https://ijmri.de/index.php/jmsi
volume 4, issue 5, 2025
1287
THE ROLE OF THE TOURISM INDUSTRY IN UNEMPLOYMENT
Khoshimjon Khamidovich Nurullaev
Senior Lecturer at the Department of Economics and Tourism,
Oriental University
hoshimnurullaev@gmail. com
Abstract:
This study examines the impact of the tourism industry on unemployment for 6
countries, namely Turkey, Greece, Italy, Spain, Egypt and Cyprus, over the period 2003-2022.
Over the past 40 years, the number of tourists visiting other countries has increased 20 times,
tourism revenues have increased 60 times, and international tourism revenues have reached $400
billion.
Keywords:
Unemployment, tourism, tourism revenue, ECCU method, Bruce-Godfrey test,
scattergram
Introduction
In the developing world, people's worldview, thoughts, and ways of enjoying the world are
changing. Individuals increasingly seek to travel, explore historical monuments, and discover the
traditions and values of other nations as a way to spend their leisure time meaningfully [1].
Today, tourism stands out as one of the fastest-growing and most significant industries in terms
of income generation [2]. In recent years, tourism has made a substantial contribution to world
exports, accounting for approximately 11% of global gross domestic product (GDP) [3]. Over
the past 40 years, the number of tourists visiting foreign countries has increased twentyfold,
while tourism revenues have grown sixtyfold, with international tourism receipts reaching
around USD 400 billion [4].
Methodology
To achieve the research objectives stated above, the primary task is to develop an appropriate
econometric model and collect relevant statistical data. In constructing the econometric model, it
is essential to use economic and social indicators identified in the reviewed literature [5]. The
model considers these factors, and the sources, content, and references related to the variables
included in the model are presented below.
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First, let us examine the indicators included in this model and their definitions. The variable
“unemployment” refers to the number of unemployed individuals in a given year in a given
country, representing the level of unemployment in that country. This indicator is obtained from
the World Bank database [6]. It is reported in census data, and descriptive statistics are presented
following the introduction of the variables. In the STATA software, the variable is coded as
“unemployment” (or “unemployment ILO”) for convenience in executing commands and
presenting STATA results.
Descriptive statistics for the variables are shown in Table 1. According to these data, the average
tourism receipts in the selected countries during the analyzed period amount to USD 19,199.
Table 1. Descriptive statistics of variables
Variable
Obs
Mean
Std. Dev.
Min
Max
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5
10
15
20
25
30
is
hs
iz
IL
O
0
10
20
30
40
chek
unemployment ILO
120
11.824
5.181
3.76
27.69
receipts
81
19.199
7.467
3.253
36.923
If we focus more on descriptive statistics of our main indicators, it is important to check the
distribution of data sets, their proximity to a normal distribution. To do this, it is advisable to
create histograms of variables using the STATA program and compare the graph with a normal
distribution
line.
0
.0
5
.1
.1
5
D
en
si
ty
5
10
15
20
25
30
ishsiz ILO
0
.0
2
.0
4
.0
6
D
en
si
ty
0
10
20
30
40
chek
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Diagram 1. Distribution of unemployment rate
Diagram 2. Distribution of tourism revenues
Figure 1 shows the distribution of the unemployment rate alongside the normal distribution curve
plotted using its arithmetic mean and standard deviation. It can be seen from the figure that the
unemployment rate approximately follows a normal distribution, which is important for the
validity of many statistical analyses [7].
Figure 2 displays the distribution of tourism revenue together with the corresponding normal
distribution curve. From this figure, it can be observed that tourism revenue indicators also
appear to follow a normal distribution, indicating the suitability of parametric statistical methods
for further analysis [8], [9].
To gain an initial understanding of the direction and strength of the relationship between these
two key economic indicators, Figure 3 was constructed as a scatterplot. This scatterplot
demonstrates a negative relationship between unemployment and tourism receipts, as evidenced
by the overall downward trend in the distribution of the data points. The negative relationship
suggests that higher tourism revenues are generally associated with lower unemployment levels,
consistent with findings in prior economic studies linking tourism activity to labor market
improvements [10], [11].
Diagramma 3. Ishsizlik va turizm tushumlari o`rtasidagi munosabat
However, since other factors may also be responsible for the emergence of such a negative
relationship, it is appropriate to analyze the relationship between these two indicators in greater
depth. To achieve this, it is advisable to calculate the correlation coefficients between the
variables, which provides an initial measure of the strength and direction of their linear
association [12]. Table 2 presents the matrix of correlation coefficients.
If we examine the results in Table 2, it becomes clear that there is only a weak economic
relationship between the two indicators, as reflected in the correlation coefficient of -0.349. This
negative coefficient confirms the visual pattern observed in the scatterplot. According to these
correlation results, there appears to be a modest inverse relationship between tourism revenues
and unemployment [13].
Table 2. Matrix of correlation coefficients
Variables
(1)
(2)
(1)unemploymentILO
1.000
(2) chek
-0.349
1.000
The main goal of this research is to determine the relationship between these economic indicators
through a regression model that accounts for their specific characteristics. To achieve this goal,
calculations were performed in the STATA 14 software using the Ordinary Least Squares (OLS)
method, a widely used approach for estimating linear relationships between variables [14]. The
results and their interpretation are presented in the next section.
Results analysis
When discussing the results of the regression analysis, it is appropriate to first examine the initial
regression estimates. These results are shown in Table 3. According to the table, the coefficients
are statistically significant at the 1% level (p-value = 0.001), indicating that the relationship
identified is unlikely to be due to random chance [15].
According to the regression results, tourism revenues have a statistically significant negative
impact on unemployment. However, the magnitude of this impact is not particularly strong, as
indicated by the relatively low R-squared value of 0.122, suggesting that tourism revenues
explain only a modest portion of the variation in unemployment rates [16].
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Diagram 3. Initial regression results
unemployILO
Coef.
St.Err.
t-value p-value [95% Conf Interval]
chek
-.244
.074
-3.31
.001
-.39
-.097
Constant
16.767
1.514
11.07 0
13.753
19.781
Mean dependent var
12.087
SD dependent var
5.210
R-squared
0.122
Number of obs
81
F-test
10.980
Prob > F
0.001
Akaike crit. (AIC)
489.726
Bayesian crit. (BIC)
494.515
*** p<.01, ** p<.05, * p<.1
To check whether there is an autocorrelation problem in our model, a Bruce-Godfrey test was
performed. The results are presented in Table 4.
Table 4. Bruce-Godfrey test
Ho:
Constant
variance
Variables:
fitted
values
of
unemploymentILO
chi2(1)
=
1.72
Prob > chi2 = 0.1899
The results show that our model is free from autocorrelation problems.
Conclusion
This study examines the impact of the tourism industry on unemployment for 6 countries,
namely Turkey, Greece, Italy, Spain, Egypt and Cyprus, over the period 2003-2022. To study the
relationship, regression was conducted using the ECKU method in the STATA-14 program.
According to the results, all regressors are significant, indicating that the development of the
tourism industry leads to a decrease in unemployment rates.
References:
1. UNWTO. (2023). International Tourism Highlights, 2023 Edition. United Nations World
Tourism Organization.
2. WTTC. (2023). Travel & Tourism Economic Impact 2023. World Travel & Tourism Council.
3. UNWTO. (2022). Tourism and GDP: The Role of the Tourism Sector in Global Economic
Growth. UNWTO Reports.
4. World Bank. (2023). World Development Indicators. Washington, D.C.: The World Bank.
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Available at:
https://databank.worldbank.org/
5. Song, H., & Li, G. (2008). Tourism demand modelling and forecasting—A review of recent
research. Tourism Management, 29(2), 203–220.
6.
World Bank. (2023). World Development Indicators Database. Retrieved from
https://databank.worldbank.org/
7. Gujarati, D. N., & Porter, D. C. (2009). Basic Econometrics (5th ed.). McGraw-Hill
Education.
9. Field, A. (2013).
Discovering Statistics Using IBM SPSS Statistics
(4th ed.). SAGE
Publications.
10. Song, H., & Li, G. (2008). Tourism demand modelling and forecasting: A review of recent
research.
Tourism Management
, 29(2), 203–220.
11. Brida, J. G., Lanzilotta, B., Lionetti, S., & Risso, W. A. (2010). The tourism-led growth
hypothesis for Uruguay.
Tourism Economics
, 16(3), 765–771.
12. Wooldridge, J. M. (2013).
Introductory Econometrics: A Modern Approach
(5th ed.). South-
Western Cengage Learning.
13. Greene, W. H. (2012).
Econometric Analysis
(7th ed.). Pearson Education.
14. WTTC. (2023).
Travel & Tourism Economic Impact 2023.
World Travel & Tourism Council.
