EVALUATING THE RELATIONSHIP BETWEEN INTERNATIONAL TOURISM RECEIPTS, REAL EXCHANGE RATES, AND ECONOMIC GROWTH:
A CASE STUDY OF THE GAMBIA
*Author: Kaddijatou B Jallow
*Supervisor: Dias Satria, SE. M.App.Ec., Ph.D.
Abstract
This study aims to evaluate and determine the association between tourism receipts, real exchange rate as independent variables, and gross domestic product (GDP) as dependent variable implementing time-series data from 1996 to 2018 yearly of The Gambia in international tourism. In addition, econometric approaches such as OLS to determine the influence of international tourism receipts on GDP in The Gambia and the effects of the real exchange rate on the country's gross domestic products. The outcome shows international tourism receipts have a positive relationship to GDP at 5% level with (3.64) of coefficient the exchange rate is (-2.89) of coefficient negatively related to GDP at 5% level. The Johansen cointegration, Granger causality, and VEC test the association between international tourism receipts, real exchange rate, and GDP in the Gambia. The results indicated one cointegration relationship and showed no long-run causality from INT_RCPT and REER to GDP. There is short-run causality running from GDP and exchange rate to international tourism receipts in The Gambia. The results of Granger causality show entirely all variables do not cause each other.
Keywords: International Tourism Receipt, Real Exchange Rate, GDP, OLS, Cointegration, Granger Causality, and Gambia
INTRODUCTION
The tourism sector has become one of the most prominent and fastest-growing economic industries, in the world, over the last few decades, which has been experiencing growth continuously and becoming more diversified (Loum, 2020).
An enormous amount of literature has examined and evaluated the relationship
between international tourism receipt, real exchange rate, and GDP, that tourism demand has positive influences on economic growth and plays a vital role in countries' economic growth. Past studies showed that many people support that tourism is beneficial to economic growth and must be advanced. The fast growth of the tourism division has led to a boost in
government revenues, incomes, directly and indirectly through multiply effects (Pavlic, Svilokos, & Tolic, 2014). The Gambia tourism industry is the largest source of foreign exchange earnings and significantly contributes 20 percent to Gross Domestic Product (GDP) yearly.
Over the years, tourism contributed significantly to GDP and employment with the positive number of investments in hotels by the private segment and other associated infrastructure through the government in The Gambia. Due to the 2019 pandemic, the tourism sectors, also trade industry were the most influential on the supply side (African Development Bank, 2018). The decrease in tourism receipts and remittances extended the current account deficit to 8.6% of GDP from 5.3% in 2019 (African Development Bank, 2018). In 2020, about 20,000 jobs lost projected; foreign exchange reserves as projected to fall by $10 million, with 40% of the unemployment rate (African Development Bank, 2018).
The Gambia dalasi continued to be considered stable and volatile in the market had improved. In 2018, The
Gambia dalasi depreciated against the United States dollar by 4.5%, nevertheless appreciated against the euro by 3.0%, the British pound by 4.0%, and CFA by 0.4%
(The Gambia Central Bank, 2018). The purpose of the research is to determine and examine the association between international tourism receipts and the real effective exchange rate as independent variables and gross domestic product (GDP) as a dependent variable in The Gambia's national economic growth, by exercising econometrics structure OLS, ADF, VECM, Johansen cointegration of trace and Max-eigenvalue and examine Granger causality test.
Research Problem
1. What is the impact of international tourism receipts on The Gambia's economic growth?
2. What is the effect of exchange rates on The Gambia's economic growth?
3. How is the relationship between international tourism receipts, real exchange rate, and GDP in The Gambia?
LITERATURE REVIEW
International TourismOver the recent decades, international tourism has been expanding in many markets around the universe. A tremendous amount of literature has explored and investigated the relationship between tourism receipt, real exchange rate, and economic growth, which showed that tourism has a positive influence on economic growth and is crucial in the national economic development strategy for tourism division.
Belloumi (2010) states that tourism can stimulate economic growth. He noted that tourist activities provide income, foreign exchange earnings, and employment as a tool. Tourism has a positive impact on gross domestic product growth and concluded cointegration between tourism and economic growth according to (Belloumi, 2010).
Wu & Wu (2018), in their research, examine the causal relationship between international tourism receipt and economic growth of 31 regions in China. The results showed that international tourism has a
positive effect on the long-run economy of those regions. Tourism is a significant foreign exchange earner that contributes to capital goods useful in production process (Wu & Wu, 2018).
International Tourism Receipts World Bank (2021) defined international tourism receipts as “expenses by international arriving visitors, with payments to national carters for international transport”. Tourism has become extensively recognized as a positive consequence on economic development in the long run through several channels. The increase in tourism flows can bring optimistic economic significance to tourist-centric countries, especially employment possibilities, foreign exchange incomes, and revenues (Pavlic, Svilokos, & Tolic, 2014). It can generate demand for new goods or services that can drive the development of those industries instead of imports. As for a national economy to profit from tourism hang on the obtainability of finance in developing the required infrastructure (Wall, Willis, & Roman, 2009).
A Real Effective Exchange Rate “the nominal effective exchange rate which
determines the value of a currency in contrast to a weighted score of many international currencies and differentiated with a price deflator index” World Bank (2021). Currency exchange rates in specific have significance due to their ability to indicate tourist-oriented product and service prices for foreign tourists visiting the country. A previous study showed exchange rate negatively correlates with foreign tourist arrivals, and nations with a higher exchange rate are fewer desirable targets for foreign visitors.
Gramatinikovski, Milenkoski, & Blazheska (2016) investigated the effect of international tourism receipts on GDP in the circumstance of the nation of Macedonia result shows the influences of the tourism receipts on the GDP could lead to substantial issues on the country economic development. The research paper of Khandaker & Islam (2017) indicates that the exchange rate is negatively correlated with tourism income at a 0.01 level with a coefficient of −0.845.
Gross Domestic Product (GDP) tracks the well-being of the economy of a nation (Mankiw, 2012). GDP measures total
incomes in the economy and overall expenditure on production (Mankiw, 2012).
Desirable macroeconomic approaches and authority tools are essential for the growth of tourist-oriented nations (Khandaker &
Islam, 2017). Korkmaz (2013) examines the influences of exchange rate on economic development from 2002 to 2011 of nine nations in Europe, chosen by chance to determine the relationship between the variables result showed a connection of exchange rate to economic development with the nine selected nations. A study shows that political steadiness is linked to international tourism at a 5% level with a coefficient of 0.535 (Khandaker & Islam, 2017).
RESEARCH METHODOLOGY
Research ApproachThe study presents a quantitative and econometrics theoretical approach to establish Granger causality and Johansen cointegration of trace and max-Eigenvalue tests. The cointegration is set to estimate the relationship between international tourism receipts, the real exchange rates, and the GDP of The Gambia. This study
determines the influence of international tourism receipts and effective exchange rates on The Gambia economic growth.
According to the past literature review, the most accepted and generally applied methodology is the cointegration and granger causality test based on past researches (Pavlic, Svilokos, & Tolic, 2014). Causality is an association between two variables such that (1) variable is declared to have caused the other variable.
That means an independent variable can cause the dependent variable (Gujarati, 2004). The Johansen cointegration test is applied to know whether two or more time- series have an equilibrium relationship.
Cointegration of two or more time-series proposes a long-run, or equilibrium, relationship between them (Gujarati, 2004).
The time-series data method was applied on annual data placed for testing OLS, cointegration, Granger causality, and short and long-run equilibrium relationship for 1996–2018 in The Gambia. Moreover, stationary can be checked to find out if the time-series includes a unit root (Gujarati, 2004). The augmented Dickey-Fuller
(ADF) or The Phillips-Perron (PP) unit root tests can be used for this purpose of testing stationery (Gujarati, 2004). The purpose of this study is to evaluate and examine the relationship between international tourism receipts and exchange rates as independent variables and gross domestic product (GDP) as a dependent variable.
Method of Analysis
The data was analyzed using EViews 10 version. The analysis relationship between international tourism receipts and effective exchange rates and gross domestic products is on time series data from World Bank data. Econometrics approaches and time series data to estimate the mutual relationship between international tourism receipts, exchange rates, and GDP from 1996 to 2018. The hypothesis-testing procedures examine whether to accept or reject the null hypothesis (Gujarati, 2004).
Data Technique
The predictive model confirmed the fundamental relationship that enables a prediction. Data were attained from the World Bank data internet and estimated by
OLS model GDP= F (INT_RCPT, REER) using EViews. The missing values of the international tourism receipts from 1997- 2002 were evaluated using EViews to interpolate missing data. The decision is based on the probability value (PV) of 5%.
To use a P-value to conclude a hypothesis test, we compare the P-value with α that is the level of significant (5%). (HO = Null Hypothesis & H1 = Alternative Hypothesis).
If P-value < 5%, it rejects H0.
If P-value > 5%, it does not to reject H0. Quantitatively information is obtained regarding the application of theory to the schemes Y= Gross domestic products (GDP), X1= International tourism receipts (INT_RCPT), X2= Real effective exchange rates (REER). A complete econometric model: GDP = β0 + β1INT_RCPT + β2REER + µ
OLS model equation as GDP = F (INT_RCPT, REER)
The ADF test can be estimated as this form below:
ΔYt = β1 + β2t + δYt−1 + αiΔYt−i + εt Co-integrating regression can be applied as this form: GDPt = β1 +β2INT_RCPTt +
𝛽3REERt + µ. The Trace Statistic and Max-Eigenvalue test display the results of the Johansen cointegration test. The VECM evaluates the long-run causality running from INT_RCPT and REER to GDP in The Gambia. The coefficient diagnostics of the Wald test show the short-run causality between the variables.
RESULTS AND DISCUSSION
Description of Research ObjectThe object of this research is international tourism generally. Tourism contributing to the economy draws an attractive tool for improvement. This research aims to evaluate and examine the relationship between international tourism receipts, real effective exchange rate, and gross domestic product. Moreover, to observe and examine whether international tourism receipts or effective exchange rates cause an effect on GDP in The Gambia.
Table 1 Summary of the Variables Result
After executing the method of OLS, international tourism receipt (INT_RCPT) can affect GDP, as the probability is less than < 5%. An increase of tourism receipt by one unit would cause an increase in the gross domestic product (GDP) by 3.64 units on average ceteris paribus. The tourism receipt has positive effects on GDP, and we assumed a rise in tourism receipt might lead to an increase in GDP in The Gambia. This study outcome implies that a relationship between international tourism receipts, and economic growth is found in The Gambia, demonstrating that international tourism receipts can increase GDP and lead to the growth of The Gambia’s economic.This observational
study is consistent with former studies (Belloumi, 2010) ( Paramati, Alam, & Chen, 2016) which claim tourism has a positive influence on the GDP.
In addition, the effective exchange rate (REER) can affect GDP because the probability value is also less than < 5%, an increase of real effective exchange rate by one unit, and the gross domestic product (GDP) to decrease by $289 on average ceteris paribus based on the prediction.
The result means that a rise in exchange rates may cause a fall in GDP, and we assumed that an increase in the exchange rate might negatively affect GDP in The Gambia. According to Özcan's (2020) study, economic growth is significantly
Variable Coefficient Standard
error
t-statistic Probability
Constant 8.60 62418542 13.77702 0.0000
International tourism receipt (INT_RCPT)
3.645 0.472049
7.723171
0.0000
Real effective exchange rate (REER)
-2.891 290784.3 -9.943210 0.0000
R-squared 0.920
Adjusted R2 0.912
Prob(F-statistic) Normality Test
Heteroskedasticity Test Serial Correlation LM Test Q-Statistics Test
White heteroskedasticity test (no cross time)
0.0000 0.4846 0.0669 0.2732 0.503 0.3582
related to real exchange rates depreciation. Khondker, Bidisha, &
Razzaque (2012) exposed that exchange rate actions influenced the total production growth. Indicating long-run consequences of optimism devaluations. Lastly, about 92% of the regression model can be explained by the independent variables, and the other 8% is unexplained. The F- statistic is less than <5% and rejects the null hypothesis. From the results of the F- statistic, it showed that when calculated jointly, international tourism receipt (INT_RCPT) and real exchange rate (REER) have an impact on gross domestic product (GDP) in The Gambia.
In addition, with 92% of R-square, we assumed that the data fit well, we can accept the model. Therefore 1% increase in tourism receipt will raise The Gambia GDP growth by 3.64%. Thus, the exchange rate is negatively associated with GDP in The Gambia; this indicates that an increase in the exchange rate will cause The Gambia GDP to diminish by 2.89%.
Khandaker and Islam (2017) noted that substantial increases in exchange rates could influence the inward global tourism
market. Their study shows that moderate exchange rates are favorable for tourist- centric economic development (Khandaker
& Islam, 2017).
Figure 1 The Graph of Stability Diagnostic
-15 -10 -5 0 5 10 15
2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 CUSUM 5% Significance
The stability of the regression model: GDP
= F (INT_RCPT, REER) to test the stability diagnostic, we run recursive estimates of CUSUM, and it shows that the dependent variable GDP is a stable variable, and the graph shows that the CUSUM line is within the 5% significant level, we accept the model because GDP has stability. The sample indicated in figure1 shows fitting econometric structures. Moreover, the normality test confirms that residuals are distributed normally with a p-value of (0.4846). The heteroskedasticity test
indicated no significance in the model (Prob. F (2,20): 0.0669), and the Breusch- Godfrey Serial Correlation LM Test displays no serial correlations of residual in the model (Prob. F (2,18): 0.2732).
Table 2 Augmented Dicky-Fuller Test UNIT ROOT TEST RESULTS TABLE (ADF)
Null Hypothesis: the variable has a unit root At
Level
GDP INT_RC
PT
REER
With Constant t-
Statistic
0.7354 0.2463 -2.4295 Prob. 0.9898 0.9693 0.1462
n0 n0 n0
With Constant &
Trend
t- Statistic
-3.3034 -1.0059 -2.3484 Prob. 0.0918 0.9225 0.3928
* n0 n0
Without Constant
& Trend
t- Statistic
4.4075 1.3166 -1.8712 Prob. 0.9999 0.9474 0.0598
n0 n0 *
At First Difference
d(GDP) d(INT_
RCPT)
d(REE R)
With Constant t-
Statistic
-4.8763 -4.4449 -2.5075 Prob. 0.0010 0.0024 0.1279
*** *** n0
With Constant &
Trend
t- Statistic
-4.8754 -4.2347 -2.8480 Prob. 0.0048 0.0176 0.1973
*** ** n0
Without Constant
& Trend
t- Statistic
-0.2275 -4.1949 -2.2606 Prob. 0.5894 0.0002 0.0261
n0 *** **
Notes: “a: (*) Significant at the 5%; and (no) Not Significant
b: Lag Length based on SIC c: Probability based on MacKinnon (1996)
one-sided p-values”
The considered time series are international tourism receipt (INT_RCPT), Real effective exchange rate (REER), and
Gross domestic product (GDP), which are for the yearly periods of 1996 to 2018, for an overall of 23 annual observations.
According to Gujarati (2004), if the calculated t value is less than the critical τ level, then the decision is that the variable is not a stationary time series. Since it is in definitive terms, we can label it as a
“stochastic trend” (Gujarati, 2004). Still, if we accept the first differences of the time series variable, we will discover them to be stationary. Thus, the variable is a
“difference-stationary (DS) time series”
(Gujarati, 2004). To verify whether the variables have unit root or stationery, thus the ADF unit root test was applied. The augmented dickey-fuller unit root test on the gross domestic product shows the P- value of 0.98 is more than 5%, so we accept the null hypothesis. It means that GDP has a unit root at the level with constant. Furthermore, the augmented dickey-fuller unit root test on international tourism receipt shows a probability of 0.96 is greater than 5% of the significant level.
In this case, we accept the null hypothesis that tourism receipt at level with constant has a unit root. The ADF unit root test on
REER at level indicates that the probability value of 0.14 is more than the 5% of the significant level; we accept the null hypothesis that the exchange rate has a unit root at level with constant.
Accordingly, the augmented dickey-fuller unit root test on GDP, tourism receipt, and exchange rate at a level, with constant and trend showed that all three variables have unit root because the P- value of 0.92, 0.84, and 0.39 respectively are more than 5% of significant level. The results of the ADF test at the first difference GDP and INT_RCPT with constant and trend the probability values are less than 5%, which means the variables GDP and INT_RCPT are stationary at first difference.
In short, the results from both ADF and PP are alike for the tourism receipt, at first difference with the constant, trend, or without constant, the INT_RCPT probability values are (0.00), which is less than 5% we conclude that INT_RCPT is stationary at first difference. Lastly, the (PP) unit root test on the exchange rate displays a similar case with ADF unit root test results on exchange rate both show
stationarity without constant and trend at first difference.
Table 3 Results of Johansen’s cointegrating relationships Hypothesized
No. of CE(s) Trace Test Max-Eigen P-values
None At most 1 At most 2
Lag 3
Critical Value 5%
0.0000* 0.0000*
0.2213 0.1617
0.1529 0.1529
Table 3 presents the results from the Johansen test 2 at first difference with lag 3. The result means a stationary around the deterministic trend. Generally speaking, when the trend in a time series is totally predictable, it is called a deterministic trend, and when it is not foreseeable, it is called a stochastic trend. Making the description formal, we considered the resulting model of the time series as: Yt = β1 + β2t + β3Yt−1 + ut: GDPt = β1 + INT_RCPTt + REERt + GDPt−1 + ut. The trace statistic shows one (1) cointegrated relationship at the 0.05 level. We can see at none* the probability is less than 5%, we reject the null hypothesis, which means there is cointegration between the variables. At most*1 and most*2 p-values
are more than 5% of the significant level.
The results from Johansen trace statistic conclude that there are cointegration relationships among GDP, tourism receipts, and exchange rates at first difference with lag 3. The Max-eigenvalue test shows one (1) cointegrated at the 0.05 level. From Table 15, we can see none* p- value is less than 5%, we accept the null hypothesis, which assumed cointegrated existed between the variables. Therefore, the variables are cointegrated. If the trace or max-eigenvalue tests are more than the critical value, we reject the null hypothesis stated by past literature research. When the null hypothesis is accepted, the final result is that there are no cointegrated relationships among the variables of the VEC model, and the test concluded. In conclusion, the Johansen test 2 indicated one cointegration between international tourism receipts, effective exchange rates, and GDP. The findings are consistent with Belloumi's (2010) study, which argued a cointegration association between economic growth and tourism.
Table 4 Testing the Number of Cointegration Between Variables Selected (0.05 level*) Number of Cointegrating
Relations by Model Lags interval: 3
Data Trend :
None None Linear Linear Quadrati c Test
Type
No Intercept
Intercept Intercept Intercept Intercept No Trend No Trend No Trend Trend Trend
Trace 1 1 1 2 2
Max- Eig
1 1 1 2 2
Table 4 includes the results summary number of cointegration between chosen variables. The majority of the models implied one cointegration relationship. In model one (1) trace statistics and Max-Eig with no intercept, no trend showed one cointegration relationship. Model two of trace and Max- Eig showed the same result one cointegration relationship. The Trace and Max-eigenvalue indicated two cointegration on the fourth model with a linear intercept trend. Moreover, the third and fifth models both trace statistics and Max-Eig showed two cointegration relationships.
Table 5 VECM of OLS: Long-Run Equilibrium
Coefficient Std. Error t-Statistic Prob.
C(1) -0.009964 0.170748 -0.058356 0.9539 C(2) -0.712121 0.465451 -1.529960 0.1391 C(3) -0.669908 0.554627 -1.207853 0.2389 C(4) 0.268952 0.599047 0.448966 0.6575 C(5) -1.104106 1.037203 -1.064503 0.2977 C(6) -0.243765 1.106896 -0.220223 0.8276 C(7) 1.083639 0.970543 1.116529 0.2752 C(8) 701070.5 1194725. 0.586805 0.5628 C(9) -12232.07 1127085. -0.010853 0.9914 C(10) 412682.9 936770.8 0.440538 0.6635 C(11) 65596453 46903847 1.398530 0.1747 C(12) 0.280542 0.055370 5.066718 0.0000 C(13) -0.621904 0.150935 -4.120352 0.0004 C(14) -0.725649 0.179853 -4.034687 0.0005 C(15) -0.728180 0.194257 -3.748541 0.0010 C(16) -0.720617 0.336341 -2.142522 0.0425 C(17) -2.042589 0.358941 -5.690605 0.0000 C(18) -1.791968 0.314724 -5.693771 0.0000 C(19) 228506.3 387421.2 0.589814 0.5608 C(20) 1236929. 365487.4 3.384328 0.0025 C(21) -51203.10 303772.8 -0.168557 0.8676 C(22) 79968787 15209818 5.257708 0.0000 C(23) 2.56E-08 7.71E-08 0.331413 0.7432 C(24) 1.84E-08 2.10E-07 0.087512 0.9310 C(25) -2.01E-07 2.51E-07 -0.800273 0.4314 C(26) -1.91E-07 2.71E-07 -0.707106 0.4863 C(27) 2.64E-07 4.69E-07 0.563048 0.5786 C(28) -2.32E-07 5.00E-07 -0.463148 0.6474 C(29) -3.78E-07 4.38E-07 -0.862729 0.3968 C(30) 0.415032 0.539753 0.768929 0.4494 C(31) 0.296968 0.509195 0.583210 0.5652 C(32) -0.318771 0.423215 -0.753214 0.4586 C(33) 7.782511 21.19023 0.367269 0.7166
The specified variables in the model have cointegration in the first difference. The VECM by one cointegrating association and three lags in every equation evaluated. The VECM lets the long-run conduct of the endogenous variables meet their long-run equilibrium association and enable a wide range of short-run dynamic forces.
The coefficient of the residual or error term in Table 5 (C (1) = -0.009964) that estimates the speediness of variation to long-run stability is negative but insignificant (p = 0.9539), which is inappropriate. According to a previous study, if the C(1) coefficient is negative in sign and the probability value is less than 5% and significant. It means a long-run causality run from the independent variables toward the dependent variables.
The result from Table 19.4 shows that the C(1) is -0.009964 with a probability value of 0.96, which is more than 0.05 level. In this case, there is no long-run causality running from international tourism receipt (INT_RCPT) and effective exchange rate (REER) to gross domestic product (GDP).
The previous study of Wu and Wu (2018) argued a causal association between tourism receipts and economic growth.
Indicating extreme travel defense and a decrease in travel consumption might be led to pressure on economic activities (Wu
& Wu, 2018). This finding would then not support the results of the study (Pavlic,
Svilokos, & Tolic, 2014), suggesting that a long-run causality existed between tourist arrivals, the effective exchange rate, openness of the economy, and GDP.
Table 6 Short-Run Causality Results
There is no short-run causality from the model D(GDP): C(5)*D(INT_RCPT(-1)) +
C(6)*D(INT_RCPT (-2)) +
C(7)*D(INT_RCPT(-3)), which is C(5) = C(6) = C(7) =0. The probability value is 0.18, more than 5%, and it is insignificant.
It means there is no short-run causality from international tourism receipts towards GDP in The Gambia. The null hypothesis:
C(8) = C(9) = C(10) =0 from the Wald test 1 shows no short-run causality running
from REER to GDP in The Gambia. The null hypothesis: C(13)=C(14)=C(15)=0, which shows that a p-value of 0.00 is less than 5% at level, meaning it rejects the null hypothesis. Therefore, there is short-run causality running from GDP towards INT- RCPT. Furthermore, the Null Hypothesis:
C(19)=C(20)=C(21)=0, indicated a probability value of 0.00 less than 0.05 at the level, and it is significant. It means there is short-run causality from REER to Null Hypothesis: C(5) = C(6) = C(7) =0
Test Statistic Value df Probability Chi-square 4.823397 3 0.1852
Null Hypothesis: C(8) = C(9) = C(10)=0
Test Statistic Value df Probability Chi-square 0.622967 3 0.8912
Null Hypothesis: C(13) = C(14) = C(15)=0
Test Statistic Value df Probability
Chi-square 21.05660 3 0.0001 Null Hypothesis: C(19 )= C(20) = C(21)=0
Test Statistic Value df Probability
Chi-square 24.66666 3 0.0000 Null Hypothesis: C(24) = C(25) = C(26)=0
Test Statistic Value df Probability Chi-square 2.486567 3 0.4777
Null Hypothesis: C(27) = C(28)=C(29)=0
Test Statistic Value df Probability
Chi-square 1.282839 3 0.7332
INT_RCPT. The third model on D(REER) showed no short-run causality. The null:
C(24)=C(25)=C(26)=0 from the Wald test 3 indicated no short-run connection since its probability is more than 5% and insignificant. Meaning there is no short-run causality running from GDP towards REER. In addition, there is no short-run causality from INT_RCPT to REER because the p-value is 0.73 more than 5%
at the level, and it is insignificant. The short-run causality test findings mean a significant causal relationship from effective exchange rates and GDP to tourism receipts in The Gambia.
Table 7 Granger Causality Results
The empirical results for The Gambia economy support the prevailed opinion in the literature. Tourism inflow has a significant beneficial influence on the overall national economy due to a substantial impact on foreign currencies, government revenues, domestic incomes, and employment generation. This study outcomes propose that international tourism, exchange rates, and GDP cause one another in the short-run behavior in the Gambia.
Statistically, with granger causality, simply two variables are estimated at a period.
According to a report, the AD-HOC Direction of
Causality
Obs Lag Probability Value
Decision on
Hypothesis Testing
Conclusion Granger Cause? / Do not Granger Cause?
Type of Causality
INTRCPT GDP 20 3 0.2849 Accept Does not Cause No Causal Relationship GDP INT_RCPT 20 3 0.3032 Accept Does not Cause No Causal
Relationship REER GDP 20 3 0.5045 Accept Does not Cause No Causal
Relationship GDP REER 20 3 0.2844 Accept Does not Cause No Causal
Relationship REER INT_RCPT 20 3 0.1247 Accept Does not Cause No Causal
Relationship INT_RCPT REER 20 3 0.6114 Accept Does not Cause No Causal
Relationship
selection technique for the lag is more applicable than other approaches in Granger causality, Jones (1989). For example, the Null hypothesis does not granger cause the predicted variable; If the probability value is more than the critical level of 5%, accept the Null hypothesis. It indicates no Causality. If the probability value is less than the critical level of 5%, reject the Null hypothesis. It suggests causality exists.
To determine and conclude, we selected three lags based on the lag selection criteria, and the results are in Table 7, granger causality tests two. It shows that in the first model, GDP does not cause international tourism receipts because the probability value is more than 5%, and it accepts the null hypothesis of 0.30 p- values. In addition, INT-RCPT does not cause GDP at a 0.28 level of more than 5%. Hence, the second and third models show the same outcome with a p-value of more than 5% levels. It means international tourism receipts and exchange rates do not cause each other.
In conclusion, there is no causal relationship between INT_RCPT, REER,
and GDP in The Gambia. The study evidence shows that variables do not cause each other with the lag selected criteria. Therefore, our findings are not consistent with Belloumi's (2010) study, which argued that tourism has a positive influence on gross domestic product growth unidirectionally.
CONCLUSIONS
The results in this study approved the hypothesis that international tourism led to economic development. As assumed, the incomes produced from international tourism have a positive influence on Gambia's economic growth. The problems formed and the outcomes of the research study are as follows:
Firstly, the results indicated that a rise in the tourism receipt might have positive impacts on the gross domestic product in The Gambia. The tourism receipt is positively related to GDP at a 5% level with a coefficient of 3.64. It means an increase in international tourism may lead to a rise in economic growth in The Gambia. Secondly, the results showed an increase in the exchange rate might lead to
a decrease in the gross domestic product in The Gambia. The exchange rate is negatively related to GDP at a 5% level with a coefficient of -2.89. International tourism receipt, exchange rate, and GDP in The Gambia are cointegrated. There was no long-run causality running from INT_RCPT and REER to GDP in The Gambia. Moreover, there is a short-run causality from the exchange rate and GDP toward international tourism receipts in The Gambia. The results from the Granger causality test showed that all variables do not cause each other.
The conclusion is that international tourism is significant to countries' economic growth. There is no causality relationship between INT_RCPT, REER, and GDP in The Gambia. The exchange rate is negatively associated with GDP in The Gambia; this indicates that an increase in the exchange rate will cause The Gambia GDP to diminish by 2.89%.
Suggestions
This study analysis showed that international tourism receipt has positive effects on GDP in The Gambia.
1. Therefore, the next researcher can focus on measures that support international tourism to meet the demands of the tourism industry.
2. In the following research, the writer suggests examining some aspects that can assist in international tourism demand and stability of the exchange rate on the economic growth.
Finally, an observational study like the one presented in this study is acceptable for any nation that focuses on the tourism industry.
Bibliography
Paramati, S. R., Alam, M. S., & Chen, C.-F.
(2016). The Effects of Tourism on Economic Growth and CO2 Emissions: A Comparision Bewteen Developed and Developing Economies. Journal of Travel Research.
African Development Bank, A. (2018). African Economic Outlook: Gambia Economic Outlook. Retrieved May 2012, from African Development Bank Group:
https://www.afdb.org/en/countries/
west-africa/gambia/gambia- economic-outlook
Belloumi, M. (2010, Febuary 12). The Relationship between Tourism
Receipts, Real Effective Exchange Rate and Economic Growth in Tunisia.
INTERNATIONAL JOURNAL OF TOURISM RESEARCH, 12(5), 550-560.
Case, K. E., Fair, R. C., & Oster, S. M. (2012).
Principles of Economics . United States of America: Pearson Education.
Global business knowledge, G. E. (2021). The Gambia: Economy. Retrieved October 5, 2021, from Global EDGE:
https://globaledge.msu.edu/countries /the-gambia/economy
Gramatinikovski, S., Milenkovski, A., &
Blazheska, D. (2016, October 4). THE IMPACT OF THE INTERNATIONAL TOURISM RECEIPTS ON GDP: THE CASE OF REPUBLIC OF MACEDONIA.
220 IInternational Journal of Academic Research in Accounting, Finance and Management Sciences, 6, 220-225.
Gujarati, D. N. (2004). BASIC ECONOMETRICS.
United States: McGraw-HiII/lrwin.
Khandaker, S., & Islam, S. Z. (2017, November 30). International Tourism Demand and Macroeconomic Factors.
International Journal of Economics and Financial Issues, 7(5), 389-393.
Khondker, B. H., Bidisha, S. H., & Razzaque, M.
A. ( 2012, October). The Exchange Rate and Economic Growth: An Empirical Assessment on Bangladesh.
Journal of South Asian Development, 12, 108-118.
Korkmaz, S. (2013, October). THE EFFECT OF EXCHANGE RATE ON ECONOMIC GROWTH. Conference Paper.
Loum, B. (2020, October 30). TOURISM AND COVID 19 IN THE GAMBIA; POLICY IMPACTS AND IMPLICATIONS IN TRANSITIONING TO THE RECOVERY PHASE OF THE PANDEMIC. The Epidemiology Hour (pp. 1-9). Banjul:
GAMBIA TOURISM BOARD.
Mankiw, G. N. (2012). MACROECONOMICS.
New York: Worth Publishers . Mathieson, A., & Wall, G. (1982). Tourism:
Economic, Physical, and Social Impacts. London: Longman.
Mishkin S.F, Eakins G.S. (2014). Financial Markets and Institutions. United States: Pearson.
Özcan, K. (2020, June). Influence of Exchange Rate on the Economic Growth in the Turkish Economy. Financial Asset and Investing, 21-34.
Pavlic, I., Svilokos, T., & Tolic, M. S. (2014, January 13). Tourism, Real Effective Exchange Rate and Economic Growth:
Empirical. International Journal of Tourism Research, 282–291.
Samirka, M., & Samirkaş, M. C. (2015). The Impact of Exchange Rate on Tourism Industry:The Case of Turkey.
Handbook of Research on Global Hospitality and Tourism Management , 107-118.
Sanneh, A. (2018). The Gambia Trade Policy 2018-2022. Banjul.
Szulczyk, K. R. (2013). Money, Banking, and International Finance (2 ed.). United
States of America: CreateSpace Independent Publising Platform.
The Gambia Bureau of Statistics, (. (2012).
THE GAMBIA
TOURISM,TRANSPORTAND
COMMUNICATION SUMMARY 2008- 2012. Kanifing Institutional Layout, The Gambia Bureau of Statistics (GBOS), Banjul.
The Gambia Central Bank, C. (2018). Central Bank of The Gambia Annual Report.
Banjul: Central Bank of The Gambia.
Retrieved from
https://www.cbg.gm/financial- statements
The Global Economy.com, B. a. (2021).
Gambia: Economic growth. Retrieved October 3, 2021, from The Global EConomy.com:
https://www.theglobaleconomy.com/
Gambia/Economic_growth/
The Global Economy.com, B. a. (2021).
Gambia: International tourism revenue, percent of GDP. Retrieved October 5, 2021, from The Global Economy.com:
https://www.theglobaleconomy.com/
Gambia/international_tourism_reven ue_to_GDP/
The Heritage Foundation, 2. I. (2021). 2021 Index of Economic Freedom:The Gambia. Retrieved May 17, 2021, from The Heritage Foundation:
https://www.heritage.org/index/coun try/gambia
Wall, R., Willis, K. G., & Roman, V. (2009, January). Management of Archaeological and Heritage
Attractions : . Tourism, 505, 487-505.
World Bank, W. D. (2021). World
Development Indicators: International tourism, receipts (current US$) - Gambia, The. Retrieved March 15, 2021, from The World Bank Group:
https://data.worldbank.org/indicator/
ST.INT.RCPT.CD?locations=GM World Tourism Organization, U. (2021). The
first global dashboard for tourism insights: Global and Regional Tourism Performance. Retrieved May 12, 2021, from UNWTO TOURISM DATA DASHBOARD:
https://www.unwto.org/unwto- tourism-dashboard
Wu, T. P., & Wu, H. C. (2018). The Influence of International Tourism Receipts on Economic Development: Evidence from China’s 31 Major Regions. Journal of Travel Research, 57(7), 871-882.
