4.0 stat. P-Value 1% 5% 10% real

4.0 Data Analysis and Results Discussion

Table
1: Augmented Dickey-Fuller (ADF) test for Unit root

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Variable

Level

First Diff.

Second Diff

ADF Critical values

 

test stat.

P-Value

test stat.

P-Value

test stat.

P-Value

1%

5%

10%

real GDP

-1.9271

0.6034

-3.0836

0.1377

-4.8618

0.006*

-4.49

-3.65

-3.26

Investment

-4.9801

0.003*

-8.1164

0.000*

-10.8814

0.000*

 

 

 

Export

-2.4299

0.3526

-5.0078

0.004*

-7.0237

0.000*

 

 

 

Tourism

-3.1441

0.1281

-2.5864

0.2891

-4.9556

0.004*

 

 

 

Note:
* indicates significance at 5% level

Presented
in table 1 are results of the ADF test which are obtained from the estimation
of equation 3.1. As it can be observed from the table, apart from Investment,
all other variables were not stationary at their level values. This mean that
they contain a unit root since their respective test statistic values are less
than their ADF critical values at 5% level. 
This suggests that the variables must be differenced. After first
differencing, only Export, and Investment were stationary, but real GDP and Tourism
were not. Thus, there is need to take the second difference for all the
variables. At second, difference, all the variables become stationary, and
hence, there is no evidence of a unit root in all the variables after second
differencing. Therefore, all the variables shall be considered to be stationary
at second difference, and hence, be integrated of the order (2). This is
required because the Johansen test of cointegration demands all the variables
to be of the same order. 

As
evident from the above, Ordinary Least Squares (OLS) method cannot be applied
directly in presence of a unit root. This will then require some adjustments be
done. However, pre-estimation tests proved the presence of autocorrelation in all
the variables. To mitigate the issue, the Generalized Least squares (GLS) was
applied as explained by Gujarati (2016). After application of the GLS method,
the regression results obtained are tabulated in table 2.

Table
2: Regression Results

Variable

Coefficient

Std. Error

t-Statistic

Prob.  

Tour

0.400208

0.064009

6.252339

0.0000

Expo

0.633826

0.033902

18.69607

0.0000

Inves

-0.011240

0.016829

-0.667896

0.5132

R-squared

0.821333

Adjusted R-squared

0.800314

 

This
work seeks to investigate the impact of tourism on the economic growth of
Cameroon. The above estimated results were obtained from the estimation of
equation 3.2. The results are presented in the form of an equation as shown by
equation 4.1   

       . . . 4.1

Equation 4.1 provides some interesting
outcomes. At the onset, except for investment, the other two variables (tourism
and export) have correct expected sign (i.e. positively related with real GDP).
Starting with the coefficient of tourism which is 0.40. this indicates that,
keeping the other variables constant (i.e. investment and exports), an increase
in the number of tourists (by 1%) will, on the average increase the real GDP of
Cameroon by 0.40%. This relationship is statistically significant at even 1%
level as shown by the probability value.

Second, the coefficient of exports is
0.63, implying that if all the other variables (i.e. tourism and investment)
are to be kept constant, an increase in exports of goods and services in Cameroon
by 1%, will on the average, increase Cameroonian real GDP by 0.63%, and this
estimation is statistically significant at 5% level. But observe that the
coefficient of export is greater than the coefficient of tourism (i.e. 0.63
> 0.40). This clearly discloses that in Cameroon, exports have more
significant influence on real GDP than tourism.

Third, the coefficient of investment is –
0.01. this figure implies that, keeping other variables (tourism and exports),
an increase in the flow of foreign direct investment into the Cameroonian
economy by 1% will, on the average, decrease the real GDP of Cameroon by 0.01%.
But this estimation is statistically significant as probability values show.
However, it was not this work’s prior expectation that the coefficient of
investment has a negative sign. This is because an increase in the flow of
investment is always expected to have positive significant impact on the real
GDP of not only Cameroon, but other countries as well. Therefore, the negative
sign seen on the coefficient of investment is highly misleading.

From table 2, the value of 0.8213 has been
reported for R-squared, and 0.8003 for the adjusted R-squared. The value of
R-squared imply that about 82.13% of the total variation in the real GDP of
Cameroon is explained by the joint influence of the three variables in the
model. And, as for adjusted R-squared, it implies that after adjusting for
other variables that influence real GDP of Cameroon which are not included in
the model, the three explanatory variables still explain about 80% of the
variations in the real GDP of Cameroon.

The post-estimation of the results in
table 2 reveal that the model is free from serial correlation (using Breusch-Godfrey Serial Correlation LM Test) and
heteroscedasticity (using Breusch-Pagan-Godfrey
test)
problems. Using the cumulative sum (CUSUM) test, the model is found adequate,
and the residuals were also normally distributed as shown by the results of
normality test. Details of these tests can be found in the appendix.

However, to investigate whether there is
long-run relationship among the variables of this study, the Johansen test of
cointegration was conducted and the results obtained are tabulated in table 3.

 

Table
3: Johansen test of cointegration

Hypothesized
No. of CE(s)

Eigenvalue

Trace
Statistic

0.05
Critical Value

Prob.**

None *

 0.944435

 98.29087

 47.85613

 0.0000

At most 1 *

 0.806576

 49.15756

 29.79707

 0.0001

At most 2 *

 0.608985

 21.22874

 15.49471

 0.0061

At most 3 *

 0.266362

 5.265575

 3.841466

 0.0217

 Trace test
indicates 4 cointegrating eqn(s) at the 0.05 level

 * denotes
rejection of the hypothesis at the 0.05 level

 

Results
of table 3 estimate the long-run cointegrating vectors for all the variables
using the Trace statistics. The decision rule is stated: Reject the null
hypothesis if the value of Trace statistics is greater than its critical values
at 5%. The decision here, with regard to results of table 3, is to reject the
respective null hypotheses at 5% level. This is because in all the hypothesized
number of cointegrating equations, respective values of the test statistics are
all greater than their corresponding critical values at 5% level. Therefore,
the all null hypotheses stating that there is no cointegrating equations will
be rejected. Rejection of these null hypotheses provide some evidences that
there is cointegration among the variables (real GDP, tourism, exports, and
investment). With this outcome, it thus clear that in the Cameroonian economy,
there is long-run relationship among all the variables controlled in this
study.

4.1 Result Discussion

One
principal factor to emphasize here is the coefficients of tourism, export, and
investments reported in equation 4.1 (or table 2). Because the variables were
initially converted into natural logs prior to the estimation, the resulting
coefficients are therefore known as elasticity (Gujarati, 2016). Impliedly,
therefore equation 4.1 says that the elasticity of impact of tourism, exports,
and investment on the real GDP of Cameroon are 0.40, 0.63, and – 0.01
respectively. According to Wooldridge (2013) the resultant elasticities can
also be interpreted as the multiplier effect. Thus, 0.40 is multiplier effect
of tourism on real GDP of Cameroon. 0.63 is the exports multiplier effect on
real GDP, while – 0.01 is the investment multiplier effect. This negative value
reported on investment is rather absurd and misleading as already explained.
The possible cause of this could be inconsistency in the way the variable is
measured (i.e. foreign direct investment as a percentage of GDP). Because
frequent fluctuations in the GDP level will also mean the same fluctuation in
the level of investment.

However, the percentage reported on the
R-squared and adjusted R-squared is unreasonably high, because in real world,
thousands of variables (like infrastructure, government policy, imports,
exchange rate, taxes, etc.) work together to influence the economic performance
of an economy not just those used in this work.

Also, the results of Johansen
cointegration test are in line with prior expectation because in a developing
economy like Cameroon, one would expect at least export, and investment to have
long-run relationship with real GDP. Nonetheless, the results also are in line
with the finding of Khalil, Khan and Waliullah (2007), Saayman & Saayman, (2012), Makochekanwa
(2013), Šimundi?, Kuliš, and Šeri? (2016) and Bezuidenhout & Grater, (2016). 

From the results of table 2, we arrive at
the first objective of this research. Thus, Tourism has significant impact of about
0.4% on real of Cameroon. And from the results of table 3, we achieve the
second objective of this work. Therefore, there is long-run relationship
between tourism and real GDP Cameroon. Overall, the empirical results of this
work are most likely in support of the economic theory reviewed in the
literature review section.

Finally, there are certain limitations
that worth emphasizing from the results of this work. One, there is scanty of
data on the variables used in this research, especially on tourism. Thus,
because the data is small, the results of this work may not be robust. Two, the
econometric models used in this work need further extension. For example, as
the Johansen test of cointegration indicates the presence of long-run
relationship among the study variables, there is need to go further to identify
how can this long-run relationship be established and at what speed. This can
be done using the vector error correction model (VECM). Finally, there is the
need to go beyond the VECM to find out what will be response of Cameroon real
GDP to a one standard deviation shock in investment, tourism, or investment.
This can be done using the impulse response function, and/or variance
decomposition tests.

 

5.0 Summary and Conclusion

5.1 Summary

In this work, effort has been made to
investigate the impact of tourism on economic growth of Cameroon, while
controlling for other variables. Both theoretical and empirical literature were
reviewed on the impact of tourism on economic growth of countries. The
methodology used in the conduct of this work involves the application of the
Ordinary Least Squares (OLS), and the Johansen test of cointegration. First,
the times series behaviors of the variables were examined using the ADF test
for unit root. All the variables were considered to be stationary at second
difference. And with the detection of serial correlation problem, the GLS
method was developed before finally running the OLS regression. From the
regression results, it has reported in this work that tourism and exports have
significant influence on the real GDP of Cameroon, while investment has not.
From the result of Johansen test of cointegration, this work identifies
evidence of long-run relationship among the controlled variables in this work.

 

5.2
Conclusion

From the empirical analysis of this work,
this research draws two conclusions: Tourism has significant impact on real of
Cameroon. And, there is long-run relationship between tourism and real GDP
Cameroon. However, the overall empirical results of this work are most likely
in support of the economic theory reviewed in the literature review section.

Policy
implication

Based
on the conclusion drawn, it can be suggested here that since tourism has
significant impact on the economy of Cameroon, the Cameroonian economic policy
makers should try to make and implement policies that promote tourism.
Furthermore, the policy makers should also make sure that policies that
strengthen international trade and promote exports are at the fore front of their
agenda because, exports have significant positive impact on the economy of
Cameroon.