Roles of Institutional Quality on the Relationship between Tourism and Economic Development in Malaysia

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 1, Pages: 119-124

J. Environ. Treat. Tech.

ISSN: 2309-1185

Journal weblink: http://www.jett.dormaj.com

Roles of Institutional Quality on the Relationship

between Tourism and Economic Development in

Malaysia

Hui Shan LEE*, Sin Yee LEE, Wai Mun HAR

Universiti Tunku Abdul Rahman, Faculty of Accountancy and Management, Bandar Sungai Long; Email: hslee@utar.edu.my

Received: 12/08/2019

Accepted: 28/09/2019

Published: 30/02/2020

Abstract

This research intends to examine the roles of institutional quality on tourism-led growth and growth-led tourism hypothesis in Malaysia

in both short run and long run analysis. This study uses yearly data from 1996 to 2015 to verify whether institutional quality significantly

affects the relationship between tourism and economic growth in Malaysia. It provides a comprehensive dataset by investigating all the

institutional quality dimensions including control of corruption, government effectiveness, regulatory quality, rule of law, voice and

accountability, political stability and absence of violence in addition to the aggregate value and average value of these dimensions. The

findings provide empirical supports that institutional quality such as control of corruption and government effectiveness do play important

roles in the tourism and economic growth in Malaysia. In this essence, any policy planning that enhances the corruption and government

effectiveness of Malaysia could promote the tourism development and economic growth in Malaysia.

Keywords: Institutional quality; tourism; economic growth; tourism-led growth; growth-led tourism.

article is to investigate the roles of institutional quality on the

relationship between tourism and economic development in

Malaysia.

Introduction

At this new era, tourism has become one of the rapidly

growing services sectors of the world. This has prompted the

Malaysian government to set tourism as a key sector for

invigorating Malaysia's long-term economic growth.

Specifically, the 11th Malaysia Plan (2015-2020) has identified

the tourism sector as one of the National Key Economic Areas

The tourism–growth hypothesis has been long debated in the

literature. Although there are many studies of the relationship

between tourism and economic growth from a range of

perspective, the direction of its causality remains an unsolved

conundrum. Scholars suggested that economic growth induces

tourism development, because the high growth countries have

many business and working opportunities, while others studies

took the view that tourism Granger-causes economic growth

from the gain in foreign exchange and the creation of

employment to the host countries. From the existing researches,

several studies have been conducted to analyse the role of

tourism in economic growth. Generally, the causal relationship

between tourism and economic growth in Malaysia remains a

controversial subject. Interestingly, at this juncture, only (17, 3,

(

NKEAs) for transforming Malaysia into a high income nation

by 2020. In 1995, only 600 thousand foreign workers in

Malaysia were illegal (18, 9). The number subsequently

increased to 2.1 million as observed during the implementation

of the Illegal Immigrant Comprehensive Settlement Programme.

In view of these counterfactual data, doubts have arisen

regarding the appropriateness of emphasising on tourism as a

key sector for driving long-term economic growth in order to

attain the high income status by 2020. As not all tourist arrivals

involve genuine tourists, higher rates of arrivals do not

necessarily mean higher rates of tourism earnings. In fact,

UNWTO (2012) noted that Malaysia's ranking in terms of

tourism earnings was much lower than the ranking by tourist

arrivals. In view of these reservations, there is an urgent need for

a more accurate empirical assessment of the actual impact of

tourism on Malaysia's economic growth (5). Furthermore, the

important question is, how Malaysian policy makers address the

institution-related issues to attract a more consistent tourism

arrival to Malaysia? By looking at the institutional trend in

Malaysia from 1996, Malaysia achieved a higher institutional

quality index in year 2005 at an average value of 0.4741 but

started to decrease to 0.3844 in 2015 which was much lower than

the value in 1996 at 0.4360. Therefore, the objective of this

14) that examine the issues of institutional quality in the theory

of tourism and economic growth theory. Hence, this study aims

to investigate the stability of the tourism–growth nexus with the

roles of institutional quality for Malaysia.

The Malaysian economy has undergone various phases of

change from the primary sector to the manufacturing and

services sectors. Coupled with some prudent policies and

practical development planning, the economy has been growing

steadily. However, the global crisis in the 1980s has awakened

the government of the importance of the tourism industry in

creating employment opportunity and stimulating economic

growth. The Malaysian Tourism Promotion Board was

established to promote the tourism industry and stimulate the

numbers of international visitor arrivals to Malaysia (12, 19).

Corresponding author: Hui Shan LEE, Universiti Tunku Abdul Rahman, Faculty of Accountancy and Management, Bandar Sungai

Long. Email: hslee@utar.edu.my.

119

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 1, Pages: 119-124

Malaysia is proud and honoured to be nominated once again in

the prestigious World Travel Awards Asia & Australasia 2017.

Since 2015, both Malaysia and Tourism Malaysia have won

is also important to achieve encompassing and sustainable

progress in tourism and economic growth. Furthermore, Cao

(2015) suggests that a more effective institutional arrangement

and government’s responsibilities are needed to plan for a

sustainable tourism development. Institutional perspective has

emerged in the early 2000s, but still remains relatively

conceptually underdeveloped within the tourism field. Majority

of the studies limit their analysis by only linking the quantitative

macroeconomics variables in estimating the tourism-growth

relationship, we take our analysis one step further by

investigating the role of qualitative macroeconomic perspective

namely quality of institutions to investigate the relationship in

between tourism and economic growth (16). Unlike the earlier

studies, we contribute to the literature by analysing the role of

tourism in Malaysia's economic growth based upon the

characteristics of institutional quality. Various econometric

approaches are employed in this study.

‘

Asia’s Leading Destination’ and ‘Asia’s Leading Tourist

Board’ awards respectively for three consecutive years. This

remarkable record has sparked the interest of researchers to

investigate the tourism industry in Malaysia. Motivated by the

aforementioned shortcomings, the goal of this paper is to

investigate the impact of tourism expansion on Malaysia's

economic growth in a bilateral framework with the roles of

institutional quality. Unlike the earlier studies, we contribute to

the literature by analysing the role of tourism in Malaysia's

economic growth based upon institutional quality. Various

econometric approaches are employed in this study.

Literature Review

The growth in international tourism has taken place around

The rest of this paper is organised as follows. The next

section will explain the data and methodology. Section 3 will

discuss the econometric procedures followed. The empirical

findings will then be presented in Section 4 followed by

conclusion in Section 5.

various activities over the years: leisure, business, medical,

cultural, adventure, wellness, sports, religious, wildlife and

ecotourism. The United Nations has reported that this growth

has achieved the US $1 trillion mark, thus leading tourism

become an engine of development for many small economies

and a viable sector for developed economies. The literature has,

without a doubt, captured the different facets of the growing

importance of the tourism industry. Past literature on the impact

of tourism on growth generally find a positive association

between tourism and the economic growth rate.

For the validation of tourism-led growth hypothesis, it has

been confirmed by the studies (4, 18, 6, 7, 1, 2, 13). In the study

by Bouzahzah and El Menyari (2013), they only find tourism-

led growth hypothesis is valid in the short run, but only

unidirectional for growth-led tourism in the long run. (18)

validate that tourism-led growth hypothesis exist in Malaysia

both short run and long run. (11) also find that tourism-led

growth hypothesis in ASEAN-5 countries with public

intervention is needed to provide a better tourism facility to

enhance the economic growth. A new finding by (7) propose that

different conditions of tourism development such as will lead to

various consequences on the tourism–growth nexus.

3 Methodology

This study employs annual time series data from 1996 to

2015 extracted from The World Bank (economic growth and

tourism indicators). The institutional quality data is obtained

from Worldwide Governance Indicators. The economic growth

is determined by Gross Domestic Product (GDP) and number of

tourists’ arrival (TA) is the proxy for tourism variable. The

institutional quality variables consist of a comprehensive dataset

by investigating all the institutional quality dimensions

including control of corruption (CC), government effectiveness

(GE), regulatory quality (RQ), rule of law (RL), voice and

accountability (VA), political stability and absence of violence

(PS) in addition to the aggregate value of institutional quality

(AggIQ) and average value of institutional quality (AveIQ).

GDP and TA are transformed into natural logarithm to induce

immobility in the varience-covarience matrix.

Apart from that tourism-led growth hypothesis, Cheam et al.

2013) find significant growth-led tourism hypothesis in

First, we apply the standard augmented Dickey-Fuller

(ADF) unit root test and Philip-Perron (PP) unit root test to

determine the stationery characteristics of all the variables.

Then, we proceed to use Johansen Juselius test to examine is

there any cointegration among the variables (10). This is to

determine the presence of long-run equilibrium relationships

amongst economic growth, tourism and institutional variables

with the advantage of this method is not sensitive to the choice

of the dependent variable because it treats all variables as

endogenous. If a set of variables are cointegrated, one should use

the Vector Error Correction Model (VECM) because it takes

into account the short-run and long run elements. The VECM

model in this study can be written as:

(

Malaysia (16). The interesting finding from this research is that

they focus on the triangular casual relation in between tourism

and economic growth with other macroeconomics variables

such as education, physical capital, government tourism

expenditure and exports. Additionally, (2) find that there is

positive link between the extent of tourism specialization and

economic growth in cross sectional countries analysis from 1980

to 2002. Their research claim that limited data in institutional

quality could lead to significant measurements errors to even

more bias. Recommendation from (15) apart from

infrastructural, and innovation capabilities, institutional quality

120

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 1, Pages: 119-124

Where lagged dependent variables added into the equations

to remove serial correlation and to ensure that the disturbances

terms are white noise. j is the optimal lag length determined by

the Akaike's Information Criterion (AIC). Nevertheless, in the

absence of cointegration, one can only discover the short-run

causal relationship using the first difference VAR model (8).

Lastly, the causal relationship between tourism and economic

growth in Malaysia will be ascertained by the Granger causality

test to investigate the variables constitute unidirectional or

bidirectional relationship. This allows us to assess both long-run

growth that interacted by CC is not being examined, CC shows

a negative relationship. Whereas, when economic growth and

CC are the explanatory variables for tourism, both are showing

positive results indicate that better economic growth and better

CC will enhance tourism. In this essence, a better economic

growth implies that the infrastructure is better and control on

corruption is effective thus attracting more tourists to visit

Malaysia.

Similar to CC, GE also presents a negative relationship to

economic growth but a positive connection with tourism.

Therefore, it is concluded that better institutional quality such as

control of corruption and government effectiveness could

enhance the tourism growth in Malaysia.

To examine the granger causality of tourism, economic

growth and institutional quality, the results are report in Table 6

to Table 13 Since CC and GE postulate cointegrationg

relationship with tourism and economic growth, the ECT terms

are illustrated in Table 6 and Table 7 respectively. The negative

and significant ECT in Table 6 suggests that the dynamic

movement of economic growth will converge in the long run. In

the short run, CC will enhance economic growth but no

influence to the tourism. This suggest that lesser corruption

issues in the short run will enhance economic growth in the short

run.

Table 7 illustrates that in the dynamic changes in tourism

and government effectiveness in the short run will converge in

the long run due to their ECT are negative signs and significant.

In the short run, tourism does end government effectiveness

have no impact towards economic growth. However, economic

growth does postulates positive impact towards tourism in the

short run. This scenario could be due to higher government

spending to improve the facilities in Malaysia could encourage

more tourists to visit Malaysia.



and short-run causality, respectively, on the

-test of the

lagged first differenced terms for each right-hand-side variable

and the t-test of the error correction term.

Results and Findings

Table 1 presents the unit root test of all the variables, at the

constant with trend model, both ADF and PP tests show that all

variables are stationery at I(1). Moving to the Johansen Juselius

test, only control of corruption (CC) and government

effectiveness (GE) indicate existence of cointegration with the

tourism and growth variables, thus the results are shown in Table

and 3 To conserve space, other institutional variables such as

RQ, RL, VA, PS, AggIQ and AveIQ which do not provide

significant cointegration relationship are not reported.

Since CC and GE show cointegration relationship, the

cointegrating eigenvectors for the long run relationship with

tourism and economic growth are reported in Table 4 and Table

respectively. From Table 4, tourism does postulate significant

impact to economic growth in the long run. Economic growth

also demonstrates positive effect to tourism in the long run. CC

illustrates a positive relationship to tourism but indicates a

negative relationship to economic growth. This can be explained

by when impacts of economic growth are explained by tourism

and CC only, there might be other variables which explain the

Table 1: Test for Unit Root

ADF Unit Root

Philip-Perron

Unit Root

ADF Unit Root Philip-Perron

Unit Root

Constant Without Trend

Level

Constant With Trend

1 Difference

lnGDP

lnTA

AggIQ

-0.395800

-3.291638**

-1.676164

-1.440448

-2.321654

-2.044815

-3.360832**

-2.568126

-2.259321

-0.424737

-3.682459**

-4.406163***

-3.104560**

-3.605434**

-4.658225***

-3.875050***

-4.095500***

-3.885472***

-3.936403***

-3.633142**

-4.537979***

-3.553353**

-3.648954**

-4.813245***

-3.866856***

-5.633263***

-3.934728***

-3.936403***

-1.162021

-1.736840

-4.626090***

-2.321654

-2.296039

-3.135401**

-2.663822*

-2.369351

AveIQ

Note: The values represent the t-statistics. *, **, *** denote significant at 10%, 5%, 1% respectively.

Table 2: Result of Multivariate Cointegration Test (Institutional variable: Control of Corruption)

Hypothesis Ho:

rank=r

r=0

r=1

Maximum Eigenvalue

Test Statistic

21.40214**

3.573887

2.451131

Trace

95%

Test Statistic

27.42715

6.025017

2.451131

95%

21.13162

14.26460

3.841466

29.79707

15.49471

3.841466

r=2

Table 3: Result of Multivariate Cointegration Test (Institutional variable: Government Effectiveness)

Hypothesis Ho:

rank=r

r=0

r=1

Maximum Eigenvalue

Test Statistic

21.20651**

15.59802**

2.978351

Trace

95%

Test Statistic

37.78288**

18.57637**

2.978351

95%

21.13162

14.26460

3.841466

29.79707

15.49471

3.841466

r=2

121

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 1, Pages: 119-124

Among the other institutional variables such as RQ, RL, VA,

AggIQ and AveIG, all of these variables do not postulate

relationship with tourism and economic in both long run and

short run. However, political stability does enhance tourism in

the short run. This implies that Malaysia could attract more

tourists if the political environment in Malaysia is stable. From

Table 8 to 13, the results proven that tourism does improve the

economic growth in the short run. This is because the spending

by the tourists could boost the output in Malaysia. In Table 11,

a better political stability will enhance both tourism and

economic growth.

control of corruption and government effectiveness are very

important to support tourism and economic growth in the long

run. If the political stability is disrupted, it will reduce the tourist

into Malaysia as the tourists are concern with the safety issue.

The message underlying this finding is that governments in

Malaysia must do more to combat corruption, and to maintain

the government effectiveness in addition to sustain a good

political stability environment. This is to take place in

conjunction with investing in projects that improve the

attractiveness of Malaysia to the outside world at the same time

improving institutional quality. This paper shows that tourists

have a higher propensity to visit a county with higher standards

of institutional quality.

Table 4: Cointegrating eigenvectors (Institutional variable:

Control of Corruption)

lnGDP

1.00

lnTA

0.806***

Table 5: Cointegrating eigenvectors (Institutional variable:

Government Effectiveness)

-0.8033**

(-2.051)

0.997**

(2.327)

lnGDP

-1.244***

(-3.990)

(

6.590)

lnGDP

-1.00

lnTA

1.00

lnGDP

1.241***

1.005***

(4.580)

lnGDP

0.995***

(4.719)

lnTA

-4.159***

(-5.782)

4.140***

(6.613)

lnGDP

-0.24**

(-2.129)

(

7.697)

lnTA

-1.00

1.00

lnTA

1.002***

(

3.877)

1.00

0.242**

(

2.363)

Conclusion

This study shows that tourism does augment economic

growth in Malaysia both in long run and short run. Furthermore,

Table 6: Granger Causality Results based on VECM (Institutional variable: Control of Corruption)

Independent Variables

Dependent

ECTt-1



statistics of lagged 1 differenced term [p-value]

coefficient

(t-ratio)

-0.4533***

(-3.8370)

-0.0172

(-0.296)

-0.113

(-1.076)

Variable

ΔlnGDP

ΔlnTA

0.647266

[0.4211]

ΔCC

4.716913**

[0.0299]

0.075278

[0.7838]

ΔlnTA

ΔCC

0.556770

0.4556]

0.141386

0.7069]

[

3.030391*

[0.0817]

[

Note: *** and ** denotes significant at 1% and 5% significance level, respectively. The figure in the parenthesis (…) denote as t-statistic and the figure in

the squared brackets […] represent as p-value.

Table 7: Granger Causality Results based on VECM (Institutional variable: Government Effectiveness)

Independent Variables

Dependent

ECTt-1



statistics of lagged 1 differenced term [p-value]

coefficient

(t-ratio)

0.174

(1.977)

-1.557**

(1.627)

Variable

ΔlnGDP

ΔlnTA

2.613412

[0.1060]

ΔGE

0.272189

[0.6019]

2.242519

[0.1343]

ΔlnTA

ΔGE

3.331547*

0.0680]

0.204626

0.6510]

[

0.138545

[0.7097]

-0.648***

(-2.569)

[

Note: *** and ** denotes significant at 1% and 5% significance level, respectively. The figure in the parenthesis (…) denote as t-statistic and the figure in

the squared brackets […] represent as p-value

122

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 1, Pages: 119-124

Table 8: Granger Causality Results based on VECM (Institutional variable: Regulator Quality)

Independent Variables

Dependent



statistics of lagged 1 differenced term [p-value]

Variable

ΔlnGDP

ΔlnTA

ΔGE

ΔlnGDP

4.318458**

[0.0377]

1.805528

[0.1790]

0.079685

[0.7777]

ΔlnTA

ΔRQ

1.284248

0.2571]

0.126601

0.7220]

[

0.009790

[0.9212]

[

Note: *** and ** denotes significant at 1% and 5% significance level, respectively. The figure in the parenthesis (…) denote as t-statistic and the figure in

the squared brackets […] represent as p-value

Table 9: Granger Causality Results based on VECM (Institutional variable: Rule of Law)

Independent Variables

Dependent



statistics of lagged 1 differenced term [p-value]

Variable

ΔlnGDP

ΔlnTA

ΔRL

ΔlnGDP

5.334201**

[0.0209]

0.611726

[0.4341]

2.102049

[0.1471]

ΔlnTA

ΔRL

0.589312

0.4427]

0.170929

0.6793]

[

0.527344

[0.4677]

[

Note: *** and ** denotes significant at 1% and 5% significance level, respectively. The figure in the parenthesis (…) denote as t-statistic and the figure in

the squared brackets […] represent as p-value

Table 10: Granger Causality Results based on VECM (Institutional variable: Voice and Accountability)

Independent Variables

Dependent

Variable

ΔlnGDP

ΔlnTA



statistics of lagged 1 differenced term [p-value]

ΔlnGDP

ΔlnTA

ΔVA

3.831068**

[0.0503]

0.465908

[0.4949]

0.855037

[0.3551]

1.208866

0.2716]

0.289298

0.5907]

[

ΔVA

0.644012

[0.4223]

[

Note: *** and ** denotes significant at 1% and 5% significance level, respectively. The figure in the parenthesis (…) denote as t-statistic and the figure in

the squared brackets […] represent as p-value

Table 11: Granger Causality Results based on VECM (Institutional variable: Political Stability)

Independent Variables

Dependent

Variable

ΔlnGDP

ΔlnTA



statistics of lagged 1 differenced term [p-value]

ΔlnGDP

ΔlnTA

ΔPS

5.972970**

[0.0145]

1.053734

[0.3046]

4.054670**

[0.0440]

3.792975*

0.0515]

8.157524***

0.0043]

[

ΔPS

8.531746***

[0.0035]

[

Note: *** and ** denotes significant at 1% and 5% significance level, respectively. The figure in the parenthesis (…) denote as t-

statistic and the figure in the squared brackets […] represent as p-value

123

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 1, Pages: 119-124

Table 12: Granger Causality Results based on VECM (Institutional variable: Aggregate Institutional Quality)

Independent Variables

Dependent



statistics of lagged 1 differenced term [p-value]

Variable

ΔlnGDP

ΔlnTA

5.450132**

[0.0196]

ΔAggIQ

0.672155

[0.4123]

0.558975

[0.4547]

ΔlnTA

1.810409

[0.1785]

ΔAggIQ

1.489952

2.101577

[0.1471]

[0.2222]

Note: *** and ** denotes significant at 1% and 5% significance level, respectively. The figure in the parenthesis (…) denote as t-statistic and the figure in

the squared brackets […] represent as p-value

Table 13: Granger Causality Results based on VECM (Institutional variable: Average Institutional Quality)

Independent Variables

Dependent



statistics of lagged 1 differenced term [p-value]

Variable

ΔlnGDP

ΔlnTA

5.450132**

[0.0196]

ΔAveIQ

0.672155

[0.4123]

0.558975

[0.4547]

ΔlnTA

1.810409

0.1785]

1.489952

0.2222]

[

ΔAveIQ

2.101577

[0.1471]

[

Note: *** and ** denotes significant at 1% and 5% significance level, respectively. The figure in the parenthesis (…) denote as t-statistic and the figure in

the squared brackets […] represent as p-value

3. Lopes IM, Oliveira P. Evaluation of the Implementation of the

General Data Protection Regulation in Health Clinics. Journal of

Information Systems Engineering & Management. 2018;3(4):28.

4. Luo F, Moyle BD, Bao J, Zhong Y. The role of institutions in the

production of space for tourism: National Forest Parks in China.

Forest Policy and Economics. 2016;70, 47-55.

5. Peterson RR, Cardenas D, Harrill R. Tourism as a catalyst for

economic development in Latin America: setting an agenda for

policy and research. Tourism analysis. 2014;19(1):117-122.

6. Pussyrmanov N, Rystina I, Bulegenova B, Askeyeva G, Gabdulina

B. President Nursultan Nazarbayevs program article “The course

towards future: Modernization of public consciousness”–ideology,

value and political aspects. Opción. 2018;34(85-2):824-837.

7. Ruhanen L, Char-lee JM. Location, Location, Location’—The

Relative Importance of Country, Institution and Program: A Study

of Tourism Postgraduate Students. Journal of Hospitality and

Tourism Management. 2010;17(1):44-52.

References

Ameen AM, Ahmed MF, Hafez MAA. The Impact of Management

Accounting and How It Can Be Implemented into the

Organizational Culture. Dutch Journal of Finance and Management.

018;2(1):02.

Piotrowski JM, Arezki MR, Cherif R. Tourism Specialization and

Economic Development+ L4183: Evidence from the UNESCO

World Heritage List (No. 9-176). International Monetary Fund.

009.

Balli F, Balli HO, Louis RJ. The impacts of immigrants and

institutions on bilateral tourism flows. Tourism Management.

016;52:221-229.

Bouzahzah M, El Menyari Y. International tourism and economic

growth: the case of Morocco and Tunisia. The Journal of North

African Studies. 2013;18(4):592-607.

Cao X. Challenges and potential improvements in the policy and

regulatory framework for sustainable tourism planning in China: the

case of Shanxi Province. Journal of Sustainable Tourism.

8. Tang CF, Tan EC. Does tourism effectively stimulate Malaysia's

economic growth?. Tourism management. 2015;46: 158-163.

9. Tayebiniya NK, Khorasgani NS. The Relationship Between

Workplace Spirituality And Job Performance Among Staff Of Azad

015;23(3):455-476.

Li CC, Mahmood R, Abdullah H, Chuan OS. Economic growth,

tourism and selected macroeconomic variables: A triangular causal

relationship in Malaysia. Margin: The Journal of Applied Economic

Research. 2013;7(2):185-206.

Chiu YB, Yeh LT. The threshold effects of the tourism-led growth

hypothesis: Evidence from a cross-sectional model. Journal of

Travel Research. 2017;56(5):625-637.

Islamic University, Iran. Humanities

Reviews. 2018;6(1):14-18.

Social Sciences

Granger CW. Some recent development in a concept of causality.

Journal of econometrics. 1988;39(1-2):199-211

Haghshenas S, Iravani MR, Nasrabadi HAB. Study Of Effective

Factors On Job Satisfaction Of Omid Hospital Staff In Isfahan City.

UCT Journal of Management and Accounting Studies.

015;3(1):15-17.

0. Johansen S, Juselius K. Maximum likelihood estimation and

inference on cointegration—with applications to the demand for

money. Oxford Bulletin of Economics and statistics.

990;52(2):169-210.

1. Kadir N, Karim MZA. Tourism and economic growth in Malaysia:

Evidence from tourist arrivals from ASEAN-S countries. Economic

research-Ekonomska istraživanja. 2012;25(4):1089-1100.

2. Lean HH, Tang CF. Is the tourism‐led growth hypothesis stable for

Malaysia? A note. International Journal of Tourism Research.

010;12(4):375-378.

124