Testing the tourism - Led growth hypothesis for Vietnam

05 level 
* denotes rejection of the hypothesis at the 
0.05 level 
**MacKinnon-Haug-Michelis (1999) p-values 
Source: Author’s survey, 2018. 
AIC (Akaie Information Criterion), SC 
(Schwarz Criterion) and LR (Likelihod Ratio) 
test are used to select the number of lags 
required in the cointegration test. The option 
lag is 2 (Table 7). 
Table 7. VAR Lag Order Selection Criteria 
Lag LogL LR FPE AIC SC HQ 
0 13.68035 NA 8.83e-05 -0.821565 -0.676400 -0.779763 
1 113.5650 169.0356 8.18e-08 -7.812693 -7.232033 -7.645483 
2 130.9372 25.39018* 4.44e-08* -8.456710* -7.440555* -8.164094* 
4.4. VECM (Vector Error correction Model) 
VECM is estimated to model the long run 
causality and short run dynamics. The aim of 
VECM model is to indicate the speed of 
adjustment from the short run equilibrium to 
the long run equilibrium state. The greater the 
coefficient of the parameter the higher the 
speed of adjustment of the model from short - 
run to long-run. VECM is a restricted VAR 
designed for use with non-stationary series that 
are known to be cointegrated. Once the 
equilibrium conditions are imposed, the VECM 
describes how the examined model is adjusting 
in each period towards its long run equilibrium 
state. Since the variables are supposed to be 
cointegration, then in the short run, deviations 
from this long run equilibrium will feedback on 
the changes in the dependent variables in order 
to force their movements towards the long run 
equilibrium state. The cointegration term is 
known as the error correction term since the 
deviation from long run equilibrium is 
corrected gradually through a series of partial 
short run adjustments. The size and statistical 
significance of the coefficient of the ECM 
measures the tendency of each variable to 
return to the equilibrium. A significant 
coefficient implies that past equilibrium errors 
play a role in determining the current outcomes. 
Considering our base equation (1), the 
VECM model is specified as follows: 
ALTRt = ao + a1 ALTRt-1 + a2ALGDPt-1 + 
a3ALEXR t -1 + pi ECM (-1) + e t (2) 
P. N. Bao Hoang, Le Duc Toan / Tạp chí Khoa học và Công nghệ Đại học Duy Tân 02(45) (2021) 141-151 148 
Where A is the first difference operator, 
ECM (-l) is the error correction term, et is the 
error term, pi captures the long run impact. The 
error correction coefficient pi is very important 
in this error correction estimation as the greater 
coefficient indicates higher speed of adjustment 
of the model from the short run to the long run. 
Table 8. Model D(LTR) 
Variable Coefficient Std.Error t-statistics Prob 
ECM(-1) 0.638838 0.26622 2.39966 0.0281 
D(LGDP(-1)) -0.541160 0.58650 -0.92269 0.3691 
D(LGDP(-2)) 0.994028 0.52344 1.89903 0.0747 
D(LEXR(-1)) -0.479523 0.89535 -0.53557 0.5992 
D(LEXR(-2)) 1.105575 0.48182 2.29459 0.0348 
D(LTR(-1)) 0.431016 0.25358 1.69975 0.1074 
D(LTR(-2)) -0.079137 0.21781 -0.36334 0.7208 
C -0.025138 0.08789 -0.28601 0.7783 
R -Squared = 0.380413, Adjusted R-squared =0.1252. 
(Source: Author’s survey, 2018) 
ECM (-1) = 0.6388 and p-value = 0.0281. 
These coefficients are statistically significant, 
there is the long - run relationship between 
LTR and other variables (LGDP, LEXR). 
Table 9 shows LM test, this test is used to 
inspect whether there is serial correlation or not 
between three variables. F=1.13 < F(0.05, 3-1, 15) = 
3.682. The results have suggested the acceptance 
of null hypothesis. There is no serial correlation, it 
means that the disturbance term relating to any 
variable has not been influenced by the disturbance 
term relating to another variable. 
Table 9. Breusch-Godfrey Serial Correlation LM Test 
F-statistic 1.132491 Prob. F(2,15) 0.3483 
Obs*R-squared 3.279733 Prob. Chi-Square(2) 0.1940 
The results in Table 10 show the Pairwise 
Granger causality test among the variables 
analyzed. In the short -run, the results indicate that: 
- There is bidirectional causality 
relationships between GDP and TR, 
between TR and EXR 
- There is no causality relation between 
EXR and GDP 
4.5. Causality test 
Table 10. Pairwise Granger Causality Tests 
Null Hypothesis Obs F-Statistic Prob. Decision 
LEXR does not Granger Cause LGDP 
LGDP does not Granger Cause LEXR 
26 3.70978 
13.2584 
0.0417 
0.0002 
Accept 
Accept 
LTR does not Granger Cause LGDP 
LGDP does not Granger Cause LTR 
26 2.38968 
1.29338 
0.1161 
0.2953 
Reject 
Reject 
LTR does not Granger Cause LEXR 
LEXR does not Granger Cause LTR 
26 1.15586 
1.74240 
0.3340 
0.1995 
Reject 
Reject 
P. N. Bao Hoang, Le Duc Toan / Tạp chí Khoa học và Công nghệ Đại học Duy Tân 02(45) (2021) 141-151 149 
4.6. Variance Decomposition 
We employ a twice- year forecasting time 
horizon and observed the relevance of the 
variables over time horizon. Table 10 gives the 
fraction of the forecast error vector variance 
that is attributed to its own innovation and to 
innovations in other variables. The own shocks 
of LTR ranged from 70.75% to 26.50%. 
In the third period, 43.54% of the total 
change on the variance of LTR is due to LGDP. 
This percent increase gradually over the time 
and even in the twice periods it gets 62.25%. 
The salient feature is that predominant source 
of variation in LTR are LGDP (Table 11). In 
case of LGDP, we see that in the fifth periods 
88.13% of the total change on the variance is 
due to LGDP and this percentage reduces 
smartly in the next period, getting 85.22% in 
the twice period (Table 12). 
Table 11. Variance Decomposition of LTR 
Variance Decomposition of LTR: Period S.E. LGDP LEXR LTR 
1 0.134692 29.24157 0.005826 70.75261 
2 0.178498 38.41681 1.701710 59.88148 
3 0.199946 43.54109 4.705508 51.75340 
4 0.212796 46.50213 7.319267 46.17860 
5 0.222456 48.79855 8.943363 42.25809 
6 0.231269 51.11795 9.760876 39.12118 
7 0.240165 53.58588 10.12877 36.28536 
8 0.249289 55.99778 10.32051 33.68171 
9 0.258356 58.10711 10.49505 31.39785 
10 0.266976 59.81430 10.71443 29.47127 
11 0.274904 61.16369 10.97304 27.86326 
12 0.282108 62.25580 11.23657 26.50763 
Cholesky Ordering: LGDP LEXR LTR 
Table 12. Variance Decomposition of LGDP 
Variance Decomposition of LGDP: Period S.E. LGDP LEXR LTR 
1 0.054224 100.0000 0.000000 0.000000 
2 0.097072 98.23965 1.032134 0.728213 
3 0.132000 94.47022 3.109630 2.420146 
4 0.158235 90.76487 5.609362 3.625771 
5 0.177058 88.13016 7.966359 3.903485 
6 0.190897 86.54877 9.794103 3.657123 
7 0.202023 85.70592 10.98296 3.311124 
8 0.211922 85.33804 11.64877 3.013187 
9 0.221324 85.24537 11.98839 2.766241 
10 0.230445 85.25882 12.17423 2.566947 
11 0.239219 85.26410 12.31771 2.418192 
12 0.247496 85.22037 12.46974 2.309886 
Cholesky Ordering: LGDP LEXR LTR 
P. N. Bao Hoang, Le Duc Toan / Tạp chí Khoa học và Công nghệ Đại học Duy Tân 02(45) (2021) 141-151 150 
5. Conclusions and recommendations 
Using VECM, this study includes EXR as a 
third variable and examines the relationship 
between tourism receipts and economic growth 
for Vietnam in 1990-2017. 
- The results point out that GDP, TR and 
EXR are cointegrated, implying a long -run 
relationship between three variables. The value 
of ECM (-1) = 0.6388, this shows that speed of 
adjustment toward long run equilibrium is 
about 1.5 year. 
- In the short run, the results also reveal that 
there is bidirectional causality relationships 
between GDP and TR, between TR and EXR. 
- Tourism industry has contributed to 
solving employments, brought foreign 
currencies and the mentioned -above results 
give us to conclude that tourist -led growth 
hypothesis (TLG) is accepted in the case of 
Vietnam in the period 1990-2017. 
This finding is in line with the research of 
Eugenio -Martin et al, [8], Katircioglu [15], 
Husein et al, [14]. 
The study also suggest some 
recommendations to making-policy officers and 
manager as: 
First, Implementing solutions to improve the 
business environment and boost the national 
competitiveness. 
- State management need to be changed 
from pre-clearance inspections to post - 
clearance inspections, the overlapping 
management of a certain product will be 
restricted, and the ratio of import shipments 
undergoing specialized inspection in the 
customs clearance process will be reduced. 
- To cut logistics costs as well as enforce 
work discipline. Civil servants failing to 
improve administrative procedure and facilitate 
investment and business activities, and those 
with signs of abuse of authority for personal 
gains will be replaced. 
Second, to attract investment capital into 
improving socio-economic infrastructure. 
Infrastructure refers to ports, expressways 
and connections key business locations. 
Vietnam should utilize resources for 
infrastructure planning and development, clear 
bottlenecks for growth, and enhance 
management capacity and policy transparency 
to boost disbursement of investment fund, 
especially for public investment, in addition to 
accelerating the digitalization process in 
aftermath of the pandemic. 
Third, Training high-quality human resource 
It is in one necessary to be aware of 
strategies and demands of high-tech groups, to 
train tourist human resource for getting high 
level in service qualities. Vietnam should focus 
on education development, ensure sufficient 
high-quality manpower for the next phase of 
development and select investment projects 
with high knowledge content and technology, 
as it is seen as the biggest ever opportunity to 
attract foreign investment, not only from South 
Korea, Japan and some other countries but also 
possibly from big US and EU corporations. 
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