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1516

JOSS :

Journal of Social Science

EFFECT OF THE TOURISM SECTOR ON ECONOMIC GROWTH IN SOLO

RAYA, CENTRAL JAVA PROVINCE

Katno

, Heru Subiyantoro

Universitas Borobudur, Indonesia

E-mail: [email protected]

, [email protected]

KEYWORDS

number of tourist

visits; hotel

occupancy rates; FDI;

economic growth

ABSTRACT

This study expects to examine the impact of vacationer visits, lodging

inhabitance rates, and unfamiliar direct speculation (FDI) on the monetary

development of Solo Raya from 2013 to 2022. Financial development

implies changes in the economy of a district. The exploration uses auxiliary

information from 7 locale/urban communities, specifically Surakarta City,

Boyolali Rule, Sukoharjo Regime, Karanganyar Rule, Wonogiri Regime,

Sragen Rule, and Klaten Regime, handled through board information

investigation utilizing the Decent Impact Model (FEM) technique. The

consequences of this study show that, to some extent, the quantity of

vacationers has a positive and huge relationship with monetary

development, lodging inhabitance rates likewise have a positive and critical

relationship with financial development, and FDI has a positive and critical

relationship with monetary development. Simultaneously, the number of

tourist visits, hotel occupancy rates, and FDI collectively influence

Economic Growth in the Java Island Province from 2013 to 2022.

INTRODUCTION

The tourism sector, as a prospective industry, can be developed to drive economic

progress and is a national development program that is always a concern for both central and

regional governments, as well as being one of the government's mainstays in assisting in the

recovery of economic crises (Ashoer et al., 2021). According to Wahab (1975), tourism as a

new industry can create economic growth that provides employment opportunities, earns

foreign exchange, improves living standards, and has the potential to boost other sectors

(Wahab, 1975). Indonesia's recognized tourism presence globally undoubtedly has the potential

and diversity of tourism ranging from natural, maritime, and socio-cultural tourism, spanning

from Sabang to Merauke (Devy & Soemanto, 2017). However, according to Nugroho (2020),

the tourism industry in Indonesia still faces various issues, including inadequate infrastructure

supporting tourism sector development, insufficient investment in the tourism sector, and

insufficient attention to environmental aspects in tourism development (Sumarjiyanto, 2020).

Economic development in regions through tourism industry development will have a positive

impact on regional economic growth conditions. According to (Sukirno, 2004), economic

growth is the development of activities that affect the economic growth of the production of

goods and services within society.

Based on the background outlined above and indications of a research gap, strengths, and

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weaknesses in previous research, the researcher has a novelty in conducting research by

combining several tourism variables as independent variables and updating the latest data to

see their impact on regional economic growth using the Gross Regional Domestic Product

(GRDP) growth rate indicator. This research is also expected to assist the government in

determining a better direction in helping to improve economic growth in the tourism sector.

Therefore, the researcher chose the title "The Influence of the Tourism Sector on Economic

Growth in Solo Raya from 2013 to 2022".

The targets of this study are to decide the impact of the quantity of traveler visits on

financial development, the impact of inn inhabitance rates on monetary development, the

impact of unfamiliar direct speculation (FDI) on monetary development, and to decide the

concurrent impact of the quantity of vacationer visits, hotel occupancy rates, and FDI on the

economic growth of Solo Raya from 2013 to 2022.

Literatur review

Economic Growth

Economic growth is an important indicator for analyzing the economic development of a

country. An economy is said to experience economic growth when there is an increase in goods

and services (Sukirno, 2004). The tool used to determine economic growth is called Gross

Regional Domestic Product (GRDP). GRDP is the gross value added (goods and services)

produced by production activities in a region during a certain period, usually one year.

There are several theories related to economic growth, such as Keynesian growth and

neoclassical growth theory. According to (Stabler et al., 2009), the examination of the

connection among the travel industry and financial development includes clashing hypothetical

viewpoints. As indicated by Keynesian development hypothesis, the travel industry creates

expanded request, which drives venture and higher pay. Conversely, neoclassical development

hypothesis suggests that the development rate isn't straightforwardly impacted on the grounds

that travel industry is probably not going to assume a huge part in expanding work, capital, or

mechanical advancement, in spite of the fact that it gives extra unfamiliar money that can be

utilized to increment capital stock.

In Keynesian economics, growth refers to the idea that increased spending (consumption)

in the economy will increase income, which then leads to more spending and income.

According to Mankiw (Mankiw, 2007), Keynes' theory relates that one person's consumption

in the economy becomes another person's income in the same economy (Ahman & Indriani,

2007), stated that in measuring the economic development of a country or region, it can be

measured by the economic growth achieved by that region in a year.

Tourism Sector

According to (Goeldner & Ritchie, 2007), tourism is a combination of activities, services,

and industries that provide travel experiences such as accommodation, dining, entertainment,

activity facilities, and other hotel services available to individuals or groups traveling far from

home. This includes all service providers related to tourists. According to Salah Wahab (1975),

tourism as a new industry can create economic growth that provides employment opportunities,

earns foreign exchange, improves living standards, and has the potential to boost other sectors

(Wahab, 1975). The relationship between tourism and economic growth is closely related

because tourism can be a double multiplier that can accelerate and drive economic growth

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itself. This is because tourism creates demand for both consumption and investment, which can

lead to the production of goods and services (Mukaffi & Haryanto, 2022).

The number of tourist visits is a measure of success in the tourism industry that impacts

the local community and local governments. According to (Ashoer et al., 2021), tourists are

visitors who stay temporarily in the place they visit for at least 24 hours, motivated to travel

only for vacation, leisure, health, learning, religious, sports, family visits, conferences, and

specific missions. Occupancy rates are one of the factors in calculating hotel revenue.

According to (Damardjati, 1981), hotel occupancy rate or hotel occupancy is the number of

rooms occupied by guests or tourists compared to the number of rooms available, calculated

daily, monthly, or annually. Investment is one of the key drivers in the economy because

investment can increase value-added in economic activities. Economic growth or GRDP cannot

be separated from investment as a trigger for increasing production capacity that impacts

economic output (Sukirno, 2004).

Tourism factors such as increasing tourist numbers will increase demand for goods and

services. With investment procurement, it is possible to increase the number of hotels and

restaurants, so that the increasing number of hotels can cause the occupancy rate to become

relatively high, then affecting economic growth. The growing number of hotels and the high

hotel occupancy rate, followed by the high number of tourists, will affect the economic growth

of Solo Raya.

Hypotheses are temporary answers to be taken in addressing the problems formulated in

the research, which are actually being tested and will be empirically proven using related data.

Based on the formulation of the previous problems, the hypotheses are as follows:

a. The quantity of vacationer visits affects financial development in the Java Island Territory

from 2013 to 2022.

b. Inn inhabitance rates meaningfully affect financial development in the Java Island Area

from 2013 to 2022.

c. Unfamiliar direct venture (FDI) affects financial development in the Java Island Area from

2013 to 2022.

d. The number of tourist visits, hotel occupancy rates, and FDI simultaneously affect

monetary development in the Java Island Province from 2013 to 2022.

METHOD RESEARCH

This study analyzes the influence of the number of tourist visits, hotel occupancy rates,

also, unfamiliar direct speculation (FDI) on monetary development in Independent Raya from

2013 to 2022. The reliant variable utilized in this study is Monetary Development (Y), and the

free factors used are the number of tourist visits (X1), hotel occupancy rates (X2), and FDI

(X3). This research employs a quantitative approach by developing previous research

conducted by (Putra et al., 2021). The scientific strategy utilized in this study is the board

information relapse examination technique. This examination plans to decide the degree of the

impact of the reliant factors on the autonomous factors. In this review, the information acquired

are optional as board information, to be specific cross-segment and time series. The board

information utilized are from the regions in Independent Raya from 2013 to 2023. The

examination objects incorporate 7 areas/urban communities, to be specific Surakarta City,

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Boyolali Regime, Sukoharjo Rule, Karanganyar Regime, Wonogiri Regime, Sragen Regime,

and Klaten Regime. The information utilized in this study are optional information gotten from

distributions of the Focal Measurements Organization, and the Tourism Office. The acquired

data are then processed for analysis to determine the research outcomes. According to

(Sugiyono, 2016), secondary data are data sources obtained indirectly or through

intermediaries, obtained and recorded from other parties, such as historical records or reports

in published archives. The data collection method used is the Library Research method, which

involves gathering data by searching for information from journals, institution or agency

reports, books, literature, and others that meet the required criteria to obtain accurate and valid

information. This study utilizes software or computer application analysis techniques, with the

assistance of the Eviews 12 program.

Panel Data Model

In this study, the researcher employs panel data analysis. Panel data is a combination of

data across regions and time series data. The time series data nature is indicated by data

consisting of more than one entity, while the time series nature is indicated by each individual

having more than one-time observation. (Gujarati & Porter, 2012) states that the use of panel

data has several advantages, such as the combination of time series and cross-sectional data

providing more informative and diverse data, as well as reducing collinearity among variables,

increasing degrees of freedom, and efficiency. Panel data is a type of data collected over a

specific period.

The basic model to be used in this study is as follows:

Y = β0 + β1X1it + β2X2it + β3X3it + εit

The equation is then transformed into equation form, so the research model is as follows:

PEit = β0 + β1KWit + β2THHit + β3PMAit + εit

Information:

PEit: Economic growth in province i in period t

KWit: Tourist visits in Province i period t

THHit: Occupancy Rate in Province i period t

PMAit: PMA in Province i period t

β0 : Constant

β1, β2, β3: regression coefficients for each independent variable

εit: error term

The analysis approach during board information relapse in this study is led utilizing the

Normal Impact Model (CEM), Fixed Impact Model (FEM), and Irregular Impact Model (REM)

methods. Before applying regression to the panel data, the researcher conducts several model

specification tests to obtain the most appropriate model estimation for use in this study. Model

specification tests include the Chow Test and the Hausman Test. Classical Assumption Tests

conducted include the Normality Test, Autocorrelation Test, Multicollinearity Test, and

Heteroskedasticity Test.

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Hypothesis testing is performed to determine whether the regression coefficients obtained

in this study are significant or not. There are three hypothesis tests conducted in this study,

including the coefficient of determination (R^2), t-statistic Test, and F Test.

RESULTS AND DISCUSSION

The economic growth of districts/cities in Solo Raya shows similarities almost every

year. Similarly, during the COVID-19 pandemic, all districts/cities experienced negative

economic growth or less than zero, as graphically indicated in the following figure.

Figure 1

Regency/City Economic Growth in Solo Raya for the 2012-2023 Period

Detail model tests are utilized to decide the most suitable board information relapse

model to be utilized in this review, whether it's the Normal Impact Model (CEM), Fixed Impact

Model (FEM), or Arbitrary Impact Model (REM). The Chow Test means to decide if the

Normal Impact Model or Fixed Impact Model will be utilized to assess the model. Assuming

the outcome shows that the likelihood worth of F > the importance level α = 5% (0.05), then

H0 is acknowledged, demonstrating that the model to be utilized is the Normal Impact Model.

Assuming the likelihood worth of cross-segment F < the importance level α = 5% (0.05), then

H1 is acknowledged, showing that the model to be utilized is the Proper Impact Model. At the

point when H1 is acknowledged, it should be guaranteed whether the Decent Impact Model is

awesome for assessing the model by leading the Hausman Test.

Table 1

Chow Test

Effects Test

Statistic

d.f.

Prob.

Cross-section F

13.837110

(5,39)

0.0000

Source: Processing results using Eviews 12, 2024.

Given Table 1, the Chow Experimental outcomes demonstrate that the Likelihood esteem

is more modest than the importance level α = 5% (0.0000 < 0.05), in this way measurably, it

very well may be presumed that H1 is acknowledged and H0 is dismissed. In this way, the

chose model is the Decent Impact Model (FEM).

-4

-2

0 2 4 6 8 10 12 14

Kabupaten Klaten Kabupaten Sukoharjo Kabupaten Wonogiri

Kabupaten Karanganyar Kabupaten Sragen Kota Surakarta

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The Hausman Test plans to decide if the Irregular Impact Model or Fixed Impact Model

will be utilized. Assuming the Hausman Experimental outcomes show that the likelihood worth

of the arbitrary cross-segment < the importance level α = 5% (0.05), then, at that point, H1 is

picked, demonstrating that the chose model is the Decent Impact Model. Notwithstanding,

assuming the Hausman Experimental outcomes show that the likelihood worth of the arbitrary

cross-area > the importance level α = 5% (0.05), then H0 is picked, demonstrating that the

model to be chosen in assessing the model is the Irregular Impact Model. In the event that the

picked model is the Irregular Impact Model, the model assessment is performed through the

Lagrange Multiplier Test.

Table 2

Hausman Test

Test Summary

Chi-Sq. Statistic

Chi-Sq. d.f.

Prob.

Cross-section random

74.564479

0.0000

Source: Processing results using Eviews 12, 2024

Based on Table 2, the Chi-Square statistical distribution value is 74.564479 with a

probability value smaller than the significance level α = 5% (0.0000 < 0.05), so statistically it

can be concluded that H1 is accepted and H0 is rejected. So, in the Hausman test the model

chosen is the Fixed Effect Model (FEM).

In the wake of obtain the outcomes from the Chow test and Hausman test, all that model

that can be utilized in this exploration is the Proper Impact Model. Coming up next are the

consequences of board information assessment utilizing the Proper Impact Model.

Tabel 3

Fixed Effect Model

Variable

Coefficient

Prob.

-1,489576

0,0022

LOG_KW

0,179320

0,0059

THH

0,005948

0,0000

LOG_PMA

0,028950

0,0026

R-squared

0,826319

Adjusted R-squared

0,790692

F-statistic

23,19372

Prob(F-statistic)

0,000000

Source: Processing results using Eviews 12, 2024

Based on the results from table 3 above, the regression model equation is as follows:

LOGPE = -1,489576 + 0,179320 LOGKW + 0,005948 THH + 0,028950 LOGPMA + e

Information :

LOGPE = Logarithm of Economic Growth

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LOG KW = Logarithm of the Number of Tourists

THH = Hotel Occupancy Rate

LOG PMA = Logarithm of Foreign Investment

Based on the regression results above, it can be observed that in the coefficient column

C, the value is -1.489576, which means the value of LOGPE is -1.489576 when not influenced

by other variables. For the variable LOGKW, it has a positive relationship with the variable

LOGPE with a value of 0.179320. It can be interpreted as when there is a 1 percent increase in

LOGNumber of Tourist Visits, it will increase LOG Economic Growth by 0.179320%

assuming other variables are constant. The variable LOGNumber of Tourist Visits has a

probability value of 0.0059, indicating that the variable LOGNumber of Tourist Visits

significantly affects the variable LOG Economic Growth because the probability of the

LOGNumber of Tourist Visits variable is less than 0.05.

For the THH variable, it has a positive relationship with the variable LOGPE with a value

of 0.005948. It can be interpreted as when there is an increase of 1 unit in the Hotel Occupancy

Rate, it will increase LOG Economic Growth by 0.005948% assuming other variables are

constant. The Hotel Occupancy Rate variable has a probability value of 0.0000, indicating that

the Hotel Occupancy Rate variable significantly affects the variable LOG Economic Growth

because the probability of the Hotel Occupancy Rate variable is less than 0.05.

For the LOGPMA variable, it has a positive relationship with the variable LOGPE with

a value of 0.028950. It can be interpreted as when there is a 1 percent increase in LOGPMA, it

will increase LOGEconomic Growth by 0.028950% assuming other variables are constant. The

LOGPMA variable has a probability value of 0.00026, indicating that the LOGPMA variable

significantly affects the variable LOGEconomic Growth because the probability of the

LOGPMA variable is less than 0.05.

Classical assumption tests are conducted to ensure the suitability of the regression model

used in this study. The research employs classical assumption tests including ordinariness tests,

multicollinearity tests, heteroskedasticity tests, and autocorrelation tests. In this review, the

model utilized is the Proper Impact Model. Coming up next are the aftereffects of the Old style

Presumption Tests.

The ordinariness test is valuable to analyze regardless of whether the information in the

review are typically disseminated. In the ordinariness test, the factual test utilized in this

examination is the Jarque-Bera test with an importance level of 0.05 or 5% (Ghozali, 2016).

The dynamic premise in the ordinariness test is: in the event that the importance esteem > 0.05,

the information are typically appropriated; in the event that the importance esteem < 0.05, the

information are not regularly dispersed. In light of the information handling results, it is shown

that the likelihood esteem is more noteworthy than the importance level α = 5% (0.469013 >

0.05). In this manner, it tends to be reasoned that the information are ordinarily dispersed.

The multicollinearity test is utilized to decide the relationship between's free factors,

where a decent relapse model is when all independent variables are independent or not

correlated. If the result of the combination of independent variables shows a value <0.8, then

it is considered free from multicollinearity. If it is above 0.80, it may indicate multicollinearity.

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Table 4

Multicollinearity Test Results

LOGKW

THH

LOGPMA

LOGKW

1,000000

0,293847

0,085105

THH

0,293847

1,000000

0,281218

LOGPMA

0,085105

0,281218

1,000000

Source: Processing results using Eviews 12, 2024

Based on Table 4, the results of the multicollinearity test show the correlation value

between LOGKW and THH is 0.293847. The correlation value between LOGKW and

LOGPMA is 0.085105. The correlation value between THH and LOGPMA is 0.281218. It is

known that all of these data results are less than 0.80 (<0.80). Thus, it can be concluded that

there is no multicollinearity issue.

Heteroskedasticity testing aims to determine whether there is equality or difference in the

variance of residuals from one observation to another. In this study, the Glejser test is used.

According to (Gujarati & Porter, 2012), the Glejser test proposes relapsing indisputably the

remaining worth on the free factor. The relapse model is said to not contain heteroskedasticity

assuming the importance likelihood is over the 5% certainty level or > 0.05.

Table 5

Heteroscedasticity Test Results

Variable

Prob.

Information

LOGKW

0,1043

Heteroscedasticity does not occur

THH

0,6774

Heteroscedasticity does not occur

LOGPMA

0,0886

Heteroscedasticity does not occur

Source: Processing results using Eviews 12, 2024

Based on Table 5, the results of the heteroskedasticity test using the Glejser test show

that there is no heteroskedasticity. This is because the probability value of each independent

variable is greater than 0.05.

The autocorrelation test is intended to examine whether there is a relationship between

the errors of the current year's research model and the previous year. Autocorrelation occurs

because consecutive observations over time are related to each other. It is found in time series

data because specific observations are influenced by previous observations (Ghozali, 2016). In

this study, it is proven using the Durbin-Watson value method found in DU and 4-DU, to

determine whether there is autocorrelation or not.

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Table 6

Autocorrelation Test

1,622323

1,5064

1,6708

4-DU

2,3292

4-DL

2,5936

Source: Data Processing Results Using Eviews, 2022

Based on the results from the table, since the value of DW > DL, there is no

autocorrelation in the model, thus it can be assumed that there is no autocorrelation issue in the

model. According to (Widarjono, 2007), the autocorrelation issue can be addressed by taking

the first difference from the base level to level 1.

Hypothesis Test

Hypothesis testing is conducted to determine whether the regression coefficients

obtained in this study are significant or not. There are three hypothesis tests conducted in this

study, namely the t-statistic test, the F-statistic test, and the coefficient of determination test.

In this study, the best model selected is the Fixed Effect Model.

T-Statistic Test (Partial Test)

The T-measurement test is utilized to show how one autonomous variable separately

impacts the reliant variable (Ghozali, 2016). The motivation behind this test is to decide if

every autonomous variable essentially influences the reliant variable. The measures used to

decide if a variable makes a huge difference or not is by comparing the probability level with

the significance level α = 5% or 0.05. If the probability result is < 0.05, then each tested

independent variable has a significant effect, meaning accepting 𝐻1 and rejecting 𝐻0.

However, if the probability result is > 0.05, then each tested independent variable does not have

a significant effect, meaning accepting 𝐻0 and rejecting 𝐻1. The following are the hypotheses

in the T-Statistic Test or Partial Test:

1. 𝐻0: There is no influence of the number of tourists on economic growth in Solo Raya 2013-

2022.

2. 𝐻1: There is an influence of the number of tourists on Solo Raya's economic growth in

2013-2022.

3. 𝐻0: There is no influence on the Hotel Occupancy Rate on Economic Growth in Solo Raya

in 2013-2022.

4. 𝐻1 : There is an influence of Hotel Occupancy Rates on Economic Growth in Solo Raya in

2013-2022.

5. 𝐻0: There is no influence of FDI on Economic Growth in Solo Raya in 2013-2022.

6. 𝐻1: There is an influence of FDI on Economic Growth in Solo Raya in 2013-2022.

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Table 7

T-Statistics Test Results

Variable

Coefficient

Std. Error

t-Statistic

Prob.

-1.489.576

0.454582

-3.276.806

0.0022

LOGKW

0.179320

0.061603

2.910.874

0.0069

THH

0.005948

0.000731

8.135.379

0.0000

LOGPMA

0.028950

0.008986

3.221.527

0.0036

Source: Data Processing Results Using Eviews 12, 2024

The explanation of table 7 above is as follows:

1. For the variable of tourist arrivals, the t-statistic probability result is 0.0069. This result has

a value greater than the significance level α = 5% (0.0069 > 0.05). Accordingly, it very well

may be inferred that 𝐻0 is dismissed and 𝐻1 is acknowledged. This implies that the variable

of vacationer appearances makes a halfway difference on Economic Growth in Solo Raya

from 2013 to 2022.

2. For the variable of hotel occupancy rate, the t-statistic probability result is 0.0000. This

result has a value smaller than the significance level α = 5% (0.0000 < 0.05). Subsequently,

it tends to be inferred that 𝐻1 is acknowledged and 𝐻0 is dismissed. This implies that the

variable of inn inhabitance rate to some degree affects Monetary Development in

Independent Raya from 2012 to 2020.

3. For the PMA variable, the t-statistic probability result is 0.0036. This result has a value

smaller than the significance level α = 5% (0.0036 < 0.05). Accordingly, it very well may

be inferred that 𝐻0 is dismissed and 𝐻1 is acknowledged. This implies that the speculation

variable makes an incomplete difference on Economic Growth in Solo Raya from 2013 to

2022.

Simultaneous Test (F Test)

The F Test is conducted to determine whether all independent variables collectively

influence the dependent variable simultaneously (Ghozali, 2016). To determine whether there

is a simultaneous effect, the calculated F value is compared with the F table value. The criterion

used to determine whether all variables have a significant effect or not is by comparing the

probability level with the significance level α = 5% or 0.05. If the probability result is less than

0.05, then all tested independent variables have a significant effect, which means 𝐻1 is

accepted and 𝐻0 is rejected. However, if the probability result is greater than 0.05, then all

tested independent variables do not have a significant effect, which means 𝐻0 is accepted and

𝐻1 is rejected. Below are the hypotheses for the F-Test or Simultaneous Test:

1. 𝐻0: There is no simultaneous influence on the number of tourist visits, hotel occupancy

rates, FDI on economic growth in Solo Raya in 2013-2022.

2. 𝐻1: There is a simultaneous influence of the number of tourist visits, hotel occupancy rates,

FDI on economic growth in Solo Raya in 2013-2022.

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Table 8

Simultaneous Test Results

F-statistic

24,19372

Prob(F-statistic)

0,000000

Source: Processing results using Eviews 12, 2024

Based on the Fixed Effect Model regression results in Table 4.11 above, the F-statistic

value is 24.19372. Looking at the F-Statistic Probability value shows a value of 0.00000. In

this way, the F-Measurement Likelihood esteem is more modest than the importance level α =

5% (0.000000 < 0.05) so acknowledge 𝐻1 and reject 𝐻0, so it very well may be reasoned that

the free factors (Number of Traveler Visits, Inn Inhabitance Rate, venture) all the while affects

the reliant variable (Monetary Development) in Independent Raya 2013-2022.

Coefficient of Determination

The coefficient of assurance makes sense of how enormous the extent of the free factor

is in making sense of the reliant variable utilized in the exploration. (R2) is the size or level of

the absolute variety in the autonomous factors made sense of by the relapse model. Detect the

coefficient of determination by looking at the adjusted (R2) value in the regression output. The

conditions for testing (R2) are:

1. On the off chance that the changed (R2) esteem is near no the capacity of the autonomous

variable to make sense of the reliant variable is restricted.

2. In the event that the changed (R2) esteem is near one practically all the data expected to

anticipate the reliant variable can be made sense of by the autonomous variable.

Table 9

Results of Determination Coefficient

R-squared

0,836319

Adjusted R-squared

0,699429

Source: Data Processing Results Using Eviews 12, 2024

In light of the aftereffects of the coefficient of assurance test in Table 4.12, shows that

the Changed R-squared esteem is 0.699429. Accordingly, it tends to be presumed that the

autonomous factors (Number of Travelers, Inn Inhabitance Rate, PMA) can make sense of the

reliant variable (Monetary Development) in Independent Raya by 69.9429% or 69%. The

leftover 39% is made sense of by different factors beyond this review.

The consequences of this study show that the Quantity of Travelers has a positive

relationship with Economic Growth in Solo Raya from 2013 to 2022. Looking at the coefficient

value of the Number of Tourists variable, which is 0.179320, it means that an increase in the

number of tourists can increase Economic Growth in the Province on the Island of Java by

0.179320%. The number of tourists has a significant effect on Economic Growth in Solo Raya.

This result suggests that the number of tourists can influence economic growth.

The number of tourist visits is a measure of success in the tourism industry that impacts

the local community and the local government. Therefore, when the number of tourists

increases, it can drive all sectors of the economy to grow in a positive and productive direction.

This is consistent with research conducted by I Gede Dea, et al., (2021), stating that a high

number of tourist visits will have a positive effect as it provides opportunities for transactions

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when visiting tourist attractions to help boost the local economy and potentially increase the

country's foreign exchange contributions (Putra et al., 2021). Thus, the activities undertaken

influence economic recovery and support the growth of the local economy towards a better

direction. This research aligns with Salah Wahab's (1975) perspective, explaining that tourism,

as a new industry, can create economic growth that provides employment opportunities, earns

foreign exchange, improves living standards, and potentially boosts other sectors (Wahab,

1975).

The results of this study show that the Hotel Occupancy Rate has a positive relationship

with Economic Growth in Solo Raya from 2013 to 2022. Looking at the coefficient value of

the Hotel Occupancy Rate variable, which is 0.005948, it can be interpreted that an increase in

the hotel occupancy rate can increase the economic growth of Solo Raya by 0.005948%. The

hotel occupancy rate variable has a significant effect on the economic growth of Solo Raya, as

evidenced by the T-Statistic probability value of 0.0000, which is smaller than the significance

level α = 5%. The result suggests that the hotel occupancy rate can influence economic growth.

In the tourism industry, especially activities related to accommodation such as hotels,

revenue will increase significantly if tourists stay longer. It is consistent with research

conducted by (Amaliyah, 2022), which reveals that when hotel occupancy is high, it will

increase hotel taxes and revenue, thereby increasing local income through hotel taxes, which

in turn will boost economic growth. It means that the presence of hotels in Solo Raya is highly

favored by the community (tourists), so the increasing hotel occupancy rates due to many

visitors, impacts the economic growth.

However, in contrast to the study by Nurhasan et al. (2023), which explains that there is

no influence of hotel occupancy rates on economic growth because available accommodation

is sometimes rarely occupied, as most visitors are local tourists who prefer to stay with relatives

or family due to the relatively expensive hotel rental costs according to local tourists, thus not

affecting economic growth (Nurhasan et al., 2023).

The results of this study show that investment has a positive relationship with economic

growth in Solo Raya from 2013 to 2022. Looking at the coefficient value of the investment

variable (PMA), which is 0.028950, it can be interpreted that an increase in investment can

increase the economic growth of Solo Raya by 0.028950%. PMA has a significant effect on

the economic growth of Solo Raya, as evidenced by the T-Statistic probability value of 0.0026,

which is smaller than the significance level α = 5%. This result suggests that investment can

influence economic growth.

Investment is a crucial driver in the economy because investment can increase added

value in economic activities. It is in line with (Stabler et al., 2009) namely according to

Keynesian growth theory where tourism can generate increased demand, which encourages

investment and higher income. This theory states that increasing production of goods and

services can cause an increase in GDP. Harrod Domar's theory states the need for capital

investment to create economic growth as an indicator for achieving strong economic growth.

The results of this research are in line with research conducted by (Kartana & Dewi, n.d.), serta

(Asmari & Sutrisna, 2021), where the amount of investment in the form of PMA and PMDN

can influence economic growth. The dynamics of capital investment can influence the highs

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and lows of economic growth and reflect the rise or fall of the economy to grow the economy

of each country and region to create a climate that can stimulate investment.

CONCLUSION

In view of the exploration results, it very well may be reasoned that the quantity of

traveler visits altogether affects Financial Development in Independent Raya from 2013 to

2022. Lodging inhabitance rates fundamentally affect Monetary Development in Independent

Raya in 2013-2022. PMA (Unfamiliar Direct Venture) fundamentally affected Financial

Development in Independent Raya from 2013-2022. Furthermore, the number of tourist visits,

hotel occupancy rates, and investment collectively or simultaneously influenced the economic

growth in Solo Raya from 2013 to 2022.

The tourism industry must enhance its contribution to the Gross Regional Domestic

Product (GRDP) by leveraging tourism potentials, and offering unique experiences to attract

tourists who wish to stay in hotels, thereby contributing through hotel occupancy rates to

regional economic growth. It is hoped that stakeholders involved in tourism development and

enhancement can improve the investment climate more effectively and innovatively to attract

investors and maintain a conducive investment environment. Moreover, developing new

market segments (ecotourism, cultural tourism, etc.) can attract investors.

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Katno

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(2024)

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