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Article

Examining Factors That Influence the International Tourism in Pakistan and Its Nexus with Economic Growth: Evidence from ARDL Approach

by
Naqib Ullah Khan
1,*,
Wajid Alim
2,
Abida Begum
3,
Heesup Han
4,* and
Abdullah Mohamed
5
1
School of Public Administration, Central South University, Changsha 410083, China
2
Lahore School of Accountancy and Finance, University of Lahore, Lahore 54660, Pakistan
3
School of Marxism, Northeast Forestry University, Harbin 150040, China
4
College of Hospitality and Tourism Management, Sejong University, 98 Gunja-dong, Gwanjin-gu, Seoul 143-747, Korea
5
Research Centre, Future University in Egypt, New Cairo 11835, Egypt
*
Authors to whom correspondence should be addressed.
Sustainability 2022, 14(15), 9763; https://doi.org/10.3390/su14159763
Submission received: 13 June 2022 / Revised: 17 July 2022 / Accepted: 19 July 2022 / Published: 8 August 2022
(This article belongs to the Section Tourism, Culture, and Heritage)
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Abstract

:
Tourism has played an influential role in the global economies. It is considered the third largest socio-economic sector and contributes about 9% to the world economy’s GDP. Tourism enhances investments, creates job opportunities, harnesses entrepreneurship, and secures heritage and cultural values and norms. However, tourism faces serious challenges in developing countries, especially in Pakistan. Therefore, the main aim of the current study is to examine the influential factors that affect the tourism sector and exhibit the nexus between tourism and economic development in Pakistan. The study collected data from the Global Terrorism Index (GTI), Pakistan Tourism Statistics, and the State Bank of Pakistan (SBP) for the period from 1995 to 2017. The results of the study present that terrorism, which hampers peace and certainty, tourism expenditure and inflation rate, has a strong influence on the tourism sector in Pakistan. Moreover, the study also disclosed that tourism boosts the long-term macro-economic factors and leads to the economic development of Pakistan.

1. Introduction

Tourism is referred to as the “goose that lays golden eggs” in modern economics [1]. The tourism industry is one of the leading industries in the world economy, as reported in the late and early twenty-first century. The association between tourism and economic development has been widely studied in developed and developing economies. Tourism is a point of interest for policymakers due to its rapidly growing nature in many countries. The tourism industry has an influential impact on the world economy [2], creating 292 million jobs and increasing the global GDP by 10.2% in 2016. It is also predicted that this industry will increase its contribution to the global GDP and create 380 million jobs by 2027, which is approximately 11% of jobs in the world. Governments worldwide are trying to tackle macroeconomic problems concerning the economy, such as poor growth, high unemployment, and alarming inflation by subsidizing the productive sector within the country. Tourism is a significant potential growth sector, which helps to reduce economic hurdles by generating revenue, creating jobs, increasing household income, and impacting the balance of payment [3,4,5,6].
The influence of tourism is undeniable because it has provided a safe harbor and fertile industry for both developed and developing economies. The United Nations World Tourism Organization (UNWTO) presents that the tourism industry contributes to various sectors of the economy. These sectors comprise hoteling and accommodation, food and beverages, transport and transport equipment, travel and tour agencies, cultural events, sports and recreational activities, and the trade of country-specific tourism goods [7]. Tourism helps policy-makers to boost economic growth by generating regional job opportunities, strengthening the supply of foreign exchange, promotes and enhancing the transportation, construction, and accommodation sector [8]. This collectively contributes to tackling inequalities in regional welfare, because tourism transfers income from the developed economies to developing economies [9], confirming the positive nexus of tourism and economic development [7,10,11].
Pakistan is a developing country that has extensive tourism potential. The World Tourism and Travel council forecast that the Pakistan tourism industry has the potential to grow up to USD 39.8 billion within a decade [12]. Pakistan is full of marvelous tourist sites, comprising stunning mountains, gorgeous hills, and amazing deserts covering an area of thousands of miles, which can garner attention [13]. Moreover, Pakistan has also vast potential from a religious perspective. The dominant religious spots are Sufi shrines, Hindu temples, Sikh gurdwaras, and Buddhist monasteries, which could boost the tourism sector. Likewise, its heritage, as an ancient civilization, also makes it an attractive spot for tourists [14]. In addition, Pakistan is also one of the leading countries in mountain tourism, with enormous mountainous peaks [12]. The international tourism receipt in 2016 for South Asia was approximately USD 33.82 billion. Pakistan made a lower contribution (less than 1 pc) compared to India (69 pc), Sri Lanka (10 pc) and the Maldives (7 pc).
A vast, conflicting literature exists on the causal association between tourism and economic development in different sets of economies. The literature presents a conflicting outcome for the relationship between tourism and economic development [7,9,15,16]. These differences in the results might be due to differences in the methodology, such as studies that only consider developed economies and time-variances. However, these conflicting results make this enriched industry ambiguous, and further work should be conducted to present a clearer picture of such a fruitful industry. Therefore, this study will try to contribute to tackling the shortcomings of the tourism industry. The foremost aim of the current study is to examine the crucial factors that resulted in the crawling growth of the tourism industry in poorly developed economies such as Pakistan. Additionally, this study examines how countries overcame the financial crisis of 2008–2009 through tourism while looking at the more pertinent period of 1995–2017, which includes and goes beyond the most recent financial crises. Moreover, this study also digs deeply into the causal association between tourism and economic development in Pakistan.

2. Review of Literature

2.1. Theoretical Support

Theoretically, the nexus between tourism and economic development is explored through the development theories paradigm. Modernization theory (MT) is one of the most prevalent and known development theories, which received admiration in the 19th and mid-20th centuries. This theory argues that the socio-economic development of a territory relies on the proper utilization of internal forces and sources. Tourism is considered to be an important intrinsic source and force, which has the potential to contribute to the economic development of a country. Tourism is a growing industry, as it redistributes wealth, is free from trade barriers, utilizes natural resources, and has more opportunities for backward linkages throughout the local economy [17]. However, modernization theory does not consider the external forces that can bring social change and economic development, while the nexus of tourism and economic development rely on both internal and external factors. Accordingly, to obtain an entire picture of tourism and economic development, the study will also use dependency theory [18], which suggests the use of external expertise and modernize strategies for the efficient utilization of internal sources. Utilizing both intrinsic and extrinsic forces can boost the tourism sector and, hence, the economic development of a country [19]. Development theories have both positive and negative links to tourism and are considered an initiative of pertinent and viable tourism development [20]. Following previous studies [17,21], this study also relies on development theories to research the association between tourism and economic development.

2.2. Empirical Support

Economic Growth in Nexus with Tourism Development

The tourism industry emerged as a prevalent sector in recent decades in terms of providing jobs, enhancing foreign exchange, increasing government revenue, and declining poverty in the country [7,22,23]. A wide range of literature exists on the relationship between tourism and economic development [6,9,24,25]. However, this association is too diverse, and most of the time is tough to estimate, because the tourism–economy nexus varies across countries due to the diversity of national policies and legal frameworks. Previously, the relationship between tourism and economic development was mostly dependent on the geographical condition of the country; however, recently tourism trends, needs, and patterns have changed. In the current scenario, a new sort of tourism has emerged, which has maximized the possibility of generating revenue and achieving a sustainable economic growth in the country. Some opposing arguments can also be found in previous studies regarding tourism growth [26].
Tourism has a direct impact on living standards. It increases the production of goods and services and maximizes the revenue-generating ability, which allows it to make a fruitful contribution to the GDP of the country [27,28]. None of the findings disclosed that tourism has a negative effect, but each study unveils the strength and value of tourism in the studied territory and claims that this sector is an engine for the economic growth of the country [21,29,30]. The economic development aspect of this enriched sector compels and attracts research scholars [6,9,11,31,32,33,34] to unveil it more, to help the policy-makers utilize this sector for the country’s economic development. Most of the findings present a unidirectional relationship between tourism growth and the economic development of the country [35,36,37]. The previous literature largely supports the positive association between economic growth and tourism development [7,15,38]. However, more attention is needed because most of the findings are mixed, and vary across countries.
Previously, a variety of techniques and hypotheses were used to consider the causal association between tourism and economic development [9]. The most frequently used hypotheses were tourism-led growth, conservation, feedback, and neutrality. The hypothesis of tourism-led growth presents that the tourism industry boosts the economic condition of the country because this sector enhances revenue, creates job opportunities, and transfers wealth to poorly developed areas [19,39], However, a decline in tourism activities brings about an economic recession, which confirms that tourism is a prevalent factor and has an influential role in the entire economy of the country. Therefore, countries, especially poorly developed countries, must focus on this sector to strengthen their economy and become economically safe and sound [9,16,34,37,39,40,41,42,43,44].
On the other hand, the conservative hypothesis posits that it is economic growth that causes the tourism development in the country. The supporting argument for this hypothesis is that only a stable economy increases tourism demand. Only individuals in good financial conditions may indulge in tourism practices; however, a country facing distressed financial conditions can demotivate individual thirst for these visits, hence weakening the tourism industry [15,38,45,46]. In addition, the feedback hypothesis for the causal association between tourism and economic development is called the reciprocal hypothesis, which posits that both factors (tourism and economic growth) are interdependent and complement one another. This hypothesis further exhibits that government investment in other sectors may boost the tourism sector, which results in the economic development of the entire country [15,47,48,49]. Interestingly, in the vast literature on the relationship between tourism and economic development, some studies [15,50,51], also present no causal association between tourism and the economic development of the country.

3. Tourism and Terrorism

Insecurity and fear are the most vital and crucial barriers to both national and international tourists [52]. Terrorism is a major source of fear and insecurity and a major concern for the globe and local territories [53]. The tragedy that took place on 9/11 dramatically changed the mindset of international tourists. A more thorough check and stringent visa processing can easily affect the trends in international tourism across the world. This ratio of international tourists declined across most countries. Tourist spots show the symbol and identity of the country and are also soft and easy targets for terrorists [54]. This, along with other terrorist attacks, significantly influences the economic condition of the country, such as European countries, which lost from 0.8 to 1 billion USD due to terrorist attacks in Paris [55]. Terrorism practices spread fear, discourages investors and demotivates tourists; hence, it provides a strong negative nexus for economic development [56,57]. Pakistan was also a major victim of terrorism (GTD) after 9/11, which brought huge losses to the country in all directions (economically, socially, security-wise, etc.). Pakistan was considered an insecure country, which discouraged investors and tourists. This ruined the tourism sector, and its contribution to the economic growth of the country was lacking [58,59,60].

4. Inflation and Tourism

Inflation (CPI) is one of the prevalent factors affecting the entire life of an individual. Inflation and tourism have a bi-causality nexus and gained the attention of research scholars. Jebabli [61] argued that tourism reduces unemployment and hence increases growth. However, this leads to inflation because higher rates of consumption occur due to tourism, which boosts demand in the country. Moreover, tourism also increases the money supply, which is the main indicator of inflation [62,63]. On the other hand, tourism reduces inflation within the country because it results in economic growth, where each step is controlled by the market. Fauzel and Tandrayen-Ragoobur [39] argue that no long-run association exists between money supply and inflation because tourism strengthens the country’s economy. The tourism sector is enriched, which boost the economy; hence, inflation may not alter this role of tourism [64]. However, an increase in inflation increases the cost of almost every item, which directly hits the local community, local tourists, and foreigners, discouraging tourists and bring about a decline in the tourism industry [65,66].

5. Methodology

5.1. The Data

To examine the determinants and outcomes of the tourism industry in Pakistan, this study used data for the period from 1995 to 2017. These periods were chosen for various influential reasons, as they included extensive terrorist attacks, such as 9/11, the financial crisis of 2008, and uncertainty due to insecurity. The study is quantitative, relying on quantitative time series data, which were collected from the Global Terrorism Index (GTI), Pakistan Tourism Statistics, and State Bank of Pakistan (SBP).

5.2. The Variables

This study examines the determinants and outcomes of the tourism sector in Pakistan. Two models were tested; therefore, to establish the determinants of tourism, the study treated international tourism receipt as a dependent variable, while for the outcome association, the study used GDP growth as a dependent variable, following Dogru and Bulut [67], Gramatnikovski, Milenkovski [68], and Sokhanvar, Çiftçioğlu [69]. To obtain the determinants of the tourism industry, this study used the terrorism index, inflation, and tourism expenditure as independent variables [70,71,72]. Moreover, to obtain the association between tourism and economic development, the study used tourism receipt as an independent variable.

6. Statistical Tools

6.1. Testing for Multi-Collinearity

One of the basic uses of the classical linear regression model (CLRM) is to detect the presence of perfect multi-collinearity, which shows that the relationship between all the variables is exactly linear. The existence of an exact linear relationship among these variables leads to the violation of the OLS assumption, confirms multicollinearity, and shows that the findings of the study are not reliable. Accordingly, to detect the presence of an exact linear relationship between variables, this study used the correlation matrix and VIF.

6.2. Testing for Heteroskedasticty

One of the basic assumptions of (CLRM) is that the error term (random disturbance) in the relationship between the dependent and independent variable is constant across all the values. This can be represented as follows.
  V a r ( ε t ) = σ 2
Violations of this assumption cause a severe problem called heteroskedasticty, which means that error terms are not constant across all the independent variables and makes the OLS invalid.

6.3. Unit Root Test

Data must be stationary for the unit root analysis; therefore, this study commences with a stationarity test of whether the data have a unit root or not. To check stationarity in time series data, the unit root test is used. When the variance, covariance, and mean of the data remain constant, then such data are said to be stationary. The most simple and pure time series model is the autoregressive of the integrated order one, mentioned below.
X t = ϕ X t − 1 + Ԑ t
X represents the simplest term for the time series observation, t shows time, and Ԑ is the error term in the model mentioned above. The current study used the Augmented Dickey–Fuller (ADF) unit root test to examine the order of integration.

6.4. Cointegration Test

The primary linkage of the cointegration is with the issue of spurious regression, which must be investigated as it occurs in the presence of non-stationarity or if the data are normal. The best-known cointegration tests are based on the Engle and Granger cointegration relationship. Under this relationship, analysis consists of a standard ADF test on the residuals µt. The ADF or DF test for the cointegration checks the null and alternative hypotheses, which are:
H 0 = t h e   v a r i a b l e s   a r e   n o t   c o i n t e g r a t e d
H 1 = t h e   v a r i a b l e s   a r e   c o i n t e g r a t e d

7. Autoregressive Distribution Lag (ARDL)

ARDL is a statistical co-integration technique, also called the bound-testing cointegrating technique, introduced by Odhiambo [46]. This approach follows the ordinary least square (OLS) estimation procedure for the cointegration to disclose and presents the long and short-run coefficients simultaneously. The ARDL model is usually used to analyze the lagged values [73]. The ARDL approach cannot be used where one of the variables shows integration at the second difference I (2). By obeying all these conditions, ARDL was found to be the best econometric technique compared.
T R = f ( I N ,   T I ,   T E )
G D P G = f (   T R )
H 0 :   α = β 1 = β 2 = β 3
H 1 :   α ≠ β 1 ≠ β 2 ≠ β 3 ≠ β

8. Results and Discussion

Table 1 presents information such as the mean, standard deviation, minimum and maximum values of each variable used in the study. The table further shows that the average value of the dependent variable (TR) is 0.6433205, with minimum and maximum values of 0.2869304, and 0.9898611, respectively. Similarly, the mean value of inflation (IN) is 8.034783, carrying a standard deviation of 4.474332, a minimum of 2.5, and a maximum value of 20.3. The terrorism index (TI) has a mean value of 6.925538, carrying the lowest value of 4.094345 and highest value of 8.991811; the standard deviation for the said variable is 1.608548. Moreover, (GDPG) has a mean value of 4.122538, carrying the standard deviation of 1.766771 and minimum and maximum values of 1.014396 and 7.667304, respectively.
Table 2 shows the results for the presence of an exact linear relationship among all variables. From the table, it is clear that none of the values exceed 90% as rule of thumb, which indicates the existence of perfect multicollinearity or exact linear relationship; therefore, none of the variables violate one of the basic CLRM assumptions.
Table 3 shows that the probability value (0.1166) for the Breusch Pagan test is higher than the statistical significance value (0.05), which means that the null hypothesis can be accepted. This means that the variance is constant, or homoscedasticity is present, and rejects the alternative hypothesis of non-constant variance or Heteroskedasticty.

8.1. Result of the Unit Root Test

Following other studies [74,75], this study also used the Pesaran test based on the pair-wise correlation coefficient of the OLS residuals, which is obtained from individual standard ADF regression.
The current study used a confidence level of 95% or 0.95, so, based on this, Table 4 shows that the probability value for each variable is less than the significance value (0.05) or 5%, which rejects the null hypothesis of unit root and accepts the alternative hypothesis that data are stationary at first difference. Data being stationary at first difference facilitates the study to examine the short- and long-run nexus between tourism and economic development through cointegration and ARDL.

8.2. Result of the Bound ARDL Cointegration Test

This study aims to examine the determinants and outcome of tourism receipt, so two models are established. Moreover, the study also checks the ARDL bound test for both models given in the separate tables (Table 5 and Table 6).
The F-statistics value of the bound tests confirms the long-term association between endogenous variables and exogenous variables. Based on the outcome of the bound tests (F-statistics) using the ARDL approach, the results present clear evidence for the long-term association between tourism/GDP growth and other determinant factors. Therefore, the null hypothesis for the bound test H 0 :   α = β 1 = β 2 = β 3 of no cointegration between dependent variable and other variables can be rejected, while accepting the alternative hypothesis H 1 :   α ≠ β 1 ≠ β 2 ≠ β 3 ≠ β that a long-term cointegration exists between endogenous and exogenous variables.
Table 7 and Table 8, present the results of the short- and long-term relationship between the autoregressive distributed lagged (ARDL) models. The study looks for the determinants and outcomes of tourism receipts. The results of Table 7 show that all the factors (inflation, tourism expenditure, and terrorism index) are statistically significant to tourism in the short- and the long-term. All factors except inflation influence the endogenous variable. Similarly, the results of Table 8 also show the short- and long-term nexus of tourism and economic development, for which GDP growth is used as a proxy. The table shows that strengthening the tourism industry has a significant influence on economic development in both the short- and long-term. This indicates that the tourism industry is the most prevalent and plays an important role in the country’s economy.

8.3. Model Selection

The Akaike [76] information criterion (AIC) was used for model selection, which is preferred over methods such as Bayesian information criterion (BIC), etc., as it filters and mitigates the unnecessarily complicated models. AIC is equivalent to an estimate of the in-sample error in the estimated model (which means true prediction error for the dataset used in the study). Considering the value of AIC results when deciding on the model selection, the lowest value leads to the fittest model. The lowest AIC value from the Table 9 is −3.182635, which means that this is the best model for the study. The Akaike information criteria for the model selection are also shown graphically in Figure 1. From the figure, it is also clear that models 2, 1, 0, 0 are most suitable for carrying the lowest value of −3.182635.

Graphical Representation of Tourism Expenditure, Tourism Receipt, and GDP Growth throughout the Prescribed Period

Figure 2 presents the tourism expenditure, which indicates the preferred government policy for the tourism industry. The figure shows that government attention to the prevalent industry was too poor in the early stage because tourism was not considered to have the capacity to contribute to the country’s economy. A gradual increase in tourism expenditure occurred, but this increase was not consistent throughout the period. A dramatic variation can be seen in the graph. The period from 2004 to 2007 was showed the highest FDI in the country, which enhanced investment in each sector, including the tourism industry. In 2008, the highest level of inflation caused financial distress and brought a decline in some industries, including tourism. However, as its importance became known, this sector attracted the attention of policy-makers and received good investment.
This variation in tourism expenditure also brought variation in the gross domestic product growth (GDPG) of Pakistan for the specified period. This indicates that the association between the tourism industry and economic development occurs on the same line. Figure 3 also shows a variation in the GDP growth and, to a large extent, variations occur in the same pattern as tourism expenditure. Including tourism receipts for the same period, as shown in Figure 4, makes it clearer that the three graphs show the same pattern of variation. This concludes that the tourism industry needs proper attention and hefty investments to strengthen the economy so that more can be earned through this sector, as occurs in developed countries.

9. Conclusions

Tourism plays a prevalent role in the economic prosperity of any country. This study examined the determinants and outcomes of the tourism sector in Pakistan for the period 1995–2017. Terrorism, inflation, and tourism expenditure are considered influential factors for the tourism industry because these factors represent the security, stability, and priority of a country’s policy-makers. Further, the study also unveils the role of the tourism industry in the economic development of the country through causal effects. To achieve these objectives, this study relies on the time series quantitative data and used all the relevant philosophical and statistical tools. The study findings make a fruitful contribution to both theoretical and empirical knowledge. Theoretically, the study contributes to the development theories (modernization and dependency theory) that suggest that both internal and external forces can boost the economic prosperity of a country. The tourism sector is one in which internal sources (natural, artificial, and tourism uniqueness) can be efficiently utilized, with the help of external forces (expertise, modern strategies, etc.), to boost the economic development of any country. The study findings reveal that the most prevalent factor for the tourism industry is the security conditions of that country, including the terrorism perspective. Terrorism received severe attention because it has a direct link with security. Terrorism spreads fear, threatens tourists, has a negative impact worldwide, and discourages investor and the local community. The study findings show that terrorism has a strong negative influence on the tourism industry, which means terrorism leads to a direct decline in tourism’s role in economic development, which aligns with the previous finding [55,56,57,59,71]. In addition, the study also reveals that inflation, which presents the country’s stability, has a negative nexus regarding the tourism industry because inflation reflects the certainty/uncertainty in the country. High inflation rates make everything costly, which discourages investors and tourists, leading to a decline in tourism and negatively affecting the economy. This finding aligns with previous findings [58,66,77]. Receiving enough return from tourism sectors can only be achieved with appropriate policies, attractive investment opportunities, and government expenditure. The current study also found that tourism expenditure has a strong positive nexus with the tourism industry, hence boosting the entire economy. The findings of the study regarding tourism expenditure and tourism revenue align with the previous studies [12,72,78]. Pakistan is a leading country from the perspective of tourism, with visitors flocking to visit natural, historical, and cultural sites. However, Pakistan’s tourism industry is vulnerable to all the factors (terrorism, inflation, tourism expenditure) found by the current study. Terrorism is the most vulnerable factor, because the tourism sector declined after the 9/11 attack, as terrorism was at its peak in Pakistan after this event (9/11). Similarly, high inflation also led to a decline in tourism’s contribution to the national economy of Pakistan, as shown in the results. Inflation boosts costs, which directly influences the inbound tourism revenue, and this occurs in Pakistan whenever inflation soars. However, the tourism industry can be enriched by injecting proper funds, providing facilities, lowering restrictions, and continuous investment in the sector. Therefore, due to the prevalent role that the tourism industry plays in the country’s economic development, the government must prioritize the tourism sector when shaping security, stability, and expenditure to boost the role of tourism in the country’s economy.

10. Discussion

Tourism is a growing industry in Pakistan, which leads the country by boosting the national economy and creating an attractive image for the country. From the perspective of people, tradition, customs, and culture, Pakistan offers attractive tourism opportunities to the world’s most affluent tourists. Unfortunately, due to poor attention paid by the state and society, security and stability issues, and other reasons, the real beauty of Pakistan has not been harvested. Moreover, Northern Pakistan’s hidden treasure and tourism potential remain undiscovered due to the lack of proper attention that has been to the tourism sector by the government, instability in the country due to the lack of long-term planning, and the security situation, which was at its peak after 9/11. The government needs to pay proper attention to designing policies to strengthen the tourism industry and tackle all the factors that negatively affect this fruitful industry. Here, the role of the government is not sufficient to boost this underrated industry in Pakistan. The tourism organization and tourism ministry must work effectively, with the proper coordination of central, provincial, and local government, to ensure the improvement and development of this sector. These organizations need to improve the basic infrastructure, and natural, cultural and spiritual resources, as well as advertising across the world, to encourage investors and visitors. The government also needs to announce this sector as tax-free for the calculated period, which will help to attract investors, and then monitor it accordingly. The study found that terrorism, inflation, and tourism expenditure can boost this sector; these aspects are not in a good position in Pakistan. However, these are not proper excuses for tourism’s downfall and all these factors can be tackled easily.

11. Limitations and Recommendations of the Study

The current study has a few limitations. This study relies on quantitative time series data to examine the factors. However, qualitative data should be acquired by interviewing domestic and foreign tourists who stayed at hotels across tourism locations to uncover the common nexus among these factors, specifically regarding determinants and tourism. This will provide a new picture of the tourism industry and would also help to gather fruitful suggestions from tourists, which will further help the policy-makers when designing policies for the tourism sector. The study is also limited in timespan; the timespan was chosen due to the 9/11 attack and its repercussions for Pakistan, as Afghanistan is a close neighbor. Further research could widen the period to consider COVID-19 and its repercussions for the tourism sector, as this had a large impact on the sector. Finally, narrowing tourism to religious tourism also provides an attractive area for researchers to work on and explore.

Author Contributions

Conceptualization, N.U.K. and W.A.; methodology, W.A.; software, A.B.; validation, H.H., N.U.K. and W.A.; formal analysis, W.A.; investigation, A.B.; resources, A.M.; data curation, A.M.; writing—original draft preparation, W.A.; writing—review and editing, A.M.; visualization, A.B.; supervision, H.H.; project administration, N.U.K.; funding acquisition, A.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The relevant information can be found in the article, or can be obtained from the corresponding authors on reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 14 09763 g001 550
Figure 1. Graphic representation of model selection.
Figure 1. Graphic representation of model selection.
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Figure 2. Tourism Expenditure.
Figure 2. Tourism Expenditure.
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Figure 3. GDP Growth.
Figure 3. GDP Growth.
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Figure 4. Tourism Receipt.
Figure 4. Tourism Receipt.
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Table
Table 1. Descriptive Statistics.
Table 1. Descriptive Statistics.
VariableObsMeanStd DevMinMax
TR230.64332050.2108840.28693040.9898611
IN238.0347834.4743322.520.3
TE2314.737397.7568824.4728.13
TI236.9255381.6085484.0943458.991811
GDPD234.1225381.7667711.0143967.667304
Source: Author Calculation.
Table
Table 2. Correlation Matrix.
Table 2. Correlation Matrix.
TlINTETRGDPG
TI1.0000
IN0.28361.0000
TE0.65470.02931.0000
TR−0.80450.12360.82151.0000
GDPG −0.0294−0.36100.41430.13421.0000
Source: Author Calculation.
Table
Table 3. Results of Breusch Pagan for Heteroskedasticty.
Table 3. Results of Breusch Pagan for Heteroskedasticty.
Chi-Square StatisticProbability
2.600.1166
Source: Author Calculation. Confidence level 95%.
Table
Table 4. Results of Unit Root Test.
Table 4. Results of Unit Root Test.
Variablet-StatProbConclusion
GDPG−4.27933 *0.0034I(1)
IN−5.756241 *0.0001I(1)
TE−4.475321 *0.0022I(1)
TI−4.068925 *0.0054I(1)
TR−3.974127 *0.00670I(1)
Source: Author Calculation, Confidence level 95%. * Represent significance level at 5%.
Table
Table 5. Result of ARDL Bound Test for Model I/for Determinants of TR.
Table 5. Result of ARDL Bound Test for Model I/for Determinants of TR.
SignificanceLower Bound I(0)Upper Bound I(1)
10%2.723.77
5%3.234.35
2.5%3.694.89
1%4.295.61
F-Statistics4.99
Source: Author Calculation.
Table
Table 6. Result of ARDL Bound test for Model II/for the outcome of TR.
Table 6. Result of ARDL Bound test for Model II/for the outcome of TR.
SignificanceLower Bound I(0)Upper Bound I(1)
10%4.044.78
5%4.945.73
2.5%5.776.68
1%6.847.84
F-Statistics6.44
Source: Author Calculation.
Table
Table 7. Results of Autoregressive Distributed Lagged (ARDL) (TR is Dependent Variable).
Table 7. Results of Autoregressive Distributed Lagged (ARDL) (TR is Dependent Variable).
Co-Integrating Form
VariableCoefficientStd. Errort-StatisticProb.
DLOG(TR(−1))0.5178280.1923582.6920050.0175
DLOG(IN)0.1311660.0281014.6677230.0004
DLOG(TE)0.0983390.0296813.3132430.0051
DLOG(TI)−0.0337430.013488−2.5017830.0124
CointEq(−1)−0.1186870.059557−1.9928410.0661
Long-Coefficient
VariableCoefficientStd. Errort-StatisticProb.
LOG(IN)0.0990970.1669980.5934000.5624
LOG(TE)0.8285560.3222832.5708950.0222
LOG(TI)−0.0347430.013488−2.5117830.0224
C0.8374890.7614951.0997950.2900
Source: Author Calculation.
Table
Table 8. Results of Autoregressive Distributed Lagged (ARDL) for the outcome of TR.
Table 8. Results of Autoregressive Distributed Lagged (ARDL) for the outcome of TR.
Co-Integrating Form
VariableCoefficientStd. Errort-StatisticProb.
DLOG(TR)4.0114041.7180202.3349000.0525
CointEq(−1)0.6128750.2060252.9747550.0081
Long-Coefficient
VariableCoefficientStd. Errort-StatisticProb.
LOG(TR)1.1965390.5359422.2325910.0743
C0.9855240.3068243.2120150.0048
Source: Author Calculation.
Table
Table 9. Results of Model Selection based on Akaike Information Criterion (AIC) Criteria.
Table 9. Results of Model Selection based on Akaike Information Criterion (AIC) Criteria.
ModelLogLAIC *BICHQAdj. R-SqSpecification
440.417671−3.182635−2.834461−3.1070730.985105ARDL(2, 1, 0, 0)
340.822123−3.125916−2.728003−3.0395590.984566ARDL(2, 1, 0, 1)
240.511037−3.096289−2.698376−3.0099320.984102ARDL(2, 1, 1, 0)
140.862655−3.034539−2.586886−2.9373860.983344ARDL(2, 1, 1, 1)
1138.514995−3.001428−2.653254−2.9258650.982146ARDL(1, 1, 0, 1)
938.654889−2.919513−2.521600−2.8331560.981027ARDL(1, 1, 1, 1)
1236.037563−2.860720−2.562285−2.7959520.978902ARDL(1, 1, 0, 0)
1036.208487−2.781761−2.433587−2.7061980.977760ARDL(1, 1, 1, 0)
1630.465998−2.425333−2.176637−2.3713600.966376ARDL(1, 0, 0, 0)
1431.299940−2.409518−2.111083−2.3447500.966872ARDL(1, 0, 1, 0)
1530.852039−2.366861−2.068426−2.3020930.965429ARDL(1, 0, 0, 1)
1331.739569−2.356149−2.007975−2.2805870.965962ARDL(1, 0, 1, 1)
830.713978−2.353712−2.055277−2.2889440.964971ARDL(2, 0, 0, 0)
631.422543−2.325956−1.977782−2.2503940.964918ARDL(2, 0, 1, 0)
730.878794−2.274171−1.925997−2.1986080.963054ARDL(2, 0, 0, 1)
531.743687−2.261304−1.863390−2.1749460.963358ARDL(2, 0, 1, 1)
Source: Author Calculation. (*), Study used AIC for Model specification.
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Khan, N.U.; Alim, W.; Begum, A.; Han, H.; Mohamed, A. Examining Factors That Influence the International Tourism in Pakistan and Its Nexus with Economic Growth: Evidence from ARDL Approach. Sustainability 2022, 14, 9763. https://doi.org/10.3390/su14159763

AMA Style

Khan NU, Alim W, Begum A, Han H, Mohamed A. Examining Factors That Influence the International Tourism in Pakistan and Its Nexus with Economic Growth: Evidence from ARDL Approach. Sustainability. 2022; 14(15):9763. https://doi.org/10.3390/su14159763

Chicago/Turabian Style

Khan, Naqib Ullah, Wajid Alim, Abida Begum, Heesup Han, and Abdullah Mohamed. 2022. "Examining Factors That Influence the International Tourism in Pakistan and Its Nexus with Economic Growth: Evidence from ARDL Approach" Sustainability 14, no. 15: 9763. https://doi.org/10.3390/su14159763

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