}{{\tilde{δ}}_{i}^{2}} ({\overset{⌢}{α}}_{i} - {\tilde{α}}_{WFE})

(6c)

where $\overset{⌣}{Δ}$ and ${\overset{⌣}{Δ}}_{a d j}$ stand for standardized dispersion and the biased-adjusted statistics. ${\tilde{α}}_{i}$ represents the pooled OLS regression parameters for each individual i for i = 1, 2, …, N. ${\tilde{α}}_{W F E}$ is the weighted fixed effect (WFE) pooled estimator. k, $M_{τ}$ , and $δ_{i}^{2}$ denote the number of independent variables, the estimate of $δ_{i}^{2}$ , and identity matrix, respectively

Panel Unit Root Test

The first-generation panel unit root tests such as Levin-Lin Chu and Im-Pesaran-Shin (IPS) tests are not valid given the condition of cross-sectional dependence (Pesaran, 2007). As a result, the second-generation panel unit root tests that account for cross-sectional dependence becomes valid. The second-generation panel unit root tests developed by Pesaran (2007) include the Cross-sectionally Augmented Dickey-Fuller (CADF) and Cross-sectionally Augmented Im-Pesaran-Shin (CIPS) tests. Equation (7a) displays the statistic of the CADF test as shown below.

Δ y_{i, t} = a_{i} + b_{i} y_{i, t - 1} + c_{i} {\bar{y}}_{t - 1} + d_{i} Δ {\bar{y}}_{i, t} + μ_{i t}

(7a)

where ${\bar{y}}_{t - 1}$ and $Δ {\bar{y}}_{i, t}$ are the cross-sectional averages of lagged values and each series’ first differences, respectively.

{\bar{y}}_{t - 1} = N^{- 1} \sum_{i = 1}^{N} y_{i, t - 1}

(7b)

Δ {\bar{y}}_{i, t} = N^{- 1} \sum_{i = 1}^{N} Δ y_{i, t}

(7c)

The CADF statistics is averaged to get the CIPS statistic, which is described in equation (7d).

C I P S = N^{- 1} \sum_{i = 1}^{N} C A D F_{i}

(7d)

It is worth noting that the CADF statistic is calculated using equation (7a).

Panel Cointegration Test

According to Westerlund (2007), equation (8) is the test-statistic for estimating the existence of a long-run relationship among variables of interest in the presence of cross-sectional dependence. This cointegration test takes an error correction model into account.

Δ y_{i, t} = \partial_{i}^{I} d_{t} + ϑ_{i} (y_{i, t - 1} + ω_{i}^{I} x_{i, t - 1}) + \sum_{j = 1}^{m} ϑ_{i j} Δ y_{i, t - j} + \sum_{j = 0}^{m} ρ_{i j} Δ x_{i, t - j} + μ_{i t}

(8)

where $ϑ_{i}$ is the adjustment term or speed at which the system returns to the equilibrium relationship and dt represents the deterministic component of the model.

There are four test statistics for each cross-section, i, from the Westerlund-ECM panel cointegration approach. The statistics are calculated using least squares estimates of $ϑ_{i}$ with the null hypothesis of no cointegration. The group mean and panel statistics are stated in equations (9a–d).

G_{τ} = \frac{1}{N} \sum_{i = 1}^{N} \frac{ϑ_{i}}{S E ({\hat{ϑ}}_{i})}

(9a)

G_{\propto} = \frac{1}{N} \sum_{i = 1}^{N} \frac{T ϑ_{i}}{ϑ_{1}^{I} (1)}

(9b)

P_{τ} = \frac{{\overset{⌢}{ϑ}}_{i}}{S E ({\overset{⌢}{ϑ}}_{i})}

(9c)

P_{\propto} = T \overset{⌢}{ϑ}

(9d)

The conclusion is that there exists at least one cross-sectional unit of the panel if Gτ and G∝ statistics reject the null hypothesis of no cointegration, while there is evidence of cointegration in the entire panel if Pτ and P∝ statistics reject the null hypothesis.

Panel Long-Run Estimates

In line with Tachie et al. (2020) and Iheonu et al. (2020), the present research uses the augmented mean group (AMG) estimator proposed by Eberhardt and Teal (2010) to achieve the stated objective. This method is preferred to other econometric approaches such as OLS, fixed/random effects (FE/RE), PMG, and generalized method of moments because it accounts for cross-sectional dependency and heterogeneity by adding a common dynamic process in the modelling procedure. The basic econometric model of the study from equation (2) is estimated utilizing the first-difference form coupled with T–1 dummies through the following regression model in the first stage of the AMG technique:

Δ {ADNS}_{i t} = α_{1} Δ {LITR}_{i t} + α_{2} Δ {LGDP}_{i t} + α_{3} Δ {INF}_{i t} + \sum_{t = 2}^{T} h_{t} (Δ D_{t}) + μ_{i t}

(10)

Where ΔDt represents the first difference T–1 time dummies. ht denotes the parameters of the period dummies.

The second stage of the AMG approach involves reparametrizing the estimated ht at time t to ut (that is, ht ≡ ut) and this is expressed further in a common dynamic process in equation (11) as:

Δ {ADNS}_{i t} = α_{1} Δ {LITR}_{i t} + α_{2} Δ {LGDP}_{i t} + α_{3} Δ {INF}_{i t} + u_{t} (d_{t}) + μ_{i t}

(11)

where:

Δ {ADNS}_{i t} - u_{t} (d_{t}) = α_{1} Δ {LITR}_{i t} + α_{2} Δ {LGDP}_{i t} + α_{3} Δ {INF}_{i t} + μ_{i t}

(12)

Equations (10) and (11) imply that the panel regression model with ut is adjusted, then the mean values for the estimated parameters are calculated with respect to country-specific models. Thus, the following relationships in equation (13), the parameter estimates of the explanatory variables based on the AMG estimator αi can be obtained:

{\overset{⌢}{α}}_{1, A M G} = \frac{1}{N} \sum_{i = 1}^{N} {\overset{⌢}{α}}_{1 i}; {\overset{⌢}{α}}_{2 AMG} = \frac{1}{N} \sum_{i = 1}^{N} {\overset{⌢}{α}}_{2 i}; {\overset{⌢}{α}}_{3, AMG} = \frac{1}{N} \sum_{i = 1}^{N} {\overset{⌢}{α}}_{3 i}

(13)

where ${\overset{⌢}{α}}_{1}, {\overset{⌢}{α}}_{2}, and {\overset{⌢}{α}}_{3}$ are the estimates of $α_{1}, α_{2}, and α_{3}$ .

The study, in particular, uses the mean group (MG) and common correlated effects mean group (CCEMG) estimation techniques to test the robustness of the estimates derived using the AMG approach. Pesaran and Smith (1995) developed the MG approach, which works well with potential heterogeneity among slope coefficients but not with cross-sectional independence. Pesaran’s (2006) CCEMG technique, on the other hand, accounts for both cross-sectional dependence and slope heterogeneity.

Data

The data on the variables are sourced from the online edition of the World Bank’s World Development Indicators. Based on the availability of data, the analysis spans the years 1995 to 2018. Variables used include ADNS, excluding particulate emission damage (% of GNI) as a proxy for sustainable development, international tourism, receipts (current US$) as a measure of tourism, gross domestic product (GDP) per capita uconstant 2010 US$), and inflation, consumer prices (annual %). To eliminate non-normality, all variables except ADNS and INF are transformed into their natural logarithm. The countries under consideration include Mexico, Indonesia, Nigeria, and Turkey (MINT countries).

Descriptive Statistics

Descriptive statistics give the summary of the features of data involved using mean, median, standard deviation, skewness, kurtosis, and Jara-Berque tests. Table 2 shows that the average value of the adjusted net saving accounts for 10.27% of gross national income with a dispersion value of 6.90, while tourism receipts, GDP per capita, and inflation have their average values to be 9.66, 3.68, and 15.78, respectively, and their respective standard deviation values are given to be 0.78, 0.33, and 19.81.

Particularly, the values of kurtosis and skewness must be three and zero, respectively, for the variables to be normally distributed. Adjusted net saving and inflation are positively skewed with long right tails, whereas tourism receipts and GDP per capita are negatively skewed with left long tails. Furthermore, the kurtosis estimations imply that all the variables of interest are flat-topped (platykurtic), with the exception of GDP per capita, which is peaked-topped (leptokurtic). These results (skewness and kurtosis) are confirmed by the Jarque-Bera statistic, indicating that all the variables are not normally distributed based on the rejection of the null hypothesis of normality. The correlation test indicates that all the explanatory variables are negatively correlated with the explained variable (adjusted net saving). Furthermore, tourism and GDP per capita have a positive link, as does GDP per capita and inflation, while tourism and inflation are negatively related. Except for tourism and GDP per capita, which have a correlation value of 0.823, there is no indication of highly connected variables. This problem can be addressed via the estimating techniques employed.

Results and Discussion

Cross-sectional Dependence Test

Because of their comparable qualities, the MINT countries have been grouped together. Given these shared qualities, cross-sectional dependence among countries or variables of interest is a possibility. To assess the presence or absence of this issue, the study uses Pesaran’s (2004) cross-sectional dependence (CD) test. The Pesaran CD result is shown in Table 3. The result shows that there is evidence of cross-sectional dependence since all the variables, except adjusted net saving, rejects the null hypothesis of no cross-sectional dependence, indicating the existence of residual cross-sectional dependence. Due to significant economic interconnections between various cross-sections within the panel, any shock in tourism, GDP per capita, or inflation within a nation in the MINT countries panel may spill over to other nations in the same panel. Given the significant p-values, Pesaran and Yamagata’s (2008) slope homogeneity test in Table 4 indicates the presence of slope heterogeneity in the panel analysis. As a result, estimate procedures that account for cross-sectional dependence and slope variability among nations or variables are used in the study.

Table 2.

Descriptive Statistics.

	ADNS	LITR	LGDP	INF
Statistic
Mean	10.269	9.658	3.675	15.781
Std. Dev.	6.896	0.777	0.332	19.808
Skewness	1.169	–1.191	–0.153	2.470
Kurtosis	5.433	3.350	1.436	8.197
Jarque-Bera	45.517	23.201	10.158	205.681
Probability	0.000	0.000	0.006	0.000
Observations	96	96	96	96
Correlation test
ADNS	1
LITR	–0.492	1
LGDP	–0.335	0.823	1
INF	–0.007	–0.121	0.036	1

Source: Authors’ computations.

Note: All variables are in natural logarithm, except ADNS and INF.

Table 3.

Pesaran Cross-sectional Dependence Test.

Variable	CD-test	p value
ADNS	–0.032	.974
LITR	9.895***	.000
LGDP	10.626***	.000
INF	4.829***	.000

Source: Authors’ computations.

Note: *** depicts significance at 1%.

Table 4.

Slope Homogeneity Test.

Variable
ADNS	4.256***(0.000)	4.579***(0.000)
LITR	3.569***(0.000)	3.927***(0.000)
LGDP	4.725***(0.000)	5.103***(0.000)
INF	1.574(0.115)	1.694*(0.090)

Source: Authors’ computations.

Note: *** and * depict significance at 1% and 10%.

Panel Unit Root Test

Following works such as Tachie et al. (2020), Mensah et al. (2019), and Dogan et al. (2017), the study applies the second-generation unit root test, which considers the issue of cross-sectional dependence. Table 5 shows that all of the variables are integrated of order (1), meaning they are stationary at the first difference, according to the findings of the CIPS and CADF panel unit root tests. The conventional IPS unit root test also confirms these findings.

Panel Cointegration Test

We next determine the existence of a long-run relationship among the variables since they are stationary when first differenced by employing the Westerlund-ECM panel cointegration test. This test is chosen above other cointegration tests because it deals with the issue of cross-sectional dependency. The rejection of the null hypothesis of no cointegration for the statistics Gτ and Pτ at a 1% level of significance, and G∝ and P∝ at a 10% level of significance in Table 6 indicates that the variables have a long-run relationship. Put differently, there is a cointegrating relationship among the variables. This means that the exogenous variables (tourism receipts, GDP per capita, inflation) have a long-run elastic relationship with adjusted net saving (sustainable development).

Table 5.

Panel Unit Root Tests.

Variable	CIPS		CADF		IPS
Variable	Without trend	Trend	Without trend	Trend	Without trend	Trend
ADNS	–2.144	–3.021	–1.596	–2.457	–0.807	–0.888
ΔADNS	–5.557***	–5.814***	–3.283***	–3.589***	–3.575***	–2.795***
LITR	–1.156	–1.810	–1.668	–2.190	2.513	–0.107
ΔLITR	–3.828***	–4.061***	–3.197***	–3.604***	–2.540***	–2.037**
LGDP	–1.078	–1.751	–1.771	–1.176	2.472	–1.161
ΔLGDP	–2.841**	–3.117***	–3.404***	–3.851***	–10.566***	–6.762***
INF	–2.789	–2.012	–2.113	–2.314	–3.647	–2.287
ΔINF	–3.489***	–3.777***	–3.752***	–4.266***	–6.063***	–5.813***

Source: Authors’ computations.

Note: IPS and CIPS panel unit root tests observe the null hypothesis of non-stationarity

*** and ** depict significance at 1% and 5%.

Long-run Estimates

Since the long-run link amidst variables has been established using Westerlund-ECM panel cointegration results, the appropriate parameters of the various exogenous variables must be estimated empirically. In this article, we look at the impact of tourism receipts, GDP per capita, and inflation on sustainable development. Most research in the literature concentrating on panel data econometric techniques often use estimation methods such as OLS, dynamic OLS (DOLS), and/or fully-modified OLS (FMOLS), which do not account for cross-sectional dependence. These techniques cannot produce reliable long-term coefficient estimates in the face of residual cross-sectional dependence which is obvious in our investigation. As a result of the panel’s residual cross-sectional dependence in Table 3, the study employs the AMG estimator as the primary long-run estimation technique which is superior to DOLS and FMOLS.

The AMG estimating approach’s long-run estimation findings are presented here. To begin with, the post-estimation test from the AMG result reveals the validity of the sustainable development model employed. This hangs on the fact that the Wald chi-square test statistic is statistically significant with a p-value of 0.00, indicating that the sustainable development predictive model proposed in this study is a good specification and efficient enough to produce reliable forecasting results.

Table 6.

Westerlund-ECM Panel Cointegration Test.

Statistic	Value	z Value	p Value
Gτ	–4.034	–3.055***	.001
G∝	–14.690	–1.508*	.058
Pτ	–8.384	–3.673***	.000
P∝	–12.075	–1.599*	.055

Source: Authors’ computations.

Note: ***, **, and * depict significance levels at 1%, 5%, and 10%.

Table 7 shows how the AMG estimator’s results match the a priori predictions. Tourism has a favorable and significant effect on sustainable development in MINT nations, according to long-run estimations, with a 1% rise in tourism receipts causing sustainable development to grow by 0.062 unit. In line with other studies such as Eusebio et al. (2014), Liu (2003), and Sharpley (2000), the development of the tourism industry in the MINT countries via tourism receipts can assist the countries in achieving sustainable development. Another explanation is that, in addition to contributing to economic growth, tourism may be used to promote sustainable economic development by increasing quality of life, generating jobs, establishing long-term linkages between industries, and safeguarding natural heritage for future generations. The MINT countries’ tourism industries have seen remarkable growth in recent years as shown in Table 1. Furthermore, with the exception of Nigeria, all MINT countries are highly ranked by the WEF (2019). Nonetheless, Nigeria offers a diverse range of tourism opportunities, including waterfalls, mountains, hills, springs, lakes, caves, parks, festivals, and carnivals. If these resources are completely utilized without these obstacles, the country will be able to attain sustainable development.

As expected, GDP per capita contributes positively and significantly to sustainable development as measured by adjusted net saving. Specifically, a 1% rise in GDP per capita results in a 0.481 unit increase in sustainable development, implying that GDP per capita has a significant impact on sustainable development. This outcome corroborates the findings of Koraila and Pradhan (2019), Twerefou et al. (2017), and Kaimuri and Kosimbei (2017) who establish that per capita income boosts adjusted net saving. In other words, a higher GDP per capita means more revenue for consumers, which will have a good impact on savings level. The discovery in the MINT countries is expected because the countries are classified based on their growth trajectories, that is, the countries are predicted to be the world’s most powerful emerging markets due to their high growth rate potentials. Thus, income growth in MINT countries is beneficial for an increase in adjusted net saving, which helps to put MINT countries on the path to sustainable development. As economic growth among individuals in the MINT countries improves, their lifestyle also changes, leading to a shift in consumer behavior toward saving.

Inflation, as one of the explanatory variables, exhibits the expected sign but has an insignificant effect on sustainable development in MINT countries. The result implies that a higher inflation rate tends to reduce adjusted net saving, causing the MINT countries to stray off the path of sustainable development. Simply put, a higher inflation rate indicates that a higher proportion of income is spent, indicating a low level of savings which is bad for sustainable development as measured by adjusted net saving. The negative impact of inflation on sustainable development is supported by the study of Koraila and Pradhan (2019). In MINT countries, inflation being insignificant implies that people in the country tend to be indifferent to the rate of inflation as regards their savings. This could be because they prefer to lower their consumption level rather than their level of savings as general prices rise.

Table 7.

Heterogeneous Long-Run Parameter Estimators (AMG, MG, and CCEMG Estimators).

Variable	AMG Estimates			MG Estimates			CCEMG Estimates
Variable	Coeff.	SE	p val.	Coeff.	SE	p val.	Coeff.	SE	p val.
LITR	6.151**	2.385	.010	4.608***	1.180	.000	8.372**	3.951	.034
LGDP	48.147**	24.029	.045	57.894	41.429	.162	–5.080	19.651	.786
INF	–3.466	3.793	.361	0.714	4.216	.865	–3.624*	2.091	.083
	Wald Test: Chi2 =11.50(0.000)			Wald Test: Chi2 =17.22(0.000)			Wald Test: Chi2 =12.06(0.000)

Source: Authors’ computations.

Note: ***, **, and * represent significance level at 1%, 5%, and 10%, respectively.

To check the robustness of the estimates obtained through the AMG approach, the study adopts both Mean Group (MG) and CCEMG estimators. While the former ignores cross-sectional reliance, the latter takes into consideration the existence of cross-sectional dependence and slope heterogeneity (Le & Bao, 2019). The focus here is on the core variables, that is, ADNS and tourism receipts. The MG and CCEMG estimators’ findings are consistent with the AMG approach, indicating that tourism has a significant and positive impact on sustainable development. A 1% rise in tourism causes sustainable development to increase by 0.046 unit (using the MG approach) and 0.084 unit (using the CCEMG approach). These results also imply that if MINT countries are to achieve sustainable development, the tourism industry must be developed. The results of the diagnostic test (Wald chi-square test) show that the sustainable development model utilized in this study is accurate.

Concluding Remarks

This research aims to examine if tourism can be an ally to sustainable development in MINT countries between 1995 and 2018. Mexico, Indonesia, Nigeria, and Turkey are known as the MINT countries because of their similar economic traits, growth paths, natural and human resource wealth, and demographic similarities. The study uses adjusted net saving and ITR in these countries as measures of sustainable development and tourism, respectively. In achieving its objectives, the study employs the AMG estimation technique to estimate the long-run parameters. Besides this approach, the MG and CCEMG techniques are used to check the robustness of the estimates obtained via the AMG approach. The results from the three estimators reveal that tourism is indeed an ally to sustainable development in the MINT countries since there is a considerable positive relationship between tourism and sustainable development. In other words, tourism can genuinely set the MINT countries on the path of sustainable development. This suggests tourism activities have positively influenced the environment, thus creating a safer and healthier landscape, and more influx of tourists would see more environmental preservation. The implication is that any policy that will improve the performance of the tourism industry will ensure sustainable development in MINT countries. Consequently, better tourism earnings via tourism development are required for MINT countries to achieve sustainable development. The governments in MINT countries, therefore, should concentrate on attaining sustainable tourism development. As a result, the following steps can be taken to accomplish this: (a) MINT countries’ governments should offer laws and controls for the tourism industry to assure competition with other tourist destinations; (b) Like the Mexican government, other MINT countries’ governments should prioritize tourism in their policy agendas in addition to raising the level of national investment and government funding in tourism; and (c) To improve tourist influx and hence tourism revenue, MINT countries, particularly Nigeria, should create a safe and secure environment devoid of crime and corruption and provide adequate infrastructural development to attract more tourists. Other studies can look into the impact of other areas of tourism (such as tourist arrivals and tourism spending) on MINT countries’ sustainable development.

Footnotes

Declaration of Conflicting Interests

Funding

The authors received no financial support for the research, authorship, and or publication of this article.

ORCID iDs

Tolulope Osinubi

Folorunsho Ajide

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