The Competitiveness of Jordanian Tourism Activity in Enhancement Economic Growth

Abstract

Competitiveness studies are regarded as one of the most essential at the worldwide level, particularly in the tourist sector, which is relying on to boost economic growth and living standards. The purpose of this study was to examine the competitive features of the Jordanian tourist industry, as well as the long-term equilibrium relationships between the factors of tourism activity and economic growth, using the Autoregressive distributed lag model (ARDL) over the period 1990-2021. The study looked at the favorable benefits of tourism on employment, poverty reduction, and the Jordanian economy's balance of payments. Using the koyck model, the study discovered a decline in the influence of the number of tourists on Jordanian tourism income. It also determined that there is a long-term and bidirectional equilibrium link between tourism activity and economic growth, as the coefficient of the speed of adjustment for short-term deviations from its long-term route was 37%. It suggested strengthening the sector's competitiveness by attracting more visitors and raising the number of tourism investments in Jordan.

Full text

*Corresponding author e-mail: mohsaleh1966@yahoo.com
© 2023 NSP
Natural Sciences Publishing Cor.
Appl. Math. Inf. Sci. 17, No. 1, 161-174 (2023) 161
Applied Mathematics & Information Sciences
An International Journal
http://dx.doi.org/10.18576/amis/170117
The Competitiveness of Jordanian Tourism Activity in
Enhancement Economic Growth
Mohammad H. Saleh1,*, Omar Jawabreh2, Emad Al Dein AL Fahmawee3 and Basel J. A. Ali4
1 Department of Finance, The University of Jordan, Aqaba, Jordan
2 Department of Hotel Management, The University of Jordan, Aqaba, Jordan
3 Department of Interior Design, Faculty of Art and Design, Applied Science Private University, Kingdom of Bahrain
4 Accounting and Finance Department, Applied Science University, Kingdom of Bahrain
Received: 10 Sep. 2022, Revised: 2 Dec. 2022, Accepted: 17 Dec. 2022
Published online: 1 Jan. 2023
Abstract: Competitiveness studies are regarded as one of the most essential at the worldwide level, particularly in the tourist
sector, which is relying on to boost economic growth and living standards. The purpose of this study was to examine the
competitive features of the Jordanian tourist industry, as well as the long-term equilibrium relationships between the factors
of tourism activity and economic growth, using the Autoregressive distributed lag model (ARDL) over the period 1990-
2021. The study looked at the favorable benefits of tourism on employment, poverty reduction, and the Jordanian economy's
balance of payments. Using the koyck model, the study discovered a decline in the influence of the number of tourists on
Jordanian tourism income. It also determined that there is a long-term and bidirectional equilibrium link between tourism
activity and economic growth, as the coefficient of the speed of adjustment for short-term deviations from its long-term route
was 37%. It suggested strengthening the sector's competitiveness by attracting more visitors and raising the number of
tourism investments in Jordan.
Keywords: Competitiveness, Autoregressive distributed lag model (ARDL), koyck model, Tourism activity, Economic
growth.
1 Introduction
The international tourism industry is widely regarded as one
of the most important industries in terms of boosting
economic growth, luring investments, and creating job
openings throughout the world. The tourist industry's
contribution to the global GDP reached 10.3% in 2019, and
throughout the course of the period (2014-2019), the tourism
industry was responsible for the creation of one out of every
four new employments around the world [1].
Countries compete with one another to entice the greatest
number of tourists from a variety of nations all over the
world. This is done with the goal of having a significant
influence on the state of the macroeconomy and the economy
as a whole, in addition to the mutually beneficial impact that
tourism has on the development of other economic sectors
[2-5]. Other areas of the economy, including as industry,
commerce, finance, insurance, and logistical services, also
benefit from the expansion that is stimulated by tourism
activities [2]. Because of this, the forward and backward
links between different economic sectors get stronger as a
direct result of the economies of scope and vertical
integration [6,7]. Through increased investments in tourist
facilities, travel and tourism agencies, and support activities,
the tourism sector of the Jordanian economy helps to
strengthen the country's balance of payments, contributes to
the provision of hard currencies for the Jordanian treasury,
and creates job opportunities. There are approximately
55,000 occupations directly related to tourist, and an
additional 125,000 jobs indirectly related to tourism [8].
The effects of crises and external shocks related to epidemics
and natural disasters have had an impact on the tourism
sector, as they have had an impact on other sectors as well.
For example, both the Jordanian economy in general and the
tourism sector were affected by the Corona pandemic (Covid
19) over the course of the two years 2020-2021[9]. This led
to a reduction in the number of jobs available in the tourism
industry, as well as a reduction in the sector's contribution to
real GDP, which went from 14% in 2019 to 8% in 2020. The
decline in tourism activity coincided with a reduction in the
number of tourists who came to Jordan. As a result of this,
the sector's revenues declined, investment and activities
related to the sector also decreased, and the sector's revenues.
In addition, the contribution of tourist revenue to the real

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© 2023 NSP
Natural Sciences Publishing Cor.
GDP fell from 31.6% in 2019 to 8% in 2020, reflecting a
general downward trend in that direction. As a result of a
decline in tourism-related income and sales taxes, as well as
taxes collected from airports and departures, as well as
admission fees to tourist attractions, this was accompanied
by a dip in Jordan's rating among the global tourist industry
competitiveness indicators for 130 nations, which went from
77 in 2016 to 60 in 2020. The ranking had previously been
at 77 in 2016.
Strategies have been devised to meet the issues that are
facing tourism based on the growth of the tourist industry
[10-13]. These strategies were designed so that the tourism
sector may continue to play the critical role it now plays in
the Jordanian economy. Attracting tourists from across the
world, retraining Jordanian workers who have left the
industry, and establishing a multi-risk travel insurance policy
for visitors arriving on Jordan's national airlines are some of
the goals of the tourism industry in Jordan. On the other
hand, the preparation of a unified paper and electronic
tourism guide, the promotion of innovation and digital
transformation, the stimulation of sustainable tourism
development practices, in addition to attracting foreign direct
investment in the tourism sector, providing incentives and
tax exemptions, and benefiting from programs developed by
the World Health Organization and the European Bank for
Development and Reconstruction are all things that should
be done. There will be a total investment of 93.6 million
Jordanian dinars [8] made by the government in the tourist
industry through September of 2020.
Following the slow but steady recovery from the Covid-19
crisis, tourist promotion techniques were implemented, and
these initiatives have had a favorable impact on the overall
tourism industry. The Travel and Tourism Sector
Competitiveness Index for 2022 that was produced by the
World Economic Forum moved Jordan's ranking up from
83rd in 2019 to 64th in 2021 out of 117 nations. In 2019,
Jordan's position on the index was 83rd.
Because the ARDL method is used in the analysis of the
impact that the tourism sector has on the competitiveness of
the Jordanian economy, the importance of the study
increases due to the small number of studies dealing with the
tourism sector using econometrics in Jordan. This is one of
the reasons why the study is so important. The importance of
Jordan's tourist industry to the country's overall economic
expansion was the primary subject of this study.
The purpose of the study is to demonstrate the influence that
the tourism industry has on economic growth and to
demonstrate the role that the tourism industry plays in
improving the competitiveness of the economy. This will be
accomplished by analyzing the effect of the number of
tourists on tourism income utilizing the Kwik model,
investigating the long-term relationship of tourism activity
utilizing the ARDL model, and determining the type of
causal relationship between tourism activity and economic
growth utilizing Granger causality.
Jordan is counting on an improvement in the competitiveness
of the tourism sector [14, 15]in order to achieve sustainable
development after the phase of recovery from the Covid-19
crisis, whose repercussions have affected all sectors,
including the tourism sector [2, 16]. Jordan is counting on
this improvement in order to achieve sustainable
development after the phase of recovery from the crisis. This
study focuses on the role that tourism plays in increasing the
competitiveness of the Jordanian economy by analyzing the
influence that tourist income has on real gross domestic
product for the period of time spanning 1990-2021. Despite
the abundance and variety of literature linked to this topic,
there are relatively few studies in Jordan that deal with the
influence of tourism activities on the macroeconomic,
particularly those that use the Koyck model and the ARDL
approach [2].
This study is exhaustive in terms of studying the impact on
employment, balance of payments, investment, and real
output to improve the level of competitiveness of the
Jordanian economy. In addition, it tests the type of causal
relationship according to Granger's method between tourism
variables and economic growth of the Jordanian economy for
the period that was mentioned. This study was carried out in
Jordan.
The paper is organized as follows: the first section is an
introduction; the second section focuses on the literature
review; the third section exposes the impact tourism can
have on certain fields of competitiveness; the fourth section
presents the data and methodology; the fifth section explains
the results; and the final section displays a conclusion and
some suggestions.
2 Literature Review
There have been a number of studies that have focused on
investigating the economic and non-economic variables that
have an effect on tourism demand by using panel data. For
example, [2,17] discussed the relationship between
macroeconomic factors and tourism demand for nine
selected ASEAN countries based on the analysis of the panel
data. These studies highlighted the positive impact that the
income of Southeast Asian countries has on their ability to
attract tourists. They came to the conclusion that free trade
increases demand, but the appreciation of Asean currencies
makes it less appealing for visitors to travel there. [18]
revealed the economic and non-economic factors that
determinants demand tourism for five Asean Countries.
International tourism arrival is included as a dependent
variable, and independent variables include: GDP per
capital, Inflation, Tourism industry expenditures, exchange
rate, and others. International tourism arrival is included as a
dependent variable. In this article, a Panel data model with a
Fixed effect and a Random effect was used to explore the
factors that determine the amount of foreign tourist that
comes to each of the five Asean countries. [19] used the
panel data approach to conduct an analysis of the factors
impacting the demand for tourism in Turkey. The analysis
included a comparison of ordinary least squares, fixed
effects models, and random effects. They came to the

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Natural Sciences Publishing Cor.
conclusion that the most important determinant in the
demand for tourism was the state of development in the
countries whose citizens traveled to Turkey as tourists. They
found that some aspects of the country, such as language and
geographical proximity, directly affect the flow of visitors,
while other aspects, such as exchange rate differences and
distance, reduce tourist attraction. [20] analyzed the factors
that determine the number of international tourists who visit
Colombia and found that language and geographical
proximity are two of those factors.
[21] investigated the demand for tourism in Malaysia by
looking at a variety of economic aspects. Using the gravity
model as an example. According to the linear logarithmic
equation, the demand for tourism is related to the total
national income (GNI) and is negatively related to the
exchange rate, while the consumer price index (CPI) or the
inflation rate reduces the number of tourists who travel to
Malaysia, and the length of distance reduces the demand for
tourism. Additionally, the number of tourists who travel to
Malaysia is reduced by the length of distance. [22] studied
the factors that drive tourism demand, with a primary
emphasis on the determinants of tourist expenditure, also
known as visitor spending. Visitors' role. [23-25] shown that
there are inelastic links between national income and tourist
spending, which means that as national income grows,
tourism expenditure also grows, but at a slower rate. [26]
proposed to develop tourism in Azerbaijan by increasing the
number of tourists visiting the country, the absence of
seasonality in tourist areas, and the increase in the number of
days of tourism; The importance of reducing high inflation
and reducing the impact of fluctuations resulting from
economic crises in neighboring countries.
[27] studied how the Number of Beds affects the Revenues
from Tourism and found some interesting results. According
to the findings, the size of the tourist units, which was
determined by the ratio of beds to tourism units, has a
significant and direct influence on the expansion of the
economy; however, the number of beds is not a particularly
relevant factor in determining the amount of revenue
generated by tourism. The authors of study [28] used the
vector autoregression (VAR) model, the error correction
model (ECM), and the Granger causality test to analyze the
link between the number of tourists visiting China and the
country's real GDP throughout the period of time (1994-
2015). The findings indicated that there is a significant
positive correlation in the long run between the number of
tourists and real GDP in Beijing, China. Additionally, the
results of the error correction model (ECM) demonstrated
that there is a connection between tourism and economic
expansion in the short term. In addition, the findings of the
VAR test revealed that the expansion of the tourist industry
had an impact on the real GDP. Studies that employed the
ARDL model, [29] used the autoregressive distributed lag
model (ARDL) to assess cointegration, long-term linkages,
and an error correction model of tourism demand from
visitors coming in Turkey from Europe and the United
States. According to the findings, the factors that are most
influential in determining the total number of tourists that
arrive are the real per capita income and the real effective
exchange. The pricing and financial crises were shown to
have very modest effects, according to the research.
[16] utilized an autoregressive distributed lag (ARDL)
model to highlight the impact that income, relative pricing
competition, and substitutes prices play in the demand for
tourism in Indonesia. They discovered that Malaysia,
Singapore, Australia, Japan, and India all had high income
elasticity, which indicates that travel is a luxury item in those
countries.
Literature related to Impact tourism on economic
growth
[30] used time-series data for various macro factors to
investigate the hypothesis of tourism-led development in Sao
Tome and Principe (STP). It was determined that there is a
one-way link between TR and GDP, as well as between FDI
and all other variables (GDP, TR, ER).
[31] used an autoregressive distributed lag (ARDL) model to
argue about tourism and its influence on economic
development in Pakistan. The findings revealed that
international tourist earnings and tourism expenditures on
passenger transportation aspects have a beneficial influence
on economic development, and that there are long-term
correlations between variables. [32] used Panel co-
integration and the PVAR approach to examine the
association between tourism and economic development in
five southern European nations. Real GDP, labor force, gross
fixed capital creation, and tourism were discovered to have
a long-term association. There is also a bidirectional causal
link between tourism, labor force, and economic growth.
[33] established the link between revenue and economic
development. The findings revealed a bidirectional causal
association between tourist revenues and economic
development in Jilin, Anhui, and Hubei, but the findings
indicated a neutrality causality compatible with just one of
Heilongjiang's provinces. For the provinces of Shanxi,
Jiangxi, and Henan, there is a negative connection consistent
with the conservation hypothesis. [34] used panel ARDL
cointegration analysis to investigate the link between tourist
flows and economic development in five BRICS (Brazil,
Russia, India, China, and South Africa) nations from 1995 to
2015. According to the panel ARDL cointegration test,
researchers discovered a long-term association between
tourist growth and economic development. The findings also
suggested a bi-directional relationship between inbound
tourism and economic growth according to the Granger
causality test. This finding supports the validity of the
feedback hypothesis in the BRICS nations.
[35] investigated the relationship between tourism and
growth in Nigeria through time (1995-2018). By using
visitor arrivals, the findings validate the tourism-led growth
hypothesis, however the neutrality hypothesis is not refuted
if there is no causal link for total growth and for both tourism
earnings and expenditures.
[36] used a vector autoregressive model and Granger
Causality to investigate the relationship between tourism and

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Natural Sciences Publishing Cor.
economic development in Saudi Arabia from 1990 to 2018.
The Granger causality test demonstrates that there is a
bidirectional link between tourism and economic
development in Saudi Arabia. According to the research,
commercial openness has a detrimental influence on tourism.
Fuinhas et al. (2020) discussed the significance of tourism in
generating economic development in Latin American and
Caribbean nations. Tourism's influence on economic
development was examined using the autoregressive
distributed lag (ARDL) model. According to the findings of
22 nations, tourism capital investment per capita and the
number of incoming visitors (number of people) are the
primary engines of economic development in the short and
long term. [37-40] shown that economic expansion may
benefit the nation.
[41] Investigated the elasticity of demand for incoming
tourism in Middle Eastern and North African nations,
demonstrating the effect of internal and external conflicts,
global GDP, and comparable pricing. The approaches of
fully modified OLS (FM-OLS), Dynamic-OLS (DOLS), and
the newly established method of instantaneous quantitative
regression were employed in the investigation (MMQR).
The findings revealed that the effect of external and internal
conflicts on incoming tourist demand is negative and
inelastic, with external conflict having a higher influence.
Furthermore, global GDP has a positive and elastic impact
in tourist demand, demonstrating that tourism is a luxury
product. The rise in relative pricing, on the other hand,
diminishes demand for incoming tourists.
[42] examined the influence of tourist development on
Jammu and Kashmir's economic growth, highlighting the
implications of tourism on employment, infrastructural
development, and regional development. The research stated
that an emphasis on hospitality management, cultural
heritage, and infrastructural development is required. This
will result in more employment possibilities, more
investment, and a reduction in poverty in the state. [43]
Using the Distributed Autoregressive Model (ARDL),
explained the contribution of tourism to economic
development in Qatar, evaluating the tourist-led growth
(TLGH) hypothesis on the Qatari economy. The research
revealed that, although tourism had no statistically
significant influence on economic growth in the near term, it
does have a statistically significant impact in the long run.
The report advocated for continued investment in the tourist
industry to encourage economic development, diversify
revenue sources, and minimize reliance on energy. [44] used
Autoregressive distributed lag (ARDL) limits to investigate
the association between tourism and economic development
in Thailand. The research found that tourism and growth
have a long-term co-integration connection, although the
beneficial effect of tourism in the near term is smaller than
in the long run. Furthermore, no substantial positive
association exists between foreign direct investment and
economic development. [45] used the Distributed Auto
Regression (ARDL) approach to investigate the influence of
tourism on Pakistan's economic growth rate. According to
the report, tourism has a crucial influence in economic
development. In Pakistan, however, the inflation rate has a
detrimental impact on economic development. [46]
investigated the link between tourism and economic
development in Hong Kong. They determined that in Hong
Kong, both the tourist-led economy growth hypothesis
(TLGH) and the economic-driven tourism growth hypothesis
(EDTGH) are unstable. [47] The link between tourism and
economic development in Italy was examined. This article
examines the influence of international visitor expenditure
on per capita value added growth in Italian regions. The
findings revealed a positive and statistically significant
effect, although it was minor in economic terms, with the
least developed governorates bearing the biggest burden.
[47] used an Autoregressive Distributed Lag (ARDL)
technique to investigate the influence of tourism on Nepal's
economic development; the findings found that tourism had
no meaningful impact on Nepal's economic growth in the
short and long run. Despite this, the amount of commerce has
a significant long-run impact on economic growth. The
research ended by rejecting the tourism-driven growth
hypothesis in Nepal.
Advantages of the competitiveness of the Jordanian
tourism sector:
Jordan possesses numerous characteristics and advantages
that qualify it for an advanced competitive position in the
regional and global markets [48-50], some of which are
related to the abundance of natural potential, such as the
moderate climate, the privileged geographical position in the
Middle East, and the abundance of natural minerals such as
phosphate and potash. Others are associated with notable
historical and archaeological locations, such as Petra, Wadi
Rum, Qasr al-Hallabat, Umm Qais, the pillars of Jerash,
Ajloun Castle, and others.
Jordan features several religious and cultural sites, including
the Jordan Valley, the Dead Sea, Mount Nebo, the Baptist
Church, and the Roman Amphitheater. There are also
museums, swimming pools, places of leisure and relaxation,
and reserves like as the Dana and Azraq Reserve, among
others, which are a source of appeal for visitors visiting
Jordan from all over the globe.
Impact of tourism on employment:
According to research conducted and published by the World
Tourism Organization (UNWTO), the tourism industry
accounts for 330 million employments on a worldwide scale,
which is comparable to one tenth of all occupations in the
world. The number of people employed in the tourist
industry in Jordan has climbed from 8.3 thousand employees
in 1990 to 52 thousand workers in 2021, which means that
tourism has helped to alleviate the issue of unemployment
on the Jordanian labor market. Employees from Jordan make
up the biggest portion of the total volume of employment in
the tourist industry, with a ratio that topped 80%, whilst the
percentage of workers from other countries was, on average,
16%. The majority of available jobs go to men, who make
up an average of 90 percent of the workforce, while the

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number of women who work in the tourist industry is still
relatively low [1].
According to statistics that were provided by the Jordanian
Ministry of Tourism and Antiquities, tourist restaurants and
the hotel sector are responsible for the employment of more
than 75 percent of the employees in the tourism industry. The
amount of linkage that exists between the tourism industry
and other industries determines the number of indirect job
opportunities. The tourism industry in Jordan is responsible
for providing approximately 0.6 jobs indirectly for every
single job opportunity, which is equivalent to six indirect
opportunities for every ten direct job opportunities. These
numbers are in line with the worldwide ratios, which show
that the total direct and indirect employment possibilities
produced by the sector comprise around 10% of the overall
workforce in the economy [51]. These percentages are
compatible with the global ratios since they show that
Impact of tourism on supporting the of balance of
payments:
The tourism sector contributed to supplying Jordan’s foreign
The contribution of the tourism sector to total exports
reached an average of 43.5% of total exports, which is
significantly higher than the contribution of the industry
sector, which was approximately 18% of total exports for the
period 1990-2020. This meant that the tourism sector was
able to help Jordan maintain its positive foreign currency
balance. The tourism balance, which can be found within the
balance of services items, illustrates the contribution that
tourism makes to the overall balance of payments by
expressing the difference between tourist revenues and
payments. In this sense, Jordan depends on the comparative
advantage given by the tourist industry to increase hard
currency reserves and strengthen the sector's
competitiveness locally and worldwide in comparison to
other economic sectors [52].
Impact of tourism on reducing poverty
The tourist industry has three benefits that help reduce
poverty, particularly in nations that are considered to be in
the third world: Direct Impact The labor-intensive tourist
industry employs a large number of individuals with low or
intermediate levels of education or training. The dependency
of the tourist industry on inputs from other economic sectors
such as food production, building, and transportation is an
example of the indirect influence that may be seen. The third
effect is the dynamic impact, which emphasizes the positive
benefits of tourist activities via improvements to
infrastructure, increasing engagement of women in the labor
market, and the recovery of the handcraft sector. People
living in poverty in many different nations, such as Namibia
and Laos, are dependent on tourism as a source of income
via the supply chain. On the other hand, the high cost of
living and high prices that are experienced by residents of
the host nation are two of the negative consequences that are
caused by tourist activities. Numerous studies have
investigated the role that tourism plays in reducing levels of
poverty. [53] This refers to how the growth of the tourist
industry in sub-Saharan Africa helps to bring down levels of
poverty. [54] Using the ARDL model, investigate whether or
not there is a correlation between international tourism in
Mexico and the level of poverty, and investigate whether or
not there is a unidirectional, long-term relationship between
international tourism and the alleviation of poverty, as
suggested by the Yamamoto causality test. It was found in
[55] that tourism has an influence on reducing poverty.
According to the findings, tourism does, in fact, contribute
to a reduction in the number of people living in poverty.
Impact of tourism on supporting economic
diversification
Many nations, particularly developing countries and
countries where the national income is centered on a single
resource, have benefited from the activity of tourism since it
has contributed to the diversification of their sources of
revenue. The tourism industry is often regarded as the sector
that contributes the most rapidly to an increase in the pace of
economic expansion. Additionally, the tourism industry is
one of the most significant foundations of an economy that
is based on services that produce money. It is anticipated that
service activities that support tourism would be established,
such as restaurants, tourist firms, banks, insurance
companies, and other types of businesses [56].
3 Methodologies
The study used a time series approach for the period 1990 -
2021, where data for the Jordanian tourism sector was
obtained from a variety of sources, including: The Central
Bank of Jordan, the Ministry of Tourism and Antiquities, the
Department of Statistics, the World Council for Tourism and
Travel (WCTT), and the Economic and Social Council /
Jordan.
The study is used two main Methods:
1.Koyck geometrical distributed lag model:
The study is based on the hypothesis of a positive effect of
the number of inbound tourists coming to Jordan on tourism
income in Jordan for the years 1990-2021.
The Koyck model is used for distributed lags, assuming that
the coefficients of the lag variable are decreasing as a
geometric progression according to the following law:
Yt= a+ B Xt +B
𝜹𝑿𝒕 − 𝟏 +'
B
𝜹𝟐
Xt-2+ B
𝜹𝟑𝑿𝒕 −𝟏+𝑼𝒕
𝜹
: The rate Decreasing of distributed lags.
(1-
𝜹)
: Adjustment speed
Yt = a
∗
+B NT + Vt
a
∗
= a (1 -
𝜹')
Koyck model assumes that the weights of explanatory

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Natural Sciences Publishing Cor.
variables lagged in time are falling with geometric
progression according to the law as follows:
Bj = B0
'𝜹𝒊
0
< 𝜹 < 𝟏
Yt= B0 +B1 Yt-1 +B2 NT+ Ut.
Yt : Tourism Jordanian Income .
Yt-1: Tourism Income for one lag.
NT: Number of inbound tourists.
2.Autoregressive distributed lag model: (ARDL)
The study analyzed the autoregressive distributed lag
(ARDL) to reveal a long-term relationship between tourism
activity and real GDP, With the fulfillment of the condition
that the studied variables are not stable at the second degree.
The boundary test is used to detect a cointegration, estimate
long-run Coefficients. In addition to estimating the error
correction model, which includes short-term coefficients and
the adjustment speed factor. The appropriate lag periods for
the model are determined based on Akaike Information
Criterian (AIC).
Autoregressive distributed lag model (ARDL) appearing as
follows:
Yt=
𝜶 +
∑
𝜸'𝒚𝒕 − 𝟏
𝒑
𝒊4𝟏
+ ∑ ∑
𝑩𝒊𝒋𝑿𝒋, 𝒕 − 𝟏 + 𝜺𝒕
𝒒
𝒊4𝟎
𝒌
𝒋4𝟏
The equation for testing cointegration as follows:
∆𝒀𝒕
=
𝜶 +'
∑
𝜸𝒊
𝒑>𝟏
𝒊4𝟏
∆𝒀𝒕𝒊
+ ∑ ∑
𝑩𝒊𝒋'∆𝑿𝒋, 𝒕 − 𝒊 +
𝒒>𝟏
𝒊4𝟎
𝒌
𝒋4𝟏
𝑷𝒀𝒕 − 𝟏 +'
∑
𝜹𝑿𝒋, 𝒕 − 𝟏 + 𝜺𝒕
𝒌
𝒋4𝟏
Hypothesis test: Ho:
𝝆 = '𝜹𝒋 = 𝟎
Ha:
𝝆' ≠ 𝜹𝒋' ≠ 𝟎
Diagnostic tests will be used as follows:
Breusch-Godfrey Serial Correlation LM Test.
Heteroskedasticity Test: Breusch-Pagan-Godfrey.
Cusum test and Cusumsq test.
4 Discuss Results
Koyck geometrical distributed lag model:
Yt = a
∗
+B NT+
𝛿
Yt-1+ Vt
a
∗
= a (1 -
𝛿')
Yt= B0 +B1 Yt-1 +B2 NT+ Ut.
Yt = -379.3 + 0.0005 NT + 0.3577 Yt-1
T ( - 2.9) (8.3) (4.6)
P-value (0.007) (0.000) (0.0001)
𝑅E=
0.92 , F-test= 161.5, P-value= 0.000
D.W= 1.29
LONG -RUN IMPACT:
LR =
F∗
(H'>'I')'
= -379.3
The Long -term impact revealed that an increase 10% in
number of tourists leads to decrease in tourism Jordanian
income of 37.9% over the long- term. We can conclude that
the inverse relationship between the number of tourists
inbound to Jordan and tourism income was due to the fact
contribution of inbound tourists related to spending in Jordan
is small. in addition to the fact that many tourists coming to
Jordan are from low-income countries. In this regard, tourists
inbound by large numbers to Jordan after epidemic covid 19,
but their contribution to tourism income is few. However,
some of them stay for a short period of time, which confirms
the decrease in tourism income despite the increase in
arrivals. This result contradicts the research hypothesis,
which indicates a positive direct relationship between the
number of inbound tourists to Jordan and tourism income.
SHORT -RUN IMPACT
The results showed that the impact of the number of tourists
coming to Jordan on tourism income in the short term is
poor. An increase in the number of tourists by 0.10 leads to
an increase in tourism income by (0.00005), which is a very
low percentage. It is inferred from this that there is a weak
relationship between the number of tourists and tourism
income in the short term.
Some indicators of short-term impact
1.The median lag: It represents the time required to (50%)
half the change of the dependent variable represented in the
tourism income resulting from the change of the independent
variable (number of tourists) by one unit. The smaller value
(
𝛿
) means the greater the speed of the Adjustment.
Median Lag=
JKL'E
JKLI
= 0.84= Equivalent to 0.8 of one month,
approximately 25 days.
2. Variance Lag: It expresses the difference between the
actual and estimated value for tourism income.

Appl. Math. Inf. Sci. 17, No. 1, 161-174 (2023)/ http://www.naturalspublishing.com/Journals.asp 167
© 2023 NSP
Natural Sciences Publishing Cor.
VL=
I
H>'I²
=
N.PQRR
H>N.HES ='
0.41, It is 0.41 of one month, and the
variance of lag period is 12.3 days.
3. Average Lag: It measures the speed of Adjustment to the
change in the number of tourists, which is equal to 0.56 of
one month, or about 16.7 days.
AL=
I
H>I
= 0.56
4. Constant (Intercept)
𝑎
∗ =
U
(H>'V)'
=
>PRW.P
H>N.PQRR
= - 590.5
5. Weights of lag variables: we can calculate weights of lag
variables as follows:
Wi =
(1 − ∂)Z
, W1= 0.64, W2=0.41
The number of Tourists contributes 105% to tourism income
change through two years.
The Koyck model was estimated as follows:
Yt = - 590.5 + 0.0005 NT+0.6423Yt-1+0.4125 Yt-2 + Vt.
Autoregressive distributed lag model: (ARDL)
Time series Stability test: (Unit Root test) and Co-
Integration test:
The study used the Dickey-Fueler tests and the Philips
Berron test to investigate the stability of the data studied in
the time series from (1990-2021). The results of the two tests
showed that the calculated values of Mackinnon (1996)
calculated for the study variables suffer from the problem of
a unit root, that the data stabilized at the first difference,
except for tourism income, which stabilized at the level. The
study uses the (ARDL) methodology, as it does not require
the stability of the data at the same degree, and it did not
stabilize at the second difference.
To determine the degrees of Lags (model rank) the study
used (AIC) indicator. The lowest value for this test is
(4,0,0,0). The long-run Equilibrium relationships are
obtained after the cointegration test using the bound tests,
which computed F-statistic value and compared with the
critical F-statistic value developed by (Pesaran et al 2001).
The study is applied Bound tests according to Table (2), the
calculated F value is greater than the upper bound value of
the critical F value, hence the null hypothesis which states
that there is no co-integration is rejected, meaning that there
is a long-term equilibrium relationship between the
variables. The study is investigated that there is a logical co-
integration relationship, based on t bound test. The finding
revealed that calculated t value is greater than the upper
bound of the critical t value, meaning that there is a usual
logical co-integration relationship.
Table (1) stationary test according to Dickey-Fueler Test
and Philips Berron (P-P) Test.
Dickey-Fueler tests (ADF)
Test
Philips Berron (P-P) Test
Variable
Stability
Rank
Variable
Stability
Rank
RGDP
I(1)
RGDP
I(1)
Yt
I(0)
Yt
I(1)
Nb
I(1)
Nb
I(1)
Capital
I(1)
Capital
I(1)
Source: Author’s own calculation in E-Views 12.
Table (2) Boundary Tests – Co-Integration Test using
(ARDL –Bound test).
Result
Probability
F-Statistics
There is
cointegration
0.0000
10.57
Critical value
F-Statistics
K=3
I(1) Upper value
I(0) Lower value
Significant level
4.45
3.47
10%
5.07
4.01
5%
5.62
4.52
2.5%
6.36
5.17
1%
Result
Probability
T-Statistics
There is a Logic
cointegration
0.0000
- 6.99
I(1) Upper value
I(0) Lower value
Significant level
-3.84
-3.13
10%
-4.16
-3.41
5%
-4.42
-3.65
2.5%
-4.73
-3.96
1%
Source: Calculation of the researchers using E-views 12
software.

168 M. Saleh et. al. : The Competitiveness of Jordanian Tourism …
© 2023 NSP
Natural Sciences Publishing Cor.
Table (3) Results of estimating the model Parameters in
the long term for the (ARDL).
Probability
T-
Statistic
S.E
coefficient
Variable
0.001
3.74
0.07
0.2626
Yt
(Tourism
Income)
0.047
2.11
0.007
0.0167
Nb (Bet
Number)
0.038
2.22
0.006
0.0145
Capital
0.000
5.186
125.36
650.2
C
0.000
4.427
17.458
77.29
trend
0.000
17.05
F-
Statistics
0.79
R²
1.91
D.W
Source: Calculation of the researchers using E-views 12
software.
Table (4) Error Correction Model and short run
Coefficients .
Prob
t-
statistics
Standard
error
Coefficient
Variables
0.000
7.28
89.29
650.2
C
0.000
6.69
11.54
77.29
Trend
0.006
3.02
0.12
0.364
D (RGDP
(-1))
0.006
3.759
0.141
0.431
D (RGDP
(-2))
0.0013
3.759
0.146
0.549
D (RGDP
(-3))
0.0000
-6.99
0.0539
-0.3772
ECT
Source: Author’s own calculation in E-Views 12.
Error Correction model and Long- term Relations:
There is a positive direct relationship in the long term at a
level of significance of 1% between the real GDP and all the
independent explanatory variables, which are: tourism
income, number of beds, and fixed assets in the tourism
sector.
The results of the test showed that the coefficient of speed
adjustment in the real GDP equation was significant at the
level of 1%, and it appeared with the expected negative sign,
which confirms a long-term equilibrium relationship
between growth and the explanatory variables. This
coefficient indicates that the speed of adjustment of the
imbalance state is about 37% during the next periods.
The overall explanatory power of the model was high,
through the value of the coefficient of determination and the
modified coefficient of determination. The results showed
that the value of the Durbin-Watson coefficient is equal 1.91
close to 2, which indicates that the model is free from the
problem of autocorrelation. The F-test showed that the
model is statistically acceptable at the 1% level of
significance.
Diagnostic tests:
1) Breusch – Godfrey LM test: According to Breusch
– Godfrey test, we don’t reject null-Hypothesis
which indicates no serial correlation between
random errors. The Probability of F-statistics was
0.57 and chi-square o.41.
2) Heteroskedasticity test: According to Breusch-
Pagan- Godfrey test, to examine Heteroskedasticity
problem, we don’t reject Null-Hypothesis which
indicates Homoskedasticity as the the probability of
F-statistics test 0.48, and chi-square 0.63.
3) Ramsey Rest test: This test revealed that the model
is free from the problem of inappropriateness of the
functional shape, as the Probability of T-statistic
reached 0.50 and the probability of F-statistics test
0.50.
4) Structural stability test:
A) Cumulative sum of residuals test (Cusum):
B) Cumulative sum of residual squares test (
CUSUMSQ)
These two tests demonstrated any structural change in the
data over time, Investigating the consistency and stability of
long-term parameters with short-term parameters. It is
observed that the regression line passes between the two
critical region boundary lines, indicating the stability of the
model with a significant boundary of 5%. It is inferred from
the figure that the estimated coefficients of the ARDL model
used are structurally stable, which indicates the stability
between the study variables. In addition to emphasizing
consistency in the model between error correction results in
the short and long periods.

Appl. Math. Inf. Sci. 17, No. 1, 161-174 (2023)/ http://www.naturalspublishing.com/Journals.asp 169
© 2023 NSP
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Figure (1): Structural stability test: (Recursive Residuals)
Figure (3): Structural stability test: Cumulative sum of
residual squares test ( CUSUMSQ)
Figure (2) : Structural stability test: Cumulative sum of
residuals test (Cusum):
Figure (4): Test the predictive performance of the estimated
unconstrained error correction model:
The quality of the Predicted results depends on the strength
of the forecast performance of the unconstrained error
correction model. One of the most important measures of
predictive performance is the unequal coefficient proposed
by Theil, which indicates that if the value of Theil coefficient
is zero, this indicates the model's significant predictive
power. If the parameter value is greater than one, this
indicates a decrease in the predictability of the model. The
Thiel coefficient according to the data of the study is less
than one and close to zero, which means that the model has
a high predictability.
-200
-100
0
100
200
300
2004 2006 2008 2010 2012 2014 2016 2018 2020
Recursive Residuals ± 2 S.E.
-0.4
0.0
0.4
0.8
1.2
1.6
2004 2006 2008 2010 2012 2014 2016 2018 2020
CUSUM of Squares 5% Significance
-15
-10
-5
0
5
10
15
2004 2006 2008 2010 2012 2014 2016 2018 2020
CUSUM 5% Significance
3,000
4,000
5,000
6,000
7,000
8,000
9,000
10,000
11,000
12,000
94 96 98 00 02 04 06 08 10 12 14 16 18 20
RGDPF ± 2 S.E .
Forecas t: RGDPF
Actual: RGDP
Forecas t sam ple: 1990 2021
Adjusted s am ple: 1994 2021
Included obs ervations : 28
Root Mean Squared Error 206.4220
Mean Absolute Error 171.8370
Mean Abs. Percent Error 2.299783
Theil Ine quali ty Coef. 0.013484
Bias Proportion 0.026104
Variance Proportion 0.053539
Covariance Proportion 0.920357
Theil U2 Coefficient 0.608009
Symmetric MAPE 2.291183

170 M. Saleh et. al. : The Competitiveness of Jordanian Tourism …
© 2023 NSP
Natural Sciences Publishing Cor.
Impulse Response Function:
The reaction response function traces the time course of
various sudden shocks that may occur to the variables
included in the study. It reflects how each variable responds
to any sudden shock in any variable in the model over time.
Figure (5) shows the reaction response of the real GDP to a
sudden change of one standard deviation in each of: tourism
income, number of beds in the Jordanian tourism sector,
capital represented by fixed assets in the tourism sector. It is
clear from the figure that the impact of tourism income on
real GDP is negative, as any sudden change of one standard
deviation in tourism income negatively affects real GDP. It
is inferred from this that the contribution of tourism income
to economic growth is modest, as most of the tourists coming
to Jordan are from low-income countries, and their tourism
spending in Jordan is small, which means that tourism
income is small. On the other hand, we found the effect of
the number of beds in hotels and various tourist
accommodation establishments positively on the real GDP.
As any sudden change in the number of beds of one standard
deviation will positively affect the real GDP, but this effect
is not direct, but appears after three years. Finally, the impact
of fixed assets in the tourism sector, represented by capital,
on the real GDP is negative, and it appears after nearly three
years, and it continues for a long time.
Figure (5) Response of the Real GDP to a sudden change of
one standard deviation in each of: tourism income (YT),
number of beds in the Jordanian tourism sector (NB), and
capital (fixed assets) in the tourism sector (KK)
-600
-400
-200
0
200
1 2 3 4 5 6 7 8 9 10
Respon se of RG DP to YT Innovation
-600
-400
-200
0
200
1 2 3 4 5 6 7 8 9 10
Respon se of RG DP to NB Inno vatio n
-600
-400
-200
0
200
1 2 3 4 5 6 7 8 9 10
Respon se of RG DP to KK Innovatio n
Response to Cholesky One S.D. (d.f. adjusted) Innovations
± 2 analytic asymptotic S.E.s

Appl. Math. Inf. Sci. 17, No. 1, 161-174 (2023)/ http://www.naturalspublishing.com/Journals.asp 171
© 2023 NSP
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Table (5) Granger Causality
Source: Author’s own calculation in E-Views 12.
The results of Granger's causality test showed a bidirectional
relationship between tourism income and economic growth.
This reciprocal relationship is consistent with the economic
logic, where the increase in tourism income contributes to
the increase in the gross domestic product, and on the other
hand, economic growth contributes to the development of
the tourism sector. This result corresponds with the opinions
of [34] who support the assumption refer to feedback-
hypothesis’ in BRICS countries. These all the studies
advocate the bi-directional relationship. It differs with the
result of [57] as revealed that Unidirectional Causality, In
Contrast, [47] concluded Weak impact of tourism on
economic growth in Nepal. The hypothesis of tourism-led
growth for Nepal has been rejected. We are concluded
through the economic literature that the causal relationship
between tourism income and growth changes over time in
the short and long term, which was confirmed by [58].
The results showed Unidirectional relationship between the
number of beds available for various tourist accommodation
establishments in Jordan and economic growth, but this
unidirectional relationship tends from economic growth to
the number of beds, as the effects of steady economic growth
will be reflected in the increase in various investments in
tourism activity, as it increases the number of hotels, the
increase in the number of beds, and various other
accommodation establishments. On the other hand, the
results showed a neutral relationship between the fixed assets
of tourism activity and economic growth. As there is no
relationship between the fixed assets in the tourism sector
and economic growth because the tourism activity is one of
the service sectors that depend little on machines and fixed
assets, in contrast to the industrial sector.
5 Conclusion and Suggestion
The study was presented to highlight the competitive aspects
of the Jordanian tourism activity using some econometric
models. Time series data collected through the Department
of Statistics, the Central Bank of Jordan, and the Ministry of
Tourism for the period 1990-2021 were used. The study
showed the impact of tourism activity on employment,
balance of payments and reducing poverty spots, in addition
to supporting economic diversification. The study used the
Kwik distributed gap method to measure the effect of the
number of tourists coming to Jordan on the Jordanian
tourism income. The study revealed the low impact of the
number of tourists on Jordanian tourism income.
Furthermore, the study revealed that there is long
relationship between independent variables with dependent
variable based on cointegration ARDL bound test. The
correction of coefficient error related to adjustment speed is
showed that the deviations for total short-term are corrected
to their long-term equilibrium value with a correction rate
equivalent to (37%). The Granger causality test showed a
bidirectional between tourism income and economic growth,
a Unidirectional relationship between the number of beds
available in the tourism sector and economic growth, and a
neutral relationship between the fixed assets of tourism
activity and economic growth in Jordan. When validating
diagnostic tests, the validity of the results was measured by
serial correlation, normality, and heterogeneity. Finally, the
performance test of the error-correcting model found that the
Thiel parameter is less than one and close to zero, which
indicates the model's high predictability.
The study recommends increasing focus and targeting
tourism activity in Jordan by decision makers for the vital
role it plays in influencing economic growth. The
responsibility of policy makers in the sector is to raise the
competitiveness of the sector by promoting the attraction of
tourists from different countries of the world, especially
from high-income countries, and expanding tourism
investments and support services.
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