The European Journal 2021 - Vol 18 No 1

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ТНЕ EUROPEAN Vol.18 N° 1 APRIL2021 journal.singidunum.ac.rs

OF APPLIED ECONOMICS Measuring DistriЬution of lntellectual Capital Components Contribution: French Context рр. 1-14 Monetary Polic y and Bank Risk-Taking in Sub-Sahara Africa рр. 15-38

Econometric Examination of the Impact of lncome on Household Expenditures for Package Holidays in SerЬia рр. 39-54

The Impact of the Metacognitive and Behavioral Factors of Cultural Intelligence on Foreign Brand Acceptance рр. 73-88

Market Reactions to Football Мatch Results: The Effect ofVennes and Competition Types рр. 55-72

The VulneraЫe Financial lssue: Capital Flight in Indonesia рр. 89-105

1s There any Govemment Debt Threshold in Four Selected Central European Countries! рр. 126-136

Domestic Consumption and Uncerta Domestic Consumption and Uncertainty of Exchange Rate in а Monetary Union: Evidence from the Euro Area рр. 151-172

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ISSN 2406-2588

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The Enterprise Potential, Individual Entrepreneurial Orientation and Entrepreneurial Intentions of Students in SerЬia рр. 106-125

The Effect of Country of Origin Image Trough Quality, Design and Attractiveness Related to Product on Consumer Loyalty рр. 137- 150

Vol. 18 No. 1

The Europiean Journal of Applied Economics has been supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia.

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Vol. 18 No. 1 Publisher: Singidunum University E d it o r ia l B o a r d

Professor Milovan Stanišić, Singidunum University, Serbia mstanisic@singidunum.ac.rs Professor Francesco Frangialli, Hong Kong Polytechnic University, Hong Kong frangialli@gmail.com Professor Gunther Friedl, Technische Universität München, Germany gunther.friedl@wi.tu-muenchen.de Professor Karl Ennsfellner, IMC University of Applied Sciences, Krems, Austria (karl.ennsfellner@fh-krems.ac.at Professor Gyorgy Komaromi, International Business School, Budapest, Hungary gyorgy@komaromi.net Professor Vasile Dinu, University of Economic Studies, Bucharest, Romania dinu_cbz@yahoo.com Professor Ada Mirela Tomescu, University of Oradea, Oradea, Romania ada.mirela.tomescu@gmail.com Professor Radojko Lukić, University of Belgrade, Serbia rlukic@ekof.bg.ac.rs Professor Alexandar Angelus, Lincoln University, USA angelus@lincolnuca.edu Professor Milan Milosavljević, Singidunum University, Serbia mmilosavljevic@singidunum.ac.rs Professor Olivera Nikolić, Singidunum University, Serbia onikolic@singidunum.ac.rs Professor Goranka Knežević, Singidunum University, Serbia gknezevic@singidunum.ac.rs Professor Mladen Veinović, Singidunum University, Serbia mveinovic@singidunum.ac.rs Professor Jovan Popesku, Singidunum University, Serbia jpopesku@singidunum.ac.rs Professor Zoran Jeremić, Singidunum University, Serbia zjeremic@singidunum.ac.rs Professor Vesselin Blagoev, Varna University of Management, Bulgaria blagoev@vum.bg Professor Michael Minkov, Varna University of Management, Bulgaria minkov@iuc.bg Professor Ionel Bostan, Department of Economics, Al. I. Cuza University, Romania ionelbostan@yahoo.com Associate Professor Christine Juen, Austrian Agency for International Mobility and Cooperation in Education, Science and Research, Wien, Austria chrisine.juen@oead.at Associate Professor Anders Steene, Södertörn University, Stockholm/Hudinge, Sweden anders.steene@sh.se Associate Professor Ing. Miriam Jankalová, University of Zilina, Prague, Czech Republic miriam.jankalova@fpedas.uniza.sk Associate Professor Bálint Molnár, Corvinus University of Budapest, Budapest, Hungary molnarba@inf.elte.hu Associate Professor Vesna Spasić, Singidunum University, Serbia vspasic@singidunum.ac.rs Associate Professor Michael Bukohwo Esiefarienrhe, University of Agriculture, Dept. of Maths/Statistics, Makurdi, Nigeria esiefabukohwo@gmail.com Associate Professor Goh Yen Nee, Graduate School of Business, Universiti Sains Malaysia, Malaysia yngoh@usm.my Associate Professor Blaženka Hadrović Zekić, Faculty of Economics in Osijek, Croatia hadrovic@efos.hr Research Associate Professor Aleksandar Lebl, Research and Development Institute for Telecommunications and Electronics, Belgrade, Serbia lebl@iritel.com Senior Lecturer Nor Yasmin Mhd Bani, Universiti Putra, Malaysia nor_yasmin@upm.edu.my Roberto Micera, PhD, Researcher, National Research Council (CNR), Italy roberto.micera@ismed.cnr.it Assistant Professor Patrick Ulrich, University of Bamberg, Germany patrick.ulrich@uni-bamberg.de Assistant Professor Jerzy Ładysz, Wrocław University of Economics, Poland jerzy.ladysz@ue.wroc.pl Assistant Professor Konstadinos Kutsikos, University of the Aegean, Chios, Greece kutsikos@aegean.gr Assistant Professor Theodoros Stavrinoudis, University of Aegean, Chios, Greece tsta@aegean.gr Assistant Professor Marcin Staniewski, University of Finance and Management, Warsaw, Poland staniewski@vizja.pl Assistant Professor Gresi Sanje, İstanbul Bilgi Üniversitesi, Istanbul, Turkey gresi.sanje@bilgi.edu.tr Assistant Professor Michaeł Biernacki, Wrocław University of Economics, Poland michal.biernacki@ue.wroc.pl Assistant Professor Piotr Luty, Wrocław University of Economics, Poland piotr.luty@ue.wroc.pl Assistant Professor Vânia Costa, Polytechnic Institute of Cávado and Ave, Barcelos, Portugal vcosta@ipca.pt Assistant Professor Tihana Škrinjarić, University of Zagreb, Croatia tskrinjar@net.efzg.hr Luu Tien Dung, PhD, Lecturer - Researcher, Lac Hong University, Dong Nai, Vietnam dunglt@lhu.edu.vn Assistant Professor Dharmendra Singh, Modern College of Business and Science, Oman dharmendra@mcbs.edu.om Associate Professor Slađana Čabrilo, I-Shou University, Kaohsiung City, Taiwan (R.O.C.) sladjana@isu.edu.tw Ed it o r ia l O f f ice

Editor in Chief: Managing Editor: Technical Editor: English Language Editor:

Professor Žaklina Spalević, Singidunum University Gordana Dobrijević, Associate Professor, Singidunum University Jovana Maričić, Singidunum University Marijana Prodanović, Associate Professor, Singidunum University

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Prepress: Miloš Višnjić Design: Aleksandar Mihajlović, MA ISSN: 2406-2588 The European Journal of Applied Economics is published twice a year.

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CONTENTS

1 - 14 15 - 38 39 - 54

55 - 72

73 - 88

Measuring Distribution of Intellectual Capital Components Contribution: French Context Endre Pap, Miloš Petković, Ana Simićević

Monetary Policy and Bank Risk-Taking in Sub-Sahara Africa

Gabriel Aboyadana

Econometric Examination of the Impact of Income on Household Expenditures for Package Holidays in Serbia

Hasan Hanić, Milica Bugarčić, Radojko Lukić

Market Reactions to Football Match Results: The Effect of Venues and Competition Types

Krismon Dwi Apredianto, Apriani Dorkas Rambu Atahau, Andrian Dolfriandra Huruta

The Impact of the Metacognitive and Behavioral Factors of Cultural Intelligence on Foreign Brand Acceptance

Stefan Zdravković, Jelena Peković

The Vulnerable Financial Issue: Capital Flight in Indonesia 89 - 105

106 - 125

126 - 136

Muhammad Basorudin, R. Dwi Harwin Kusmaryo, Sri Hartini Rachmad, Gantjang Amannullah, Serly Rachmadani Hamid

The Enterprise Potential, Individual Entrepreneurial Orientation and Entrepreneurial Intentions of Students in Serbia Jelena Rajković, Jasmina Poštin, Marko Konjikušić, Aleksandra Jagodić Rusić, Hadži Strahinja Stojković, Milan Nikolić

Is There any Government Debt Threshold in Four Selected Central European Countries?

Yu Hsing

III

137 - 150

151 - 172

IV

The Effect of Country of Origin Image Trough Quality, Design and Attractiveness Related to Product on Consumer Loyalty Srđan Šapić, Jovana Lazarević, Jovana Filipović

Domestic Consumption and Uncerta Domestic Consumption and Uncertainty of Exchange Rate in a Monetary Union: Evidence from the Euro Area Samuel N. Okafor, Juste S. Lokossou

EJAE 2021, 18(1): 1 - 14 ISSN 2406-2588 UDK: 005.336.4 005.336.1 658.1(44)"2008/2016" DOI: 10.5937/EJAE18-28628 Original paper/Originalni naučni rad

MEASURING DISTRIBUTION OF INTELLECTUAL CAPITAL COMPONENTS CONTRIBUTION: FRENCH CONTEXT Endre Pap, Miloš Petković*, Ana Simićević Singidunum University, Belgrade, Serbia

Abstract: In this paper the contribution of intellectual capital components in the overall intellectual capital value is investigated. This paper adopted quantitative statistical methods Lambda phase measurement and Shapley’s value on the sample of 498 French companies in the period of 2008 to 2016 in order to estimate the highest and lowest contributions of intellectual capital components. For the purpose of the study, the official financial information from the companies’ annual reports were taken from the financial database “Point Risk”. The paper concentrates on two out of three intellectual capital components: structural and customer capital components. By the Shapley’s value final result, the customer capital component, which represents company’s commercial activities with the coefficient of 0.29911, is of greatest importance. On the other side, the lowest importance belongs to the structural capital component that represents value coming from research and development expenses with the coefficient of 0.07463 This study contributes to the management sciences literature byexamining distribution of contribution of two intellectual capital components in the annual reports of French companies.

Article info: Received: October 1, 2020 Correction: November 23, 2020 Accepted: December 21, 2020

Keywords: Distribution, Intellectual Capital Components, Contribution, -monotone measure, Shapley’s value.

INTRODUCTION In the knowledge-based economy, it is not enough just to take the traditional and financial measures of a company into account, but it is important to find a way to recognize intellectual capital as well.

Traditional measures are highly unsuitable mainly because they are based on conventional accounting principles. This is the biggest challenge for companies, because companies must measure these values consistently and systematically over time (Belo et al., 2014). The limits in the valuation process are no longer focused on the production of physical products or providing services. Instead, they are focused on the creation of new innovations and ideas (Mura et al., 2012), because the main value creators are intellectual investment values (Zéghal & Maaloul, 2011). *E-mail: mpetkovic@singidunum.ac.rs

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PAP. E., PETKOVIĆ. M., SIMIĆEVIĆ. A.  MEASURING DISTRIBUTION OF INTELLECTUAL CAPITAL COMPONENTS CONTRIBUTION: FRENCH CONTEXT

The concept of intellectual capital was revealed for the first time in 1969 by Kenneth Galbraith.

Kenneth Galbraith wrote a letter to the economist, Michael Kalecki, where he stated, “I wonder if you realize how much those of us around the world have owed to the intellectual capital you have provided over these past decades.” (Hudson, 1993).

The interest in studying this topic lies in the fact that employees’ competences and human capital are the main drivers of companies’ competitiveness in the modern economy (Radivojevic et al., 2019).

Investing in human capital influences positively on company’s productivity with precise measurements (McGrattan, 2020). Intellectual capital plays an important role in a company’s final success (RodriguezCastellanos et al., 2011), but at the same time, in a period of crisis and financial shocks, it enables greater labor market volatility as a response (Lopez and Olivella, 2018).

Intellectual capital as a strategic resource of each company is not a sole thing; it is composed of many interrelated elements that have been continuously cooperated and supported together as a whole (Corrado et al., 2012). Based on the available literature, intellectual capital is classified into three components: human capital, structural capital and customer capital (Martínez-Torres, 2006). The competitive advantage of a company lies in the complexity of these components of intellectual capital.

Success of a company depends on the strategic management of the selected components of intellectual capital because the investments in intellectual capital are seen as capital expenditures (Piekkola, 2011).

Garanina and Pavlova (2011) prove that a positive interaction between human capital, structural capital and customer capital exists. The interaction between three main components of intellectual capital, human capital, structural capital and customer capital generates benefits to a company. The results of the study of Sumedrea (2013) showed that a company’s crisis in development can be exceeded by a company’s human and structural capitals. Maditinos et al. (2011) found significant human capital and structural capital efficiency and financial company performance. Diez et al. (2010) tried to examine the influence of human capital and structural capital on the creation of business value of Spanish companies which have 25 or more employees. The study confirmed a positive relationship between the use of human and structural capital and value creation that comes from sales growth.

In an effort to emphasize importance of particular intellectual capital component compared to other two, the purpose of this study is to turn attention to the unique contribution of a total intellectual capital surplus generated by the coalition of all intellectual capital components. A coalition between intellectual capital components obtain certain overall gains from that correlation. Since some components may contribute more to the coalition than others, what final performance should arise in any particular contribution? The question that arises is what is the contribution of each intellectual capital component to the overall intellectual capital value?

Our paper contributes to science by examining which intellectual capital component contributes the most in the sample of French companies, taking into consideration their existing interrelations. In that way, a company will pay attention and invest more in a particular component in order to gain higher benefits in the upcoming future periods. The analysis is composed of 498 French companies over the period of 2008 to 2016 from 34 different industries. In this paper, the following statistical quantitative methods are implemented: Lambda Phase Measurement Method and Shapley’s Value Method.

This paper consists of six sections. Section 2 contains the explanation of the history of the intellectual capital components. Section 3 is devoted to the problem of Intellectual Capital of 498 French companies.

In Section 4, we explain the methodology used to solve our research question. In Section 5, we apply the presented methodology from Section 4 on the stated problem from Section 3. The last Section 6 is devoted to the discussion of the obtained results.

2

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PAP. E., PETKOVIĆ. M., SIMIĆEVIĆ. A.  MEASURING DISTRIBUTION OF INTELLECTUAL CAPITAL COMPONENTS CONTRIBUTION: FRENCH CONTEXT

HISTORY OF THE PROBLEM OF INTELLECTUAL CAPITAL Intellectual Capital Components and Its Interrelations Twentieth century is a century of ideas, knowledge, innovations, information and changes. Industries that provide services expanded radically. Simultaneously, the financial market became influential in the global market, so “intellectual capital” obtained a very important role for itself. Market value of a company is composed of total book value, everything that is a company’s property and intellectual capital (Ciprian et al., 2012).

The results today must come from the investments made in previous periods (García- Zambrano et al., 2018). If an organization wants to fulfil itsadvanced planned goals, it is not possible without the existence of intellectual capital (Singla, 2020; Sydler et al., 2014).

According to a synthesis from extant literature, intellectual capital is classified into three components (Marr and Moustaghfir, 2005; Martínez-Torres, 2006;): 1) Human Capital - Human capital represents employees’ knowledge, competencies and education; 2) Customer Capital - Customer capital represents all relations with customers, suppliers, distributors and other stakeholders. Customer capital is a very important type of intellectual capital for every company, mainly because a company is not an isolated entity. It is an organization that continuously interacts with its business environment. Business environment, together with its customers and clients represents a source of knowledge regarding advantages or disadvantages of a company’s products or services, new ideas, organizational practices, etc.; 3) Structural Capital - Structural capital refers to organizational systems, culture, practices, processes and business routines (Marr and Moustaghfir, 2005). A company exists because of a combination of employees’ competences and internal structure and organization (Hashim et al., 2015).

Based on the literature, intellectual capital components and its investments are linked to value factors (Dumay, 2012).

Different authors prove highly positive interrelations between intellectual capital components.

Ognjanovic (2017) proved a strong and positive relationship between the observed intellectual capital components based on the combined factor, analysis and structural equation study of 44 hotel companies in Serbia. The strongest relationship is observed between customer capital and structural capital.

Authors Ulubeyli and Yorulmaz (2019) proved that human capital and structural capital together have a strong impact on the financial performance in a highly innovative industry such as consulting industry.

However, the relational capital may not lead to the same result on the financial result. The study published

by Molodchik et al. (2012) examined the interrelation between intellectual capital components. From those interrelations, further improvements of company’s competitive advantage were produced, which increased company’s value. Many authors proved a positive and significantly strong relationship between intellectual capital components and financial performance (Chang, 2013; Díaz- Fernández et al., 2015; Pucci et al., 2015; Sharabati et al., 2010; Soewarno and Tjahjadi, 2020; Tanideh, S., 2013).

Jensen et al. (2020) proved that between intellectual stimulation and financial performance indicators exists a strong and positive relationship.

On the other side, relationship between intellectual capital components and final results were not always positive (Chu et al., 2011; Mehralian et al., 2012).

3

EJAE 2021  18(1)  1 - 14

PAP. E., PETKOVIĆ. M., SIMIĆEVIĆ. A.  MEASURING DISTRIBUTION OF INTELLECTUAL CAPITAL COMPONENTS CONTRIBUTION: FRENCH CONTEXT

INTELLECTUAL CAPITAL OF 498 FRENCH COMPANIES Data Sample Explanation The study is focused on the financial information gathered by the financial database “Point Risk”.

It comprises of financial data from the financial statements of French companies during the period of 2008 to 2016. Both high-technology and low-technology companies are included in the sample. Divided into these two industrial groups, companies belong to 34 different industries that proves heterogeneity of the observed sample. The classification of industries is proposed by Francis and Schipper (1999).

In the research model, the initial number of companies was 1,990. After detailed investigation, 1,195 companies did not have complete required financial information. Furthermore, 297 companies are mentioned more than once in the database. The final number of companies observed and finally tested is 498.

Table 1: Number of companies included in the research model Number of Companies Starting Number, Observed

1,990

Missing Data Companies

1,195

Repeating Companies

297

Final Number of Companies

498

In Table 2 below, the structure of the total number of selected companies in the sample is presented.

As we can conclude from the sample, the largest number of companies is from the Low Technology Industry – Miscellaneous Manufacturing Industries – 89, followed by those from High Technology, listed chronologically, Computer Programming, Software, Data Processing, Research, Development, Testing Services, Drugs and Electrical Apparatus, 81, 44, 38 and 35. In total, there are 241 companies that belong to the High Technology industries, and 257 companies that belong to the Low Technology industries. The percentage between the High Technology and Low Technology is 48% and 52%.

4

EJAE 2021  18 (1)  1 - 14

PAP. E., PETKOVIĆ. M., SIMIĆEVIĆ. A.  MEASURING DISTRIBUTION OF INTELLECTUAL CAPITAL COMPONENTS CONTRIBUTION: FRENCH CONTEXT

Table 2: Number of companies per industry in the sample Industry

Number of Companies

Low Technology - Miscellaneous Manufacturing Industries

89

High Technology - Computer Programming, Software, Data Processing

81

High Technology - Research, Development, Testing Services

44

High Technology - Drugs

38

High Technology - Electrical Industrial Apparatus

35

Low Technology - General Industrial Machinery and Equipment

31

Low Technology - Agricultural Products

26

Low Technology - Miscellaneous Plastics Products

18

Low Technology - Motor Vehicles and Motor Vehicle Equipment

18

Low Technology - Blast Furnaces and Steel Works

16

High Technology - Electrical Machinery and Equipment, Excluding Computers

15

Low Technology - Wood Buildings, Mobile Homes

14

High Technology - Computer and Office Equipment

11

Low Technology - Grocery Stores

10

Low Technology - Textile Mill Products

9

Low Technology - Lumber and Wood Products, Excluding Furniture

8

High Technology - Computer Hardware

6

High Technology - Telephone Communications

6

High Technology - Electronic Components, Semiconductors

5

Low Technology - Construction - Special Trade

5

Low Technology - Trucking, Courier Services, Excluding Air

5

High Technology - Electrical Transmissions and Distribution Equipment

4

High Technology - Household Audio, Video Equipment, Audio Receiving

4

Low Technology - Paper and Allied Products

4

High Technology - Communication Equipment

3

High Technology - Electrical Lighting and Wiring Equipment

3

Low Technology - Dairy Products

3

Low Technology - Cement Hydraulic

2

Low Technology - Rubber and Miscellaneous Plastics Products

2

Low Technology - Scheduled Air Transportation, Air Courier

1

Low Technology - Water Transportation

1

Total Number of Companies

498

5

EJAE 2021  18(1)  1 - 14

PAP. E., PETKOVIĆ. M., SIMIĆEVIĆ. A.  MEASURING DISTRIBUTION OF INTELLECTUAL CAPITAL COMPONENTS CONTRIBUTION: FRENCH CONTEXT

METHODS OF LAMBDA MONOTONE MEASURE AND SHAPLEY VALUE As an efficient tool for measuring the interaction between elements, monotone measure is defined in the following way, see Pap (1995): Definition 4.1. Let be a fixed set. is a set of all the subsets of the set X. Monotone measure on X is a set function , which satisfies the following conditions: (i) (ii) If

and

, then

.

In order to determine such a monotone measure, we must find of its values, since by Definition 4.1. and are always equal to zero and one, respectively. It is obvious that such an evaluation process is rather complex, especially in application. In order to reduce the complexity of calculation, -monotone measure , which acts as a special kind of monotone measure has been proposed, see Wang and Klir (2009), and Torra et al. (2014).

Definition 4.2. Let be a fixed set. Monotone measure measure if it satisfies the following condition:

where

, for

on X is called -monotone

and

Theorem 4.1. Let

be a finite set, and

be a -monotone measure on X.

Then the following equality holds

(1)

where

, for each i, j =1,..., n and i j.

For a subset

the following holds

(2)

The value of the parameter can be determined by applying the above equation. The equation for X, since , reduces on the following equation (3) 6

EJAE 2021  18 (1)  1 - 14

PAP. E., PETKOVIĆ. M., SIMIĆEVIĆ. A.  MEASURING DISTRIBUTION OF INTELLECTUAL CAPITAL COMPONENTS CONTRIBUTION: FRENCH CONTEXT

Theorem 4.2. The sign of the parameter is given by the following conditions (i)

, when

,

(ii)

, when

,

(iii)

, when

.

Shapley proposed a coefficient of importance which is called Shapley value (abbreviated to defined it on in the following way, see Grabisch (2016):

),

(4) where, t is the cardinality of the subset T of the set X. The Shapley value of a particular variable intuitively represents the average change in prediction that occurs in a coalition when joined by a given variable. Based on the previous equation (4), we know that Shapley value is an expected value of the total marginal contribution between elements i and any other coalition. Having in mind the definition of monotone measure, it is easy to notice that for each i , which means that when measure is additive, then i and any other coalition

and

, is a weight vector, named Shapley value. Specially, , which means that there is no interaction between elements

. In this case, Shapley value becomes a traditional weight vector

, , where . When is not additive, then , which means that there is a complementary interaction between elements i and any other coalition .

If then there is a redundant interaction between elements i and any other coalition .

Therefore, Shapley’s weight not only offers the measure of criteria value, but also maintains their interactive characteristics.

SOLUTION OF THE PROBLEM FROM SECTION 3 We demonstrate the determination of the parameter formonotone measure for the problem stated in Section 3. The following data are presented in the table: (a) value of the research asset, (b) research and development expenditures, (c) commercial expenses and (d) sales expenses.

They are compared in this paper by calculating Shapley values for all four variables.

7

8

2,006,000

Median

189,743,489

0

Std. Error of Skewness

625,988,000

1,839,190,370

4,024,849,670

9,768,882,840

Maximum

Sum

2,000

625,986,000

0

22,110

4,024,827,560

0

346

0

18

Minimum

Range

Std. Error of Kurtosis

421

20

Kurtosis

30,809,718

16000a

370,500

1,380,616

3,693,153

45,031,785

8,000

224,000

2,017,923

6,289,476

-

498

Commercial Fond (Expenses)

246,236,861

3,047,000

2,271,000

11,034,138

29,469,536

-

498

Costs of Sales (Expenses)

67,934,611

795,000

2,416,056

3,044,223

16,361,130

-

498

Personnel Costs (Salaries and Traitments + Sociales Charges)

135,390,637

327000a

1,559,000

6,067,000

18,211,491

-

498

Total Intangible Assets

915,075,382

353000a

3,635,500

41,005,508

84,546,702

-

498

Total NonCurrent Assets (mora da bude vece od TIA)

1,047,114,585

16,890,000

9,016,500

46,922,327

118,432,756

-

498

Total Book Value

3,132,158,944

684,690,000

1,000

684,689,000

0

203

0

14

14,675,828,725

3,905,922,000

3,555

3,905,918,445

0

226

0

15

8,147,842,906

1,019,128,000

7,555

1,019,120,445

0

114

0

9

9,069,322,598

2,777,520,000

35,000

2,777,485,000

0

351

0

18

42,104,257,779

18,158,162,000

65,222

18,158,096,778

0

326

0

17

58,979,512,447

19,728,169,000

287,000

19,727,882,000

0

278

0

16

36,002,591,784,481,500 949,238,695,524,134 2,027,861,623,283,460 60,632,591,752,763,800 4,615,111,330,303,220 18,330,624,560,269,300 837,362,954,065,728,000 1,096,448,954,132,360,000

Skewness

Variance

Std. Deviation

85,440

8,706,028

Mode

20,366,069

-

Mean

498

23

Research and Development Expenses

475

Std. Error of Mean

Valid Missi

Research Asset Value

Table 3: Descriptive statistics of selected variables

EJAE 2021  18(1)  1 - 14

PAP. E., PETKOVIĆ. M., SIMIĆEVIĆ. A.  MEASURING DISTRIBUTION OF INTELLECTUAL CAPITAL COMPONENTS CONTRIBUTION: FRENCH CONTEXT

EJAE 2021  18 (1)  1 - 14

PAP. E., PETKOVIĆ. M., SIMIĆEVIĆ. A.  MEASURING DISTRIBUTION OF INTELLECTUAL CAPITAL COMPONENTS CONTRIBUTION: FRENCH CONTEXT

The study is focused on two out of three intellectual capital components, and those are: customer capital and structural capital components.

According to Miles (2011) costs related to marketing and commercial activities represent the customer capital. In order to achieve a better market or financial position, companies strive to attract more and more customers. The study by García-Zambrano et al. (2018) also proved a positive link between the investments in customer capital and Tobin’s Q ratio results that resulted in improvement of company’s financial performance.

For the purpose of calculating more easily, the set of variables in the table is marked as X and each variable is allocated a letter in the following order a, b, c, d. , We then 6 use the formula (3) to calculate the parameter . Since the Theorem 4.2, we expect for the parameter that

, and based on

. We obtain the following equation:

which is transformed into an equation of fourth degree:

We denote Using the halving method, we obtain an approximate solution of the previous equation where the error is lower than 10-2. The number of steps is determined by the following condition:

Applying the halving method, we obtain a series of approximations by finding the midpoint of the interval: Then, using the function sign we check, which in half of the interval, is the root of the equation. The same method is repeated on that half of the interval with the new A and B. We search for the midpoint of the interval again and we repeat the method 7 times. All the obtained values are given in the following table: Table 4: Produced values from halving method n

A

B

1

-1

0

-0.5

0.1439

2

-1

-0.5

-0.75

-0.0027

3

-0.75

-0.5

-0.625

0.0682

4

-0.75

-0.625

-0.6875

0.0321

5

-0.75

-0.6875

-0.71875

0.0145 -0.3847

6

-0.75

-0.71875

-0.734375

7

-0.734375

-0.71875

-0.7265625

9

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Value is an approximate solution of the starting equation. Then, based on the formula (1), we calculate the remaining values of the measures on all subsets of the set X. The obtained results are presented below:

Based on the formula (4) we can calculate Shapley values for all four variables:

We see that the third variable has the greatest Shapley value. Finally, by the Shapley’s value the customer capital component that represents company’s commercial activities with the coefficient of 0.29911 has the greatest importance. On the other side, the lowest importance has the structural capital component that represents value coming from research and development expenses with the coefficient of 0.07463.

CONCLUSIONS Intellectual capital is the main value driver within a company, with a combined use of its three components: human capital, structural capital and customer capital. The intellectual capital leads to positive results only with synchronized use of these three main components. The purpose of our research was to examine the level ofimportance of each intellectual capital component for 498 French companies. The importance of each intellectual capital component represents its contribution in the final performance of a company.

The paper was based on two quantitative methods that relied on the analysis of financial information of companies: Lambda monotone measure method and Shapley’s Value method. Our paper is different with respect to the existing literature by conducting a quanti-statistical analysis of disclosure practices, based on a sample of French companies, without any previous selection by sector.

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The results of empirical analysis of 498 French companies were used to fill the gap in the literature about the estimation of intellectual capital components importance. Our results may have important implications to companies’ decision making processes. While the current managements seek to improve their financial performance with a high level of risks and uncertainty, the findings from this paper suggest that investments in a particular component can even enhance company’s higher financial performance by reducing risks in the managerial investment decisions. This research suggests that managers should pay more attention to the structural component of intellectual capital because it has the highest coefficient of contribution. The role of the particular intellectual capital component forces a more intensive use it in the future value creation processes.

ACKNOWLEDGEMENT The first author is supported by the project on Artificial Intelligence ATLAS (grant No. 6524105) by the Science Fund of the Republic Serbia.

We would like to thank the reviewers for their valuable suggestions that resulted in the improvements of our paper.

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Díaz-Fernández, M. C., González-Rodríguez, M. R., & Simonetti, B. (2015). Top management team’s intellectual capital and firm performance. European Management Journal, 33(5), 322– 331. https://doi.org/10.1016/j.emj.2015.03.004 Diez, J. M., Ochoa, M. L., Prieto, M. B., & Santidrian, A. (2010). Intellectual capital and value creation in Spanish firms. Journal of Intellectual Capital, 11(3), 348–367. https://doi.org/10.1108/14691931011064581 Dumay, J. C. (2012). Grand theories as barriers to using IC concepts. Journal of Intellectual Capital, 13(1), 4–15. https://doi.org/10.1108/14691931211196187 Francis, J., & Schipper, K. (1999). Have Financial Statements Lost Their Relevance? Journal of Accounting Research, 37 (2), 319–352. https://doi.org/10.2307/2491412 Garanina, T., & Pavlova, J. (2011). Intangible Assets and Value Creation of a Company: Russian and UK Evidence.

In G. Turner & C. Minnone (Eds.) European Conference on Intellectual Capital (pp. 165–175). Nicosia, Cyprus: University of Nicosia.

García-Zambrano, L., Rodríguez-Castellanos, A., & García-Merino, J. D. (2018). Impact of investments in training and advertising on the market value relevance of a company’s intangibles: The effect of the economic crisis in Spain. European Research on Management and Business Economics, 24(1), 27–32. https://doi. org/10.1016/j.iedeen.2017.06.001 11

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Procedia - Social and Behavioral Sciences, 211, 207–214. https://doi.org/10.1016/j.sbspro.2015.11.085 Hudson, W. J. (1993). Intellectual Capital: How to Build It, Enhance It, Use It. New York, USA: John Wiley & Sons.

Jensen, M., Potočnik, K., & Chaudhry, S. (2020). A mixed-methods study of CEO transformational leadership and firm performance. European Management Journal, 38(6), 836–845. https://doi.org/10/ghp2vz Lopez, J. I., & Olivella, V. (2018). The importance of intangible capital for the transmission of financial shocks.

Review of Economic Dynamics, 30, 223–238. https://doi.org/10/gfnvv9 Maditinos, D., Chatzoudes, D., Tsairidis, C., & Theriou, G. (2011). The impact of intellectual capital on firms’ market value and financial performance. Journal of Intellectual Capital, 12(1), 132– 151. https://doi. org/10.1108/14691931111097944 Marr, B., & Moustaghfir, K. (2005). Defining Intellectual Capital: A Three‐dimensional Approach. Management Decision, 43 (9), 1114–1128. https://doi.org/10.1108/00251740510626227 Martínez-Torres, M.R. (2006). A Procedure to Design a Structural and Measurement Model of Intellectual Capital: An Exploratory Study. Information & Management, 43 (5), 617–626. https://doi.org/10.1016/j.im.2006.03.002 McGrattan, E. R. (2020). Intangible capital and measured productivity. Review of Economic Dynamics, 37(Sup 1), S147–S166. https://doi.org/10/ghp2rs Mehralian, G., Rasekh, H. R., Akhavan, P., & Sadeh, M. R. (2012). The Impact of Intellectual Capital Efficiency on Market Value: An Empirical Study from Iranian Pharmaceutical Companies. Journal of Intellectual Capital, 13(1), 138-158. https://doi.org/10.1108/14691931211196259 Miles, J. (2011). Análisis del Capital Intelectual de las pequeñas y medianas empresas uruguayas y su impacto en los resultados [Dostoral dissertation]. San Sebastián: Universidad de Deusto.

Molodchik, M. A., Shakina, E. A., & Bykova, A. A. (2012). Intellectual Capital Transformation Evaluating Model.

Journal of Intellectual Capital, 13(4), 444–461. https://doi.org/10.2139/ssrn.2550215 Mura, M., Lettieri, E., Spiller, N., & Radaelli, G. (2012). Intellectual Capital and Innovative Work Behaviour: Opening the Black Box. International Journal of Engineering Business Management, 4(39), 1-10. https:// doi.org/10.5772/54976 Ognjanovic, J. (2017). Relations of Intellectual Capital Components in Hotel Companies. Industrija, 45(2), 181–196. https://doi.org/10.5937/industrija45-12144 Pap, E. (1995). Null-Additive Set Functions. Heidelberg, Germany: Springer Netherlands.

Piekkola, H. (2011). Intangible capital: The key to growth in Europe. Intereconomics, 46(4): 222–228. https://doi. org/10.1007/s10272-011-0387-2 Pucci, T., Simoni, C., & Zanni, L. (2015). Measuring the relationship between marketing assets, intellectual capital and firm performance. Journal of Management & Governance, 19(3), 589– 616. https://doi.org/10.1007/ s10997-013-9278-1 Radivojevic, V., Kahrovic, E., & Krstic, M. (2019). Population skills as an indicator of European countries’ competitiveness in the modern economy. Vojno delo, 71(5), 105-116. https://doi.org/10.5937/vojdelo1905105R Rodriguez-Castellanos, A., Garcia-Merino, J.D., & Garcia-Zambrano, L. (2011). Organisational Knowledge, Intangible Resources and Business Performance. Journal of Knowledge Management Practice, 12(2), 1-11.

Sharabati, A. A., Naji Jawad, S., & Bontis, N. (2010). Intellectual capital and business performance in the pharmaceutical sector of Jordan. Management Decision, 48(1), 105–131. https://doi.org/10.1108/00251741011014481 Soewarno, N., and Tjahjadi, B. (2020). Measures that matter: An empirical investigation of intellectual capital and financial performance of banking firms in Indonesia. Journal of Intellectual Capital, 21(6), 1085–1106. https://doi.org/10/ghp2sd Sumedrea, S. (2013). Intellectual Capital and Firm Performance: A Dynamic Relationship in Crisis Time. Procedia Economics and Finance, 6, 137–144. https://doi.org/10.1016/S2212-5671(13)00125-1 12

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Singla, H.K. (2020), Does VAIC affect the profitability and value of real estate and infrastructure firms in India? A panel data investigation. Journal of Intellectual Capital, 21(3), 309-331. https://doi.org/10.1108/JIC-03-2019-0053 Sydler, R., Haefliger, S., & Pruksa, R. (2014). Measuring intellectual capital with financial figures: Can we predict firm profitability? European Management Journal, 32(2), 244–259. https://doi.org/10.1016/j.emj.2013.01.008 Tanideh, S. (2013). Relationship between innovation capital and intellectual capital with value and financial performance. Life Science, 10(10s), 251–254.

Torra, V., Narukawa, Y., & Sugeno, M. (2014). Non-Additive Measures. Heidelberg, Germany: Springer International Publishing.

Ulubeyli, S. & Yorulmaz, D. (2019), Intellectual capital based reputation for market internationalization: The case of engineering consultancy firms, Journal of Intellectual Capital, 21(1), 40-61. https://doi.org/10.1108/ JIC-01-2019-0010 Wang, Z., and Klir, G. (2009). Generalized Measure Theory. New York, USA: Springer US. Zéghal, D., and Maaloul, A. (2011). The accounting treatment of intangibles – A critical review of the literature.

Accounting Forum, 35(4), 262–274. https://doi.org/10.1016/j.accfor.2011.04.003

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MERENJE RASPODELE DOPRINOSA KOMPONENATA INTELEKTUALNOG KAPITALA: FRANCUSKI KONTEKST Rezime: U ovom radu istražuje se doprinos komponenata intelektualnog kapitala ukupnoj vrednosti intelektualnog kapitala. Ovaj rad je usvojio kvantitativne statističke metode merenja Lambda faze i Šeplijevu vrednost na uzorku od 498 francuskih kompanija u periodu od 2008. do 2016. godine kako bi se procenili najveći i najmanji doprinos komponenata intelektualnog kapitala. Za potrebe ovog istraživanja, zvanične finansijske informacije iz godišnjih izveštaja kompanija preuzete su iz finansijske baze podataka „Point Risk“. Rad se fokusira na dve od tri komponente intelektualnog kapitala: strukturne komponente i komponente kapitala kupaca. Krajnjim rezultatom Šeplijeve vrednosti, komponenta kapitala kupaca, koja predstavlja komercijalne aktivnosti kompanije sa koeficijentom 0,29911, smatra se najvažnijom. Sa druge strane, najmanji značaj ima komponenta strukturnog kapitala koja predstavlja vrednost koja proizlazi iz troškova istraživanja i razvoja sa koeficijentom 0,07463. Ovo istraživanje doprinosi literaturi o naukama o menadžmentu ispitivanjem raspodele doprinosa dve komponente intelektualnog kapitala u godišnjim izveštajima francuskih kompanija.

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Ključne reči: distribucija, komponente intelektualnog kapitala, doprinos, monotona mera, Šeplijeva vrednost.

EJAE 2021, 18(1): 15 - 38 ISSN 2406-2588 UDK: 338.23:336.74 005.334:336.71(292.63/.69)"2001/2015" DOI: 10.5937/EJAE18-28152 Original paper/Originalni naučni rad

MONETARY POLICY AND BANK RISK-TAKING IN SUB-SAHARA AFRICA Gabriel Aboyadana* Department of Economics, Strathclyde Business School, Glasgow, Scotland, United Kingdom

Abstract: Monetary policy has been shown to influence the risk-taking behaviour of banks in Europe and North America. Africa has however received limited attention in this regard. This study contributes to the monetary policy and bank risk-taking literature for sub-Sahara Africa by examining a panel of commercial banks from 2001-2015 for different types of risks.

We find that monetary policy significantly influences bank risk-taking both statistically and economically, but the effect differs across the types of risks. Bank size and profitability are important in determining how effective monetary policy impacts risk-taking. The effects are stronger for countries without exchange rate controls. In terms of policies, monetary authorities intending to pursue expansionary monetary policy must remedy the risk-taking response by banks.

Article info: Received: September 3, 2020 Correction: October 19, 2020 Accepted: November 2, 2020 Keywords: Risk-taking, Financial Crisis, Monetary Policy, Africa.

JEL Classification: E52, E58, G01, G18, G21, G28, G31

INTRODUCTION The discussion about the risk-taking channel of monetary policy can be traced to the 2008 global financial crisis. That crisis proved that even the most advanced financial systems are vulnerable to uncertainties, which can lead to failures both for the domestic financial system and international financial markets and could have detrimental consequences on the real economy. The crisis was sparked by excessive credit expansion and the burst of a chain of bubbles in the real estate market (Özşuca & Akbostanci, 2016). These events led to instability in the global credit market which led to a realisation of an existing threat to the stability of the global financial market.

Policymakers and researchers alike have systematically raised questions about the possible causes of the crisis to explain the factors that are responsible for the fragility of the global financial system. In the meantime, however, though empirical research in this regard is scant, there appears to be a consensus on some factors that could have caused the crisis. These include failure of macroprudential policies, regulatory and supervisory failure, the emergence of complex financial instruments and weak corporate governance systems (Bruno, Shim, & Shin, 2017; Fahr & Fell, 2017). *E-mail: gabriel.aboyadana@strath.ac.uk

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ABOYADANA. G.  MONETARY POLICY AND BANK RISK-TAKING IN SUB-SAHARA AFRICA

In addition to these, monetary authorities have also been blamed for implementing excessively accommodative monetary policies in the half-decade before the crisis (Dell’Ariccia, Laeven, & Marquez, 2014).

This has sparked a strong debate among economists because those who point fingers at monetary policy have argued that when the monetary condition is lax such as in a period of low-interest rates and loose liquidity over prolonged periods, there is an increased incentive for financial intermediaries to take more risk. Thus, the proponents on this side of the debate hold the view that monetary policy was an important driving tool that caused the financial crisis. The situation becomes even more contentious when we consider that, to remedy the effects of the crisis, monetary authorities in some countries eased the monetary condition further. Consequently, the debate over the relationship between monetary policy and financial stability has intensified among researchers and in policy discourse.

Before the financial crisis, monetary policy paid little attention to financial stability because traditionally the role of monetary policy has been thought of in terms of price stability whereas macroprudential policies were the responsibility of supervisory authorities. Moreover, the rapid innovation in the financial sector has been regarded as a tool for achieving stability in the financial system. That notwithstanding, the crisis has given strong basis to argue that policy decisions aimed at financial stability can no longer be taken without recourse to the effect of monetary policy (Bruno et al., 2017; Salle & Seppecher, 2018).

As well, monetary policy decisions must consider implications for financial stability.

The question of how monetary policy transmits to banks’ risk-taking incentives has been central in the said debate and has led to the emergence of the so-called risk-taking channel. Risk-taking channel refers to the perception and the pricing of risk because of the monetary policy stance at a given time.

The emphasis has been on how policy stance affects the risk appetite and the risk perception of banks.

The central argument here is that when interest rates are very low banks will lean towards higher risk-taking and cause a shift in credit supply; that is, the risk-taking channel implies an increase in the riskiness of lending via low-quality portfolios. In this way, monetary policy can contribute to financial instability via a build-up of imbalances in the financial system. The mechanism through which monetary policy can affect bank risk can be complex and dynamic. A few ways are identified. Firstly, it can affect the valuation of their incomes and cash flows, and this affects how they measure risks. Secondly, lowinterest rates could make banks aggressive, to meet profit targets. Thirdly, the risk-taking channel can operate through how policies are announced and the reaction function of the monetary policy authority including the effect produced by the idea that the reaction function of the monetary policymaker is effective in curtailing excessive downside risk.

While the debate has been based on the prolonged period of low-interest rates in the US and EU in the five years preceding the crisis, it is important to admit that that argument only loosely relates to the situation in sub-Sahara Africa. Interest rates have not been low in Africa both in absolute terms and when compared to rates in the US and EU for any period in the past but since the end of the global financial crisis, interest rates in many sub-Sahara African countries have demonstrated a tendency to decline (see Fig. 1). That notwithstanding, there is a justification for studying the risk-taking channel of monetary policy in the context of Africa, especially if we consider context-specific conditions and dynamics. To begin with, while interest rates have not fallen so low as in the case of the US and EU, they have fallen to historically low levels in many countries in the years after the crisis, particularly for countries that are part of common currency areas. Moreover, the business community continues to advocate for even lower interest rates so that cost of credit can fall and boost real investments.

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Also, African countries are bank-oriented and coupled with the prevailing policy environment in many countries provides us with an ideal setting for analysing this transmission channel of monetary policy. Consequently, our focus in this study is to empirically determine the relationship between monetary policy and risk-taking of banks operating in 37 countries in sub-Sahara Africa, using data from 2001 to 2015 and for different types of risk. We also determine the impact of size and capitalisation in moderating banks’ risk-taking behaviour. The results show evidence of the existence of a risktaking channel of monetary policy in the context of sub-Sahara Africa when assessed using different risk measures. The impact of monetary policy on bank risk-taking in sub-Sahara Africa has yet to be examined in the literature. We introduce a novelty in risk-taking literature for Africa by using a large cross-country panel consisting of only Africa countries. Additionally, this study highlights bank-specific characteristics that may have an impact on risk-taking.

The rest of the paper is organised as follows. Section 2 reviews the risk-taking literature. Section 3 describes the data and methods employed. Section 4 reports and discusses the results. Section 5 concludes.

LITERATURE REVIEW Banking Sector Risk The strength or weakness of a banking industry is a function of how much risks it is exposed to. Laeven & Valencia (2013) observed that there have been 147 banking crises across the world from 1970 to 2011 and this was accompanied by 218 currency crises. They observed that those crises often occurred in one country and then spread to other countries due to increasing integration of the global financial system.

Cihák & Schaeck (2010) agreed with this observation and affirmed that those crises are difficult to predict.

Laeven & Valencia's (2013) report also revealed that many of the factors that led to most banking crises were within the control of bank management and were due to internal policies and decisions. Industry players have therefore tended to focus more attention on the management of uncertainties that the sector is prone to. Consequently, risk and risk management have become topical in the banking literature. This has particularly been the case following the global financial crisis that started in 2008.

One is likely to conclude that given the structural deficiency of Africa's economy, there is an enormous risk in the banking industry. There is the need however, to obtain an empirical basis for the sources and nature of the factors that put the financial system at risk and how the sector responds to these factors over time. This is because the risk in the banking industry has proven to be crucial for the overall financial health of any economy (Alter & Schüler, 2012). At least this is evident from the impact of banking crisis in the US, Europe and Asia (Black, Correa, Huang, & Zhou, 2016; Carbó-Valverde, Benink, Berglund, & Wihlborg, 2015).

Government intervenes in the economy from time to time to achieve some macroeconomic objective. One of the tools of intervention has been the monetary policy stance. Loose monetary policy can impact bank risk-taking in many ways such as the valuation of bank assets (Altunbas, Gambacorta, & Marques-Ibanez, 2010). In terms of valuation, lower interest rates increase the value of assets, incomes and collateral and may cause banks to modify their estimation of risk downward, thus increasing their risk appetite. Our focus in this study is to determine the impact of monetary policy on bank risk-taking in sub-Sahara Africa.

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Theories Moral Hazard Theory The moral hazard theory is commonly cited to explain bank risk-taking behaviour. The theory emphasises asymmetric information and deposit insurance shielding banks from the control of depositors (Jokipii & Milne, 2011; Rahman, Zheng, & Ashraf, 2015). Empirical studies have shown that capital adequacy regulation may reduce the number of risky assets that are held by banks (Zheng, Xu, & Liang, 2012). They also show that if we assume a utility function for a risk adverse bank, their portfolio composition may be distorted towards more risky assets (Hamza & Saadaoui, 2013; Mensah, 2013). As a result, average risk can increase and the bank may require risk consistent weights to adjust for moral hazard (Zheng et al., 2012).

Franchise Value Theory The franchise value (or charter value) theory explains bank risk-taking in a framework of franchise value. Franchise value can be defined as how much of the value of the bank would be foregone should the bank be closed (Chen, Hwang, & Liu, 2012). Factors that may increase franchise value include regulatory restrictions on entry and competition in the industry. It argues that since shareholders would lose big in case of closure, banks with higher franchise value would have lower incentives to take higher risk.

Even though traditional theory suggests that high franchise value reduces bank risk-taking incentives, the recent financial crisis saw banks with exceptionally highly valuable franchises getting exposed.

A Monetary Policy Transmission Channel There are two ways that monetary policy can primarily affect bank risk-taking. Firstly, it affects the valuations, incomes and cash flows of banks and this impacts on how they measure risks. A low-interest rate will bloat the value of their assets and increase their valuation, thus increasing their risk appetite (Altunbas, Gambacorta, & Marques-Ibanez, 2014). Low-interest rates could make banks aggressive in an attempt to meet profit targets (Altunbas, Gambacorta, & Marques-Ibanez, 2012; de Moraes, Montes, & Antunes, 2016). Given the positive relationship between risk and return, they will take on more risk to meet their set nominal targets (Altunbas et al., 2012).

Banks are thought to take on more risk in a lax monetary policy environment. This means that while monetary policy authorities seek to achieve desirable macroeconomic ends using lax monetary policy measures, this can trigger bank failures as a result of excessive risk-taking by banks and defeat the intended purpose of the policy (Bekaert, Hoerova, & Lo Duca, 2013). What we do not know is how long this process takes. For example, Jiménez, Ongena, Peydro, & Saurina (2014) and Ioannidou et al.

(2015) find evidence that in Spain and Bolivia, a ‘‘too accommodative’’ monetary policy led to additional risk-taking by banks before the global financial crisis in 2008. We know that before the crisis several countries were in recessions and monetary authorities used the traditional method of expansionary monetary policies to stimulate investment and to help their economies recover. Similar to the findings of Jiménez et al., (2014) and Ioannidou et al. (2015), Altunbas, Gambacorta, & Marques-Ibanez (2010) and Maddaloni & Peydró (2011) find supportive evidence of the risk-taking channel of monetary policy using cross-country data.

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EMPIRICAL STRATEGY Econometric Specification

(1)

Where t refers to the index for time (1………T), the year and i is the index for the individual bank (1……N) and and are the error terms and j=1,……J.

Estimation The LSDV estimator is pooled OLS including a set of N-1 dummy variables which identify the heterogeneity within each panel and hence an additional N-1 parameter. Due to the constant that is included, one of the individual dummies is dropped. The use of Least Squares Dummy Variable Model helps to obtain the bank specific effects which absorb omitted variables that differ from one bank to another but are constant over time. The LSDV estimates are optimal if the error processes have the same variance (homoscedasticity requirement) and the errors are independent of each other (no autocorrelation requirement). We examined the data for homoscedasticity and autocorrelation and found that most of the specifications had heteroscedastic and autocorrelated error terms. It is however common to find panel processes being plagued by complex error correction issues and so we report robust standard errors instead of the standard OLS errors following a similar approach to Ofoeda et al. (2012). This ensures that the LSDV estimates are optimal. For robustness still, a panel corrected standard error (PCSE) technique was implemented. The PCSE corrects for heteroscedasticity and autocorrelation.

The two techniques use different error correction processes to achieve similar outcomes. The LSDV with robust standard error implements the white-correction whiles PCSE.

Measuring Risk-Taking ◆ Non-Performing Loans Ratio (NPLR) The non-performing loans ratio is a measure that shows the stock of loans that are non-performing compared to total loan stock at a given point in time (Angkinand & Wihlborg, 2010). We estimate the model using NPLR (expressed as a percentage) as a measure of credit risk-taking (Shahid, Haque, & Shahid, 2016). We transform this variable by taking logs as follows: (2) Higher values of lnNPLR denote higher credit risk while lower values indicate lower credit risk.

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◆ Loan Loss Provision to Gross Loans Ratio (LLP) Asset risk refers to uncertainties in the realisation of the assets. A high risk means that there is a greater uncertainty on whether a bank would be able to realise its assets. We measure asset risk using the ratio of loan loss reserve to gross loans (expressed as a percentage) (Distinguin, Roulet, & Tarazi, 2013; Sarkar & Sensarma, 2016). Loans are an important part of bank assets and can be large relative to other asset classes. Banks therefore use the loan balance as a basis for making a provision for potential losses to assets. Banks keep the reserve in anticipation of shocks to asset value. The reserve is itself recorded as an asset in the balance sheet of the firm and it reflects movements in the assets of the bank and asset quality. A high reserve helps banks to absorb losses better and this makes banks with a higher reserve less prone to bankruptcy. A higher ratio is desirable, however, to present the result consistently, we take the inverse by multiplying it by minus 1. This measure is stated formally as: (3)

Other Variables Monetary Policy: For this study, we consider the monetary policy rate as a desirable measure for monetary policy (Fiador & Biekpe, 2015; Matemilola, Bany-Ariffin, & Muhtar, 2015). The monetary policy rate is an explicit statement by the monetary authorities. It signals interest rates in the economysomething that directly affects bank performance.

Bank Size: Bank size has been found to influence bank risk-taking across time and contexts (GarcíaKuhnert, Marchica, & Mura, 2013; Laeven, Ratnovski, & Tong, 2016; Rahman et al., 2015; Terraza, 2015).

We use the natural log of total assets following Bhagat, Bolton, & Lu (2015), Ioannidou et al. (2015) and Stolz & Wedow (2011) as a proxy for bank size. This has the advantage of reducing the possibility of heteroscedasticity and to pull in extreme values (García-Kuhnert et al., 2013).

Profitability: We proxy profitability by ROA since this is readily obtainable and is the preferred measure of profitability in the literature (Dong, Firth, Hou, & Yang, 2016; Kolapo, Ayeni, & Oke, 2012).

Competition: We follow Sarkar & Sensarma (2016) and Delis & Kouretas (2011) to measure competition using the Concentration Ratio (CR) in the banking industry. The Concentration Ratio measures how much of total industry assets are owned by a selected number of big banks. It measures industry dominance. We use the ratio of assets owned by the three largest banks (known as CR3). A high ratio signifies low competition because it means that few banks control a significant portion of the industry; they have market power. It is expected that high competition will lead banks to take more risks in a bid to remain competitive in the market.

Cost Efficiency: Cost efficiency is measured by the ratio of expense to revenue or cost to income (expressed as a percentage). It tells us how many dollars it costs to generate a dollar in revenue. A high ratio signals low efficiency and this is expected to reduce profit and increase risk (Kar, 2012; Zaini Abd Karim, Chan, & Hassan, 2010). It is plausible to expect that banks that are cost efficient are likely to be taking less risk.

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Financial Openness: Financial openness has been used to explain bank risk-taking in previous studies (Luo, Tanna, & De Vita, 2016). It indicates the level of regulation in the financial system of a country. We measure financial openness with the KAOPEN Index developed by Chinn & Ito (2006).

RESULTS AND DISCUSSION Summary Statistics Table 2 shows the descriptive statistics for all the variables used in the study. The table presents information on the number of observations, the mean and standard deviation, and the minimum and maximum values for each variable. This allows us to see the upper and lower bounds of each variable in the dataset as well as the averages and dispersion of the variables. For brevity, only a few variables are discussed. The study included 534 banking organisations listed in the BankScope database from 37 countries in Sub-Sahara Africa for a period of 15 years starting from 2001 to 2015. The data is unbalanced and contains gaps. The table also shows that the data on some variables were unavailable for some banks and countries in some years and this accounted for some variables having fewer observations this also shows in the regression tables. Similar to Kuranchie-pong, Bokpin, & Andoh, (2016) and Pathan, (2009) we observe that some of the variables such as policy rate and cost efficiency recorded outliers. To deal with the extreme values, some variables were winsorized and others were log-transformed. We present additional descriptive statistics for variables that were winsorized or log transformed.

The mean (standard deviation) of Competition is 69.73 (18.97) which shows that most banking sectors in Africa are weakly competitive. Indeed, we find support for this conclusion in the data. The data shows that many of the countries in the sample have had concentration ratios (CR3) above 50% for most of the sample period. Another observation is that, except for a few countries like Nigeria and Ghana whose banking sector became more competitive over time, many others were becoming less competitive. This could be explained partly because of regulatory factors which led to bank mergers and acquisitions over the period. The mean (standard deviation) of bank assets is USD1,210,417 (USD 6,578,848). This shows that there is significant heterogeneity between banks in terms of size as can be seen from the minimum and maximum value.

The mean (standard deviation) of monetary policy rate was 10.81% (7.435%). This shows that the average country in the sample had tight monetary policy regimes compared with rates in more developed economies. High policy rates are used to control inflationary tendencies and signal the cost of loanable funds. Fiador (2015) reported that during the period of this study many countries in Africa had high inflation rates. This is shown by the range and mean of the inflation. This could explain why the policy rates were high as found.

Correlation Analysis Correlation coefficients of variables are reported in Table 3. Coefficients that are significant at 10% are reported with a star. Monetary policy shows a negative and significant correlation with the solvency risk, liquidity risk and capital risk. We interpret the sign of the correlation coefficient to mean that expansionary policy rates are associated with high risk-taking in terms of default, liquidity, and capital risk. This strengthens the conclusions of the regression results discussed below.

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Model Diagnostics No Multicollinearity The results in table 3 show that no pair of variables have a correlation coefficient greater than 0.5 and so there is no multicollinearity in our data. Thus, the models specified do not suffer from multicollinearity. This implies that the standard errors of the model estimates are not inflated and are not biased.

Homoscedasticity This assumption is required to ensure that the LSDV estimators are efficient, unbiased, and consistent.

If this assumption is violated, the model is said to be heteroscedastic, that is, the error processes are not stationary. We perform this test using the Breusch-Pagan / Cook-Weisberg test for heteroscedasticity.

The results of the test are shown in table 4. The results show that, at 10% significance level, all the model specifications are heteroscedastic. Even though models are still unbiased and consistent in the presence of heteroscedasticity, they are no longer efficient. We remedied this by reporting robust standard errors.

No Autocorrelation We test for first order autocorrelation using the Wooldridge test for autocorrelation. The results are presented in table 5. If the assumption of no autocorrelation is violated, LSDV estimators are still unbiased but are not efficient. The results show that there is first order autocorrelation. To correct for this violation, we report robust standard errors.

Exogeneity Exogeneity is a required condition for optimal LSDV estimates. The exogeneity condition requires that no explanatory variable be correlated with a corresponding error. If this condition does not hold, then there is said to be endogeneity within the model. Based on economic theory, we expected that monetary policy rate will be endogenous. To establish this, we conducted the test of endogeneity by using the Durbin and Wu-Hausman tests for endogeneity. The results are presented in table 6. The results of the tests in table 6 show that all models are exogenous.

Regression Results Monetary Policy and Risk-taking Table 7 reports the empirical results of the relationship between monetary policy and risk-taking among banks in sub-Sahara Africa. In each column, a positive coefficient shows that monetary policy rate moves in the same direction as risk-taking and a negative coefficient shows otherwise. We present results for the LSDV regression and the PCSE regression. Our discussion is based on the LSDV results for brevity because the findings are qualitatively similar across techniques.

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Credit Risk-taking

We find that the relationship between monetary policy and credit risk-taking is positive and significant.

A 1% increase in monetary policy rate will increase banks’ credit risk by 0.5726%. Put differently, if monetary policy rates double from their current average to about 22%, banks’ credit risks (in this case non-performing loans ratio) will increase by about 60%. This means that in periods of high monetary policy rates, there is an increase in non-performing loans, hence high credit risk. We explain this in two ways. Firstly, high interest rates lead to high borrower default because of increased cost of borrowing.

This directly increases the NPLR. Secondly, because high interest rates signal high borrower solvency risks, risk-averse banks will reduce lending. As the denominator decreases, the NPLR is increased. At least one of these must occur for credit risk to increase or both may occur but at different magnitudes.

From the perspective of the borrower, as interest rates increase (particularly for floating rates); they tend to be unable, or less likely to meet their loan obligation.

This is consistent with Forssbæck (2011), Tabak et al., (2014), Adhikari & Agrawal (2014) and Agoraki, Delis, & Pasiouras, (2011). Forssbæck (2011) used NPLR and inverse Z-Score as proxies for risk-taking and their work included real interest rate- a proxy of monetary policy measure- and found similar results. The coefficients of monetary policy rate are smaller for solvency risk than for credit risk suggesting that in periods of rising policy rates, credit risk increases faster than the rate at which solvency risk increases in periods of falling policy rates. Thus, as policy rates increases, banks’ credit risk rises faster than the rate at which their solvency risk declines.

It can be shown that the signs of the results for solvency risk and credit risk and our interpretation are logically consistent. To see this, assume that as we explained in the case of the latter, there will be fewer borrower defaults when policy rates are lower. On the other hand, we explained in the case of the former that in a period of low policy rate banks will be more aggressive to meet targeted nominal returns; undertaking more income generating activities such as giving out more loans1. As they do these, the denominator of the NPL ratio become bigger hence having less credit risk. Thus, solvency risk and credit risk will move in opposite directions for any given change in the monetary policy rates.

Asset Risk

The results show that when monetary policy rate is reduced by 1%, asset risk rises by 1.37%. This can be explained by similar factors as in solvency risk. As monetary policy rates decline, the value of banks assets will increase and so any adverse event will impact more on banks. In orderto create a buffer against this uncertainty, banks will keep higher loan loss reserves.

The Effect of Bank Size and Profitability We investigate the effect of size and profitability in the monetary policy channel. The result suggests that the impact of monetary policy rate on bank risk is diminished for bigger banks, for solvency risk, liquidity risk, asset risk and capital risk but amplified for credit risk. This finding is partly consistent with Delis & Kouretas (2011) who found that in the euro area interest rates impacted the risk of bigger banks less than smaller banks. In the case of Delis & Kouretas (2011) they examined asset and credit risks only.

1 Holding all of our arguments in the case of solvency risk

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For profitability, the result show that the risks of more profitable banks were impacted less by monetary policy for all types of risks except capital risk. For capital risk, more profitable banks were impacted more by the monetary policy rate. These relationships were however only statistically significant for asset risk and capital risk.

CONCLUSION AND POLICY RECOMMENDATION This paper contributes to the bank risk-taking literature by providing evidence of a relationship between monetary policy and risk-taking in developing countries. Banking researchers have always known that high risk-taking was a threat to banks. However, little attention was paid to the potential for macroeconomic policy to affect banks’ risk appetite before the 2008 financial crisis. The results of this study have shown that a lax monetary policy regime is associated with increases in banks’ risk-taking behaviour. This holds for different risk types.

The results show that low monetary policy rates cause banks to take more risk. Secondly, changes in monetary policy influence changes in risk-taking behaviour. Banks form expectations of monetary policy stance and revise their risk appetite accordingly. Also, bank characteristics such as size and capitalisation are important in moderating the effect of monetary policy on bank risk-taking. Bigger banks and well-capitalised banks take more risk on average. Thus, there is an insulation effect for size and capitalization. Industry characteristics are important in moderating the effect of monetary policy on bank risks. Banks in competitive national sectors are insulated from the adverse effects. A strong domestic currency was found to influence higher risk-taking by banks. All our results were robust to the type of risk-taking measure. In the case of credit risk, it was shown that adjusting for bank and industry characteristics were crucial in arriving at our conclusion. These results confirm findings by previous authors who studied banks in other countries. We, therefore, show that banks in Africa are not different in their response to monetary policy. The policy implication of our results is that central banks should be modest in easing the monetary condition.

We recommend that central banks in Africa should slow down their pace of monetary policy expansion.

Even though it is a good intention to ease monetary conditions to reduce the cost of borrowing and grow the economy, this could threaten the stability of the financial system. Such an intention should be carried out gradually over a long time.

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Pathan, S. (2009). Strong boards, CEO power and bank risk-taking. Journal of Banking and Finance, 33(7), 1340–1350. https://doi.org/10.1016/j.jbankfin.2009.02.001 Rahman, M. M., Zheng, C., & Ashraf, B. N. (2015). Bank Size, Risk-taking and Capital Regulation in Bangladesh.

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Figures and Tables: Figure 1: Money Policy in Sub-Sahara Africa

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ABOYADANA. G.  MONETARY POLICY AND BANK RISK-TAKING IN SUB-SAHARA AFRICA

Table 1: Variables Variable

Measurement

Expected Sign

Data Source

Credit Risk (NPLR)

Ratio of Non-Performing Loans to Total Loans

BankScope

Credit Risk (LLP)

Ratio of loan loss reserves to total loans

BankScope

Capital (CAP)

Ratio of Shareholders’ equity to total Bank Assets

BankScope

Liquidity (LIQ)

Ratio of liquid assets to total deposits and shortterm borrowing

BankScope

Bank Size (SIZE)

Natural log of Total Assets

+

BankScope

Profitability (ROAA)

Ratio of Profit after tax to Total assets

-

BankScope

Cost Efficiency (COSTEFF)

Operating cost to Operating income.

-

BankScope

Monetary Policy (MPR)

-

IMF

Competition (CR3)

+ -

World Bank

Inflation Rate (INF)

+

World Bank

GDP Growth (GDP)

+

World Bank

Financial Openness

-

Chinn & Ito (2006)

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Table 2: Summary Statistics Untransformed Variables Inflation GDP Growth Competition Financial Openness Monetary Policy Rate Profitability Capitalization Cost Efficiency Size (Total Assets-USD) LLP NPL Winsorized Variables Cost Efficiency Inflation Log transformed Variables Monetary Policy Rate NPL Size Profitability

Mean 9.684 5.399 69.73 -0.348 10.81 1.201 16.24 66.78 1210417 6.364 8.45

SD 20.95 4.502 18.97 1.436 7.435 6.247 15.87 47.59 6578848 8.041 10.94

Min -35.84 -36.70 23.32 -1.895 2 -160.1 -46.03 0.141 88.8754 100 0.008

Max 359.9 63.38 100 2.389 70 78.37 99.85 732.9 124e+08 -0.240 100

61.98 7.647

19.31 4.832

35.47 1.298

98.09 16.19

2.189 7.672 12.15 0.571

0.613 1.757 1.659 0.989

0.693 0.693 4.487 -6.908

4.248 11.59 18.64 4.361

Source: Research Data

Table 3: Correlation Matrix 1

3

4

5

6

7

8

9

1. Inflation

1.00

2. GDP Growth

-0.06*

1.00

3. Competition

0.07*

-0.12*

1.00

4. Monetary Policy Rate

0.48*

0.08*

-0.03*

1.00

5. Profitability

-0.00

0.01

0.02

-0.00

1.00

6. Cost Efficiency

0.01

-0.00

0.01

-0.06*

0.04*

1.00

7. LLP

0.01

0.03*

-0.03

0.01

-0.01

-0.06*

1.00

8. Size

-0.01

-0.07*

0.04*

-0.06*

-0.00

0.02

-0.01

1.00

9. NPL

-0.02

-0.08*

0.07*

-0.00

-0.03

0.00

0.06

-0.03

1.00

10. Financial Openness

-0.03*

-0.02*

-0.28*

0.15*

0.02

0.00

0.02

-0.07*

-0.06*

Source: Research Data

30

2

10

1.00

EJAE 2021  18 (1)  15 - 38

ABOYADANA. G.  MONETARY POLICY AND BANK RISK-TAKING IN SUB-SAHARA AFRICA

Table 4: Breusch-Pagan / Cook-Weisberg test for heteroscedasticity Credit Risk

Asset Risk

Chi2(1)

12.1

134.01

Prob>Chi2

0.0005***

0.0000***

Source: Research Data Table 5: Wooldridge test for autocorrelation H0: No first order autocorrelation Df

F-Statistic

Prob>F

Credit Risk

F (1,35)

9.9111

0.0034***

Asset Risk

F (1,51)

18.665

0.0001***

Source: Research Data Table 6: Test of Endogeneity H0: Variables are Exogenous Model

Durbin(score) Chi2(1)

Prob>Chi2

Wu-Hausman F (v1, v2)

Prob>F

Credit Risk

0.0820 (1)

0.7745

0.0793 (1,262)

0.7784

Asset Risk

0.0402 (1)

0.8410

0.0393 (1,376)

0.8429

Source: Research Data

31

EJAE 2021  18(1)  15 - 38

ABOYADANA. G.  MONETARY POLICY AND BANK RISK-TAKING IN SUB-SAHARA AFRICA

Table 7: Monetary Policy and Risk-taking (LSDV) (1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Variables

NPL

NPL

NPL

NPL

NPL

NPL

NPL

NPL

Cost Efficiency

0.012***

0.013***

0.013***

0.015***

0.012***

0.013***

0.012***

0.014***

(0.004)

(0.004)

(0.004)

(0.004)

(0.004)

(0.004)

(0.004)

(0.004)

1.409***

1.186***

1.036***

0.534***

0.749**

0.549*

0.379

-0.070

(0.312)

(0.260)

(0.287)

(0.174)

(0.365)

(0.320)

(0.337)

(0.247)

0.731***

0.734***

0.749***

0.759***

(0.248)

(0.249)

(0.248)

(0.251)

Monetary Policy Monetary Policyt-1 MPRCAP MPRLIQ

-0.032

-0.049**

-0.028

-0.042*

(0.024)

(0.024)

(0.023)

(0.024)

-0.014*** -0.017***

-0.013**

-0.016***

(0.005)

(0.005)

(0.006)

(0.006)

0.015

0.024

0.027

0.047

-0.031

-0.023

-0.019

-0.002

(0.049)

(0.048)

(0.050)

(0.049)

(0.052)

(0.051)

(0.053)

(0.053)

0.009

0.015

0.016

0.027

0.002

0.006

0.007

0.017

(0.026)

(0.025)

(0.026)

(0.025)

(0.026)

(0.025)

(0.026)

(0.026)

0.008

0.009

0.001

0.000

-0.006

-0.005

-0.013

-0.013

(0.022)

(0.022)

(0.022)

(0.022)

(0.023)

(0.023)

(0.023)

(0.023)

0.015***

0.016***

0.016***

0.018***

0.013***

0.013***

0.014***

0.015***

(0.004)

(0.004)

(0.004)

(0.004)

(0.004)

(0.004)

(0.004)

(0.004)

-0.167

-0.177

-0.197

-0.224*

-0.226*

-0.236*

-0.253*

-0.277**

(0.130)

(0.126)

(0.131)

(0.126)

(0.134)

(0.131)

(0.134)

(0.130)

0.155***

0.184***

0.198***

0.260***

0.163***

0.188***

0.203***

0.257***

(0.043)

(0.036)

(0.041)

(0.028)

(0.044)

(0.037)

(0.041)

(0.030)

0.093*

0.021***

0.132**

0.023***

0.081

0.018***

0.114**

0.020***

(0.053)

(0.006)

(0.053)

(0.006)

(0.051)

(0.006)

(0.052)

(0.006)

0.035***

0.040***

0.007**

0.007**

0.034***

0.039***

0.009**

0.010***

(0.011)

(0.011)

(0.004)

(0.004)

(0.011)

(0.011)

(0.004)

(0.004)

Observations

547

547

547

547

515

515

515

515

R-Squared

0.948

0.948

0.948

0.947

0.949

0.949

0.948

0.948

Financial Openness GDP Growth Inflation Competition Profitability Size CAP LIQ

Source: Research Data Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

32

EJAE 2021  18 (1)  15 - 38

ABOYADANA. G.  MONETARY POLICY AND BANK RISK-TAKING IN SUB-SAHARA AFRICA

Table 8: Monetary Policy and Risk-taking (1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Variables

LLP

LLP

LLP

LLP

LLP

LLP

LLP

LLP

Cost Efficiency

0.050***

0.050***

0.057***

0.056***

0.051***

0.050***

0.057***

0.057***

(0.017)

(0.017)

(0.017)

(0.017)

(0.018)

(0.018)

(0.017)

(0.017)

4.702***

4.978***

1.581**

1.643***

5.036***

5.326***

2.117

2.201*

(0.980)

(0.892)

(0.798)

(0.599)

(1.458)

(1.395)

(1.328)

(1.234)

-0.813

-0.846

-0.988

-0.996

(1.261)

(1.264)

(1.291)

(1.292)

Monetary Policy Monetary Policyt-1 MPRCAP MPRLIQ

0.030

0.006

0.028

0.007

(0.039)

(0.045)

(0.038)

(0.043)

-0.081*** -0.080***

-0.078*** -0.077***

(0.015)

(0.015)

(0.016)

(0.016)

-0.073

-0.083

-0.005

-0.007

0.000

-0.008

0.086

0.083

(0.229)

(0.228)

(0.232)

(0.232)

(0.226)

(0.225)

(0.229)

(0.229)

-0.072

-0.075

-0.026

-0.026

-0.034

-0.037

0.013

0.012

(0.078)

(0.078)

(0.078)

(0.078)

(0.082)

(0.082)

(0.082)

(0.082)

-0.009

-0.005

-0.045

-0.044

-0.004

0.000

-0.030

-0.029

(0.078)

(0.077)

(0.080)

(0.079)

(0.079)

(0.079)

(0.082)

(0.081)

-0.022*

-0.023*

-0.011

-0.011

-0.017

-0.018

-0.006

-0.006

(0.013)

(0.013)

(0.013)

(0.013)

(0.013)

(0.013)

(0.013)

(0.013)

0.270

0.276

0.118

0.120

0.210

0.216

0.063

0.065

(0.327)

(0.327)

(0.332)

(0.332)

(0.334)

(0.334)

(0.340)

(0.339)

-0.112

-0.148

0.225**

0.217**

-0.080

-0.114

0.246**

0.236**

(0.121)

(0.112)

(0.110)

(0.093)

(0.124)

(0.115)

(0.110)

(0.096)

-0.092

-0.029*

-0.034

-0.022

-0.086

-0.027

-0.036

-0.021

(0.077)

(0.017)

(0.088)

(0.017)

(0.075)

(0.017)

(0.085)

(0.017)

0.092***

0.090***

-0.050*** -0.050*** 0.087***

0.085***

-0.048**

-0.048**

(0.029)

(0.029)

(0.019)

(0.019)

(0.030)

(0.030)

(0.020)

(0.020)

Observations

704

704

704

704

671

671

671

671

R-Squared

0.448

0.447

0.431

0.431

0.442

0.441

0.427

0.427

Financial Openness GDP Growth Inflation Competition Profitability Size CAP LIQ

Source: Research Data Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

33

EJAE 2021  18(1)  15 - 38

ABOYADANA. G.  MONETARY POLICY AND BANK RISK-TAKING IN SUB-SAHARA AFRICA

Table 9a: Countries without exchange controls (1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Variables

NPL

NPL

NPL

NPL

NPL

NPL

NPL

NPL

Cost Efficiency

0.011***

0.013***

0.011***

0.014***

0.011***

0.012***

0.011***

0.013***

(0.004)

(0.004)

(0.004)

(0.004)

(0.004)

(0.004)

(0.004)

(0.004)

1.502***

1.256***

1.383***

0.877***

0.588

0.368

0.547*

0.120

(0.311)

(0.274)

(0.274)

(0.170)

(0.365)

(0.336)

(0.318)

(0.242)

0.969***

0.960***

0.974***

0.981***

(0.270)

(0.271)

(0.268)

(0.271)

Monetary Policy Monetary Policyt-1 MPRCAP MPRLIQ Financial Openness GDP Growth Inflation Competition Profitability

-0.041*

-0.048**

-0.037*

-0.039*

(0.023)

(0.022)

(0.021)

(0.021)

-0.005

-0.011*

-0.002

-0.006

(0.006)

(0.006)

(0.006)

(0.006)

0.112**

0.119**

0.118**

0.137***

0.067

0.073

0.069

0.084

(0.051)

(0.051)

(0.052)

(0.051)

(0.054)

(0.054)

(0.054)

(0.054)

0.019

0.026

0.022

0.035

0.019

0.026

0.020

0.031

(0.027)

(0.026)

(0.027)

(0.026)

(0.026)

(0.025)

(0.026)

(0.025)

0.038

0.038

0.037

0.037

0.024

0.024

0.023

0.023

(0.023)

(0.023)

(0.023)

(0.024)

(0.024)

(0.024)

(0.024)

(0.024)

0.012***

0.013***

0.013***

0.014***

0.009*

0.009**

0.009**

0.010**

(0.004)

(0.004)

(0.004)

(0.004)

(0.004)

(0.004)

(0.004)

(0.004)

-0.380*** -0.390*** -0.388*** -0.415*** -0.451*** -0.462*** -0.454*** -0.477*** (0.114)

(0.111)

(0.114)

(0.110)

(0.118)

(0.115)

(0.117)

(0.113)

0.105**

0.136***

0.119***

0.181***

0.119***

0.148***

0.124***

0.175***

(0.043)

(0.038)

(0.040)

(0.030)

(0.044)

(0.039)

(0.041)

(0.032)

0.116**

0.024***

0.132***

0.026***

0.104**

0.021***

0.109**

0.022***

(0.051)

(0.006)

(0.049)

(0.006)

(0.048)

(0.006)

(0.047)

(0.006)

0.019

0.031**

0.008**

0.009**

0.013

0.023*

0.010**

0.010***

(0.013)

(0.013)

(0.004)

(0.004)

(0.013)

(0.013)

(0.004)

(0.004)

Observations

444

444

444

444

414

414

414

414

R-squared

0.954

0.953

0.953

0.953

0.955

0.954

0.955

0.954

Size CAP LIQ

Source: Research Data Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

34

EJAE 2021  18 (1)  15 - 38

ABOYADANA. G.  MONETARY POLICY AND BANK RISK-TAKING IN SUB-SAHARA AFRICA

Table 9b: Countries without exchange controls (1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Variables

LLP

LLP

LLP

LLP

LLP

LLP

LLP

LLP

Cost Efficiency

0.040**

0.039*

0.044**

0.043**

0.040*

0.039*

0.044**

0.043**

(0.020)

(0.020)

(0.020)

(0.020)

(0.021)

(0.021)

(0.020)

(0.020)

4.306***

4.631***

2.283**

2.460***

4.175**

4.545**

2.148*

2.393**

(1.125)

(1.090)

(0.891)

(0.706)

(1.961)

(1.985)

(1.292)

(1.188)

-0.397

-0.429

-0.325

-0.343

(1.416)

(1.425)

(1.394)

(1.396)

Monetary Policy Monetary Policyt-1 MPRCAP MPRLIQ

0.040

0.015

0.041

0.020

(0.043)

(0.044)

(0.042)

(0.044)

-0.063**

-0.059**

-0.060**

-0.057*

(0.027)

(0.027)

(0.031)

(0.030)

0.147

0.142

0.236

0.232

0.216

0.212

0.313

0.307

(0.264)

(0.264)

(0.246)

(0.246)

(0.265)

(0.265)

(0.243)

(0.243)

-0.059

-0.063

-0.008

-0.011

-0.016

-0.020

0.031

0.027

(0.090)

(0.090)

(0.093)

(0.093)

(0.094)

(0.094)

(0.099)

(0.099)

0.049

0.056

0.054

0.057

0.047

0.056

0.056

0.060

(0.091)

(0.089)

(0.092)

(0.091)

(0.094)

(0.092)

(0.095)

(0.093)

-0.026

-0.027

-0.018

-0.019

-0.018

-0.020

-0.012

-0.012

(0.017)

(0.017)

(0.017)

(0.017)

(0.016)

(0.016)

(0.017)

(0.017)

0.421

0.432

0.357

0.364

0.343

0.357

0.274

0.283

(0.418)

(0.420)

(0.404)

(0.404)

(0.436)

(0.438)

(0.416)

(0.416)

-0.110

-0.156

0.112

0.088

-0.070

-0.119

0.141

0.111

(0.153)

(0.149)

(0.135)

(0.119)

(0.158)

(0.154)

(0.142)

(0.127)

-0.108

-0.022

-0.044

-0.011

-0.111

-0.021

-0.054

-0.011

(0.088)

(0.019)

(0.088)

(0.018)

(0.086)

(0.018)

(0.087)

(0.018)

0.061

0.053

-0.069*** -0.069*** 0.057

0.048

-0.067**

-0.067**

(0.072)

(0.070)

(0.027)

(0.026)

(0.081)

(0.079)

(0.029)

(0.029)

Observations

494

494

494

494

466

466

466

466

R-squared

0.429

0.428

0.422

0.422

0.419

0.418

0.413

0.413

Financial Openness GDP Growth Inflation Competition Profitability Size CAP LIQ

Source: Research Data Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

35

EJAE 2021  18(1)  15 - 38

ABOYADANA. G.  MONETARY POLICY AND BANK RISK-TAKING IN SUB-SAHARA AFRICA

Table 10a: Countries with exchange controls Variables Cost Efficiency Monetary Policy

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

NPL

NPL

NPL

NPL

NPL

NPL

NPL

NPL

0.005

0.004

0.005

0.004

0.003

0.002

0.002

0.002

(0.009)

(0.009)

(0.010)

(0.009)

(0.010)

(0.010)

(0.010)

(0.010)

-0.020

-1.752

0.823

-1.117

-4.193

-5.998

-0.268

-1.354

(4.662)

(4.665)

(2.864)

(0.935)

(5.395)

(4.753)

(4.284)

(2.888)

0.013

0.170

0.152

0.226

(2.735)

(2.672)

(2.768)

(2.725)

Monetary Policyt-1 MPRCAP MPRLIQ

-0.255

-0.228

-0.245

-0.119

(0.350)

(0.320)

(0.350)

(0.324)

0.019

0.011

0.092

0.084

(0.088)

(0.084)

(0.073)

(0.073)

-6.153

-8.472

-5.072

-7.652**

-12.435** -14.592** -7.047

-8.376***

(6.745)

(6.876)

(4.382)

(2.956)

(6.014)

(5.622)

(4.370)

(2.801)

-0.049

-0.045

-0.049

-0.046

-0.142*

-0.143*

-0.133*

-0.134*

(0.080)

(0.080)

(0.079)

(0.080)

(0.077)

(0.076)

(0.076)

(0.076)

0.027

0.021

0.024

0.020

0.043

0.040

0.031

0.030

(0.075)

(0.074)

(0.073)

(0.072)

(0.086)

(0.086)

(0.083)

(0.083)

-0.002

-0.001

-0.002

-0.001

-0.001

0.000

0.001

0.002

(0.012)

(0.012)

(0.012)

(0.012)

(0.012)

(0.012)

(0.012)

(0.012)

0.848**

0.841**

0.854**

0.845**

0.830**

0.832**

0.865**

0.865**

(0.352)

(0.350)

(0.358)

(0.354)

(0.336)

(0.334)

(0.373)

(0.370)

-0.053

-0.091

-0.049

-0.086

-0.151

-0.182

-0.118

-0.135

(0.208)

(0.210)

(0.201)

(0.200)

(0.192)

(0.190)

(0.190)

(0.186)

0.374

0.022

0.336

0.022

0.347

0.009

0.172

0.008

(0.480)

(0.055)

(0.441)

(0.055)

(0.483)

(0.056)

(0.449)

(0.052)

-0.015

-0.004

0.012

0.012

-0.114

-0.102

0.016*

0.017*

(0.122)

(0.117)

(0.009)

(0.009)

(0.102)

(0.101)

(0.009)

(0.008)

Observations

103

103

103

103

101

101

101

101

R-squared

0.955

0.955

0.955

0.955

0.960

0.960

0.959

0.959

Financial Openness GDP Growth Inflation Competition Profitability Size CAP LIQ

Source: Research Data Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

36

EJAE 2021  18 (1)  15 - 38

ABOYADANA. G.  MONETARY POLICY AND BANK RISK-TAKING IN SUB-SAHARA AFRICA

Table 10b: Countries with exchange controls (1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Variables

LLP

LLP

LLP

LLP

LLP

LLP

LLP

LLP

Cost Efficiency

0.067*

0.067*

0.056

0.056

0.068*

0.068*

0.060

0.060

(0.035)

(0.035)

(0.035)

(0.035)

(0.036)

(0.036)

(0.037)

(0.036)

-14.063**

-15.987*** 1.200

1.011

-12.215

-14.909*

8.832

6.621

(6.333)

(5.058)

(1.773)

(10.249)

(8.367)

(8.359)

(5.834)

-2.442

-2.319

-5.706

-5.592

(5.539)

(5.499)

(5.513)

(5.448)

Monetary Policy

(4.685)

Monetary Policyt-1 MPRCAP MPRLIQ Financial Openness

-0.253

-0.021

-0.308

-0.239

(0.495)

(0.513)

(0.516)

(0.535)

0.323***

0.317***

0.350***

0.348***

(0.089)

(0.089)

(0.101)

(0.101)

-45.749*** -48.138*** -27.124*** -27.358*** -46.444*** -49.591*** -24.937*** -27.503*** (9.249)

(8.116)

(7.507)

(5.905)

(10.068)

(8.407)

(7.573)

(6.014)

-0.126

-0.122

-0.122

-0.123

-0.124

-0.092

-0.092

(0.119)

(0.118)

(0.124)

(0.123)

(0.129)

(0.130)

(0.131)

(0.131)

0.232

0.231

0.218

0.218

0.206

0.206

0.154

0.154

(0.219)

(0.218)

(0.226)

(0.225)

(0.246)

(0.246)

(0.251)

(0.251)

GDP Growth -0.127 Inflation

-0.105***

-0.109***

-0.109***

-0.103***

-0.103***

-0.109***

-0.108***

(0.021)

(0.021)

(0.022)

(0.022)

(0.021)

(0.021)

(0.022)

(0.022)

0.462

0.465

0.368

0.368

0.386

0.393

0.267

0.273

(0.440)

(0.438)

(0.459)

(0.459)

(0.415)

(0.414)

(0.454)

(0.453)

-1.763***

-1.772***

-1.702***

-1.703***

-1.766***

-1.776***

-1.693***

-1.701***

(0.342)

(0.339)

(0.341)

(0.341)

(0.346)

(0.343)

(0.348)

(0.348)

0.349

-0.009

0.015

-0.015

0.430

-0.005

0.329

-0.009

(0.654)

(0.106)

(0.676)

(0.108)

(0.682)

(0.107)

(0.707)

(0.109)

-0.515***

-0.506***

-0.041*

-0.041*

-0.552***

-0.548***

-0.046*

-0.045*

(0.139)

(0.139)

(0.023)

(0.023)

(0.152)

(0.153)

(0.025)

(0.025)

Observations 210

210

210

210

205

205

205

205

R-squared

0.610

0.592

0.592

0.613

0.612

0.597

0.597

Competition -0.104*** Profitability Size CAP LIQ

0.610

Source: Research Data Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

37

EJAE 2021  18(1)  15 - 38

ABOYADANA. G.  MONETARY POLICY AND BANK RISK-TAKING IN SUB-SAHARA AFRICA

MONETARNA POLITIKA I PREUZIMANJE RIZIKA OD BANAKA U PODSAHARSKOJ AFRICI Rezime: Pokazalo se da monetarna politika utiče na ponašanje preuzimanja rizika banaka u Evropi i Severnoj Americi. Međutim, Afrika dobija malo pažnje po ovom pitanju. Ovo istraživanje doprinosi monetarnoj politici i literaturi o preuzimanju rizika za podsaharsku Afriku ispitivanjem panela komercijalnih banaka od 2001. do 2015. godine za različite vrste rizika. Otkrivamo da monetarna politika značajno utiče na preuzimanje rizika banaka kako statistički, tako i ekonomski, ali se efekat razlikuje u zavisnosti od vrste rizika. Veličina banke i profitabilnost su važni za određivanje koliko efektivna monetarna politika utiče na preuzimanje rizika. Efekti su jači za zemlje bez kontrole deviznog kursa. Kada je reč o politici, monetarne vlasti koje nameravaju da vode ekspanzivnu monetarnu politiku moraju da poprave odgovor banaka na preuzimanje rizika.

38

Ključne reči: preuzimanje rizika, finansijska kriza, monetarna politika, Afrika.

Klasifikacija jela: E52, E58, G01, G18, G21, G28, G31

EJAE 2021, 18(1): 39 - 54 ISSN 2406-2588 UDK: 338.48-12:330.567.22(497.11)"2006/2019" 331.218.3(497.11)"2006/2019" DOI: 10.5937/EJAE18-29551 Original paper/Originalni naučni rad

ECONOMETRIC EXAMINATION OF THE IMPACT OF INCOME ON HOUSEHOLD EXPENDITURES FOR PACKAGE HOLIDAYS IN SERBIA Hasan Hanić, Milica Bugarčić*, Radojko Lukić Faculty of banking, Belgrade banking academy, Union University, Belgrade, Serbia

Abstract: The subject of this paper is the econometric examination of the impact of income on expenditures, i.e. on the demand for package holidays of households in Serbia. The aim of this paper is to quantify the impact of income on household expenditures for package holidays in the country and abroad on the basis of alternative functional forms of Engel curves and elasticities derived from them. The starting research hypothesis is that with the increase in household income, the share of expenditures for tourist arrangements in total household expenditures in Serbia remains approximately unchanged. As sources of data, Household Budget Surveys in Serbia were used, which were conducted every year, starting from 2006 (until 2019). Based on the different functional forms of Engel curves, the parameters of the impact of income on expenditures for package arrangements were estimated, and then income elasticities were estimated. In addition to the impact of income, the impact of qualitative characteristics of households and especially household heads on expenditures for tourist arrangements was examined. With the help of appropriate statistical tests, the basic research hypothesis was proven and the influence of socio-economic and demographic characteristics of households on the demand for package holidays was quantified.

Article info: Received: November 24, 2020 Correction: December 28, 2020 Accepted: February 10, 2021

Keywords: income elasticities, package holidays, Engel curve, parameter significance, dummy variable.

INTRODUCTION In 1857, Ernest Engel formulated the regularities in the behavior of consumption depending on the changes in the total earnings (i.e. income) of households depending on expenditures, and finally formulated them in 1895 in the form of precise conclusions, which were later called Engel's laws. In brief, these laws can be formulated as follows: the percentage of food expenditures changes in inverse proportion to changes in income; the share of expenditure on clothing, footwear and housing is *E-mail: milica.bugarcic@bba.edu.rs

39

EJAE 2021  18(1)  39 - 54

HINIĆ. H., BUGARČIĆ. M., LUKIĆ. R.  ECONOMETRIC EXAMINATION OF THE IMPACT OF INCOME ON HOUSEHOLD EXPENDITURES FOR PACKAGE HOLIDAYS IN SERBIA

approximately constant for all income levels; with the increase in income, the share of expenditures for health, cultural needs, entertainment, etc. also grows. These laws have been empirically tested by numerous researchers in different countries and at different periods in the same countries to examine their spatial and temporal universality. The starting research hypothesis in this paper is that expenditures for tourist arrangements grow in proportion to the increase in household income, i.e. in the terminology of elasticity, the starting hypothesis is as follows: income elasticities of household expenditures in Serbia for tourist arrangements are equal to one.

LITERATURE REVIEW Depending on whether the demand for a particular product or service is viewed in isolation from the formation of demand for other products and services or as an element of a consumer demand system in which a simultaneous demand formation process is observed that takes into account the substitution and complementarity of products and services consumption, empirical econometric research uses two fundamentally different methodological approaches to demand modeling. The first approach, known in the literature as partial modeling, is reflected in modeling the demand for a particular product or service by a regression equation, while the second approach, known in the literature as a complex approach to the study of demand, consists of modeling demand using complete systems of regression equations.

Different functional forms were used in the analysis of demand using a complex approach: WorkingLeser model, Linear Expenditure System, Additive-logarithmic model, Transcendental logarithmic model, Rotterdam model with absolute prices, Rotterdam model with relative prices, Almost Ideal Demand System, Quadratic Almost Ideal Demand System (Hanić & Bugarčić, 2020). Some of these models allow the qualitative characteristics of consumers to be included in the regression equations of demand in addition to the variables of consumer income and prices of individual products and services, with households being most often observed as consumer units. In this respect, the most popular functional forms of complete regression equation systems are Almost Ideal Demand System (AIDS) and Quadratic Almost Ideal Demand System (QUAIDS).

Using the QUAIDS model of complete Regression Equation Systems, Coenen and Van Eekeren (2003) estimated income and price elasticities of tourism spending based on a survey of a relatively large number of Swedish households between January 1990 and October 1996. The original sample included 370604 observations, with each observation referring to one tourist trip that the surveyed household had in the observed period. In the procedure of econometric evaluation of model parameters, only those observations were taken into account that reflected the fact that tourist trips were realized by all household members in the observed period. The results obtained by these researchers reveal that household expenditures for tourist trips in relation to income are elastic (elasticity greater than one), or normally elastic (elasticity approximately equal to one) where the numerical value of income elasticities varies depending on socio-economic and demographic characteristics of the household. They showed, for example, that income elasticity is affected by the age structure of the household - for households with children aged 7 to 12, income elasticity is 1.2, while households with children aged between 19 and 20 have an income elasticity of 1.0.

The AIDS model of the complete systems of regression equations of demand was used, for example,

by Sadeghi, Jamshidi, and Tayyebi (2005) based on a sample of 504 households which had a vacation in Hamedan Province in 2003 to estimate income and price elasticities of demand for tourist travels in the country.

40

EJAE 2021  18(1)  39 - 54

HINIĆ. H., BUGARČIĆ. M., LUKIĆ. R.  ECONOMETRIC EXAMINATION OF THE IMPACT OF INCOME ON HOUSEHOLD EXPENDITURES FOR PACKAGE HOLIDAYS IN SERBIA

The results of empirical research of these authors have shown that expenditures for tourist trips in the country in relation to household income are approximately equal to one, i.e. that the demand for this type of tourist services is normally elastic in relation to income, and that the price elasticity of demand is less than one, that is, that the demand for domestic tourist services is inelastic in relation to the price of those services.

Although the complex approach to demand modeling has its theoretical foundation, the partial approach to demand modeling is more often used in empirical research, primarily due to a simpler way of estimating demand parameters and a more reliable statistical-analytical basis.

Alegre and Pou (2004) studied the impact of income, socio-economic and demographic characteristics of households on expenditure on tourism services in a sample of 18038 Spanish households surveyed between 1985 and 1996. Among other things, they came to the conclusion that expenditures for tourist trips are statistically significantly influenced by household income, level of education of the household head, free time of the household head, household size and number of employed household members.

Wang and Davidson (2010) made a valuable contribution in which they presented the results of empirical research on expenditure on travel arrangements presented in 27 separate studies. Almost all studies have shown that income and prices are key explanatory variables that can be used to explain variations in household expenditures for travel arrangements. A large number of studies have shown that the socio-economic and demographic characteristics of households are variables that can also explain a significant part of the total variations in household expenditures on tourism arrangements.

The influence of the following variables is especially distinguished: gender and age structure of the household, household size, level of education of the household head.

Song, Eugenio-Martin and Campos-Soria (2011) conducted a survey on a sample of 16,153 households from 15 European countries surveyed in 1997. They found that the income elasticity of expenditures for tourist trips in the country is 0.377, i.e. that expenditures are inelastic in relation to household income and that expenditures for tourist trips abroad are relatively elastic (the coefficient of income elasticity is 1,188). They also found that patterns of household behavior in this area are determined by socio-economic and demographic variables such as follows: income level, household size, age structure of the household, marital status and occupation of the household head.

Bernini and Cracolici (2015) investigated the impact of household demographic characteristics on the level of tourist travel expenditure of Italian households. Based on the sample of households surveyed during the ten-year period from 1997 to 2007, it was determined that there is a strong influence of household demographic characteristics on household expenditures for tourist trips, but also that there are significant differences in income elasticity between households whose members are of different ages.

Based on micro data obtained from surveys of consumption of Norwegian households conducted

by Statistics Norway in the period from 2009 to 2012 Thrane (2016) proved the assumption that expenditures for tourist trips are strongly influenced by income, gender and age structure households.

Subanti et al. (2018) analyzed tourism expenditures in Central Java Province in Indonesia depending on household income per member, but also socio-economic and demographic factors and regional household affiliation and found that age and gender structure, household income, level of education of the household head and household members, the number of household members, the degree of urbanity of the settlement in which the household has its residence are variables that statistically significantly affect expenditures for tourist travel.

41

EJAE 2021  18(1)  39 - 54

HINIĆ. H., BUGARČIĆ. M., LUKIĆ. R.  ECONOMETRIC EXAMINATION OF THE IMPACT OF INCOME ON HOUSEHOLD EXPENDITURES FOR PACKAGE HOLIDAYS IN SERBIA

Based on data from surveys on the consumption of Kenyan households conducted in 2015-2016 Odeny (2019) examined the impact of income and a number of socio-economic and demographic characteristics of households on expenditures for domestic tourism. The results of the empirical econometric analysis showed that the age of household members, gender of household head, marital status, employment and household size are variables that can be used to explain variations in Kenyan households' travel expenditures.

Finally, Haq, Ullah, and Sajjad (2019) analyzed the impact of socioeconomic factors on the share of recreation, culture, and travel expenditures in total expenditures of Pakistani households. They found that there is a significant positive impact of income, the level of economic development of the region to which the household belongs and the level of education and age of the household head, as well as the fact that household size negatively affects the level of expenditure on recreation and tourism. Therefore, expenditures for recreation and tourism of bigger households are lower per household member.

Theoretical, methodological and empirical aspects of the demand for tourist services were dealt with, among others, by the following authors: Cortés-Jiménez and Blake (2011), Schiff and Becken (2011), Nelson, Dickey and Smith (2011), Wu, Li and Song (2012), Baležentis et al. (2012), Konovalova and Vidishcheva (2013), Fuleky, Zhao and Bonham (2014), Gatt and Falzon (2014), Untong et al. (2014, 2015), Peng et al. (2015), Martins, Gan and Ferreira-Lopes (2017), Dogru, Mariyono (2017), Sirakaya-Turk and Crouch (2017), Khoshnevis Yazdi and Khanalizadeh (2017), Falk and Lin (2018), Fredman and Wikström (2018) and Kumar and Kumar (2020).

METHODOLOGY Partial modeling of consumer demand is based, as it has already been stipulated above, on the formulation of a demand model in the form of a regression equation (1) where q denotes the dependent variable - demand for the observed product or service, m income of the bearer of demand (individual or household) explanatory variables - prices of products and services on which income is spent, systematic component of demand, i.e. that part q which is formed under the influence of factors , that systematically affect demand, h denotes the dependence of demand on factors that systematically affect demand, while denotes a random component of the model, i.e. that component of demand that is the result of the action of many factors that randomly act in the direction of increasing or decreasing demand, where the influence of any such factor is not significant.

The first step in empirical - econometric partial modeling of demand is the choice of functional form h which best represents the dependence of demand on the factor that determines it or, econometrically speaking, the choice of a particular specification of the demand model, taking into account the complexity of parameter estimation methods, their statistical properties, such as: linearity, impartiality, consistency, existentialism, efficiency, etc. (Hanic, 2018).

If it is assumed that prices are constant, as is the case when data on demand or consumption or household expenditure are collected using surveys conducted at short intervals (month, quarter or year), then the previous demand model can be written in the following form (2) 42

EJAE 2021  18(1)  39 - 54

HINIĆ. H., BUGARČIĆ. M., LUKIĆ. R.  ECONOMETRIC EXAMINATION OF THE IMPACT OF INCOME ON HOUSEHOLD EXPENDITURES FOR PACKAGE HOLIDAYS IN SERBIA

The demand model written in the previous form, which is known in the literature as Engel's model, allows to quantify the "pure" impact of changes in income on changes in expenditures on the basis of cross-sectional data obtained through household consumption surveys (Hanić and Bugarčić, 2020). The graph of the function , where the dependent variable denotes the natural or value-expressed consumption of a given product or service, and m the household income, is called the Engel curve.

In the analysis of expenditures for package holidays of households in Serbia, the following functional forms are used: Linear model:

(3)

Quadratic model

(4)

Log-log model:

(5)

Lin-log model:

(6)

Log-lin model:

(7)

Reciprocal model:

(8)

Log-reciprocal model:

(9)

For each analytical form of Engel curves, an implied mathematical expression is given that expresses the dependence of income elasticity on income level. As it can be seen from this presentation, only the log-log Engel consumption model, in which both variables are expressed in logarithmic form, implies a constant income elasticity. In all other cases, income elasticity is different at different levels of household income.

To calculate the numerical value of income elasticity that approximates the elasticity of expenditure in relation to income for the entire income interval, variable X is approximated by the corresponding mean value - arithmetic, geometric or harmonic mean, depending on the econometric specification of the model In addition to the analysis of income as the main explanatory variable, the impact of other socioeconomic and demographic characteristics of the household (household size, regional household affiliation and level of education of the household head) was quantified, which are assumed to affect household expenditures for package holidays, apart from income or in interaction with income The variables such as Household size, Regional affiliation and Level of education of the household head are included in the model using dummy variables, as independent variables and as variables that interact with income, measuring changes in the initial income level and the slope of the demand curve: (10)

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EJAE 2021  18(1)  39 - 54

HINIĆ. H., BUGARČIĆ. M., LUKIĆ. R.  ECONOMETRIC EXAMINATION OF THE IMPACT OF INCOME ON HOUSEHOLD EXPENDITURES FOR PACKAGE HOLIDAYS IN SERBIA

Since each of these qualitative (categorical) variables has four modalities, three artificial variables are defined for each of them: 1. Level of education of the head of household

2. Regional affiliation of the household

3. Size of household

The survey has been conducted on micro data collected by the Statistical Office of the Republic of Serbia through surveys on household consumption in Serbia according to the methodology of the EU Agency for Statistics (EUROSTAT), the International Labor Organization and the United Nations, every year since 2006.

The databases which were used contain annual data on all household expenditures, starting from consumer goods and services intended to meet daily needs, and for which households record expenditures daily in the consumption diary, to expenditures for semi-durable and durable goods and services. In addition to expenditure data, consumption surveys also contain sources of household income as well as the socio-economic characteristics of each surveyed household of each of its members.

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HINIĆ. H., BUGARČIĆ. M., LUKIĆ. R.  ECONOMETRIC EXAMINATION OF THE IMPACT OF INCOME ON HOUSEHOLD EXPENDITURES FOR PACKAGE HOLIDAYS IN SERBIA

Products and services of personal consumption according to the Classification of Products and Services of Personal Consumption by Purpose (COICOP) are classified into 12 groups: 1) Food and non-alcoholic beverages; 2) Alcoholic beverages and tobacco; 3) Clothing and footwear; 4) Housing, water, electricity, gas and other fuels; 5) Equipment for the apartment and current maintenance; 6) Health; 7) Transport; 8) Communications; 9) Recreation and culture; 10) Education; 11) Restaurants and hotels; 12) Other personal items and other services. Expenditures for package holidays belong to the group "Recreation and Culture".

Table 1: Structure of household samples in Serbia, 2006-2019 Year

Number of households surveyed

Number of households assessed

Fraction in %

Average size of households

2006

4560

2536714

0.18

3.15

2007

4608

2536714

0.18

2.95

2008

4621

2536714

0.18

3.04

2009

4592

2536714

0.18

3.00

2010

4585

2536714

0.18

2.94

2011

4592

2536714

0.18

2.89

2012

4546

2536714

0.18

2.88

2013

4517

2465799

0.18

2.88

2014

6071

2466316

0.25

2.86

2015

6531

2466316

0.26

2.81

2016

6457

2466316

0.26

2.74

2017

6403

2466316

0.26

2.70

2018

6296

2466316

0.26

2.66

2019

6354

2466316

0.26

2.68

Average

5259

2501478

0.21

2.85

Source: Statistical Office of the Republic of Serbia

In the research, 14 partial databases were used, and each database includes data collected by household surveys for a certain year of the observed period (2006-2019). The average annual number of surveyed households in Serbia is 5,259, with the number of respondents increasing every year, as shown by Table 1: the survey sample in 2006 and in 2019 covered 4,560, 6,354 households respectively.

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HINIĆ. H., BUGARČIĆ. M., LUKIĆ. R.  ECONOMETRIC EXAMINATION OF THE IMPACT OF INCOME ON HOUSEHOLD EXPENDITURES FOR PACKAGE HOLIDAYS IN SERBIA

RESULTS AND DISCUSSION

The number of households that have expenditures for package holidays is relatively small. In 2006, only 165 out of a total of 4560 households or 3.6% had expenditures for package holidays, while in 2019 the share of households with expenditures on package holidays increased about 2.5 times, so that it amounted to 8.7%.

Figure 1: Structure of households that had expenditures for package holidays in 2019 according to the amount of income, household size, regional affiliation and level of education of the household head

The average monthly expenditure of households in 2019 amounted to 1371 dinars, or it was eight times higher than in 2006, when it amounted to 170 dinars. The average amount of expenditures of households that spent on package holidays was 15696 dinars in 2019, compared to 2006 when it amounted to 4709 dinars.

Table 2 indicates that only 18 households in Serbia in 2019 whose income ranged from 6894 to 37856 dinars spent on package holidays. There are 86 such households from the income range from 37856 to 72265 dinars, while in the income interval from 72265 to 759320 dinars there are 3776 households that spent on package holidays. In 2019, the share of expenditures for package holidays in all income groups was approximately 12%, which implies a unit elasticity of expenditures in relation to household income.

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HINIĆ. H., BUGARČIĆ. M., LUKIĆ. R.  ECONOMETRIC EXAMINATION OF THE IMPACT OF INCOME ON HOUSEHOLD EXPENDITURES FOR PACKAGE HOLIDAYS IN SERBIA

Table 2: Household expenditures for package holidays in 2006 and 2019 according to income All households Year Income in RSD

Number of households

Average income

4560

33339

1984 - 285301 2006

2019

Households which had expenditures ExpeExpeHousenditures in nditures in hold percentage RSD RSD 170

3.6

4709

Average income

Share of expenditures

63513

0.07

1984 - 21164

1519

13689

4

0.26

1575

18947

0.08

21164 - 36889

1519

28775

27

1.38

1948

29699

0.07

36889 - 285301

1521

57536

480

9.2

5213

69858

0.07

6894 - 759320

6354

66049

1371

8.7

15696

129611

0.12

6894 – 37856

2118

25640

18

0.5

3826

32352

0.12

37856 – 72265

2102

53780

293

4.1

7163

59461

0.12

72265 – 759320

2134

118240

3776

21.5

17554

144873

0.12

Table 3 shows the estimated values of the parameters of the used functional forms of Engel curves, together with the relevant statistical indicators p-value, value of F-statistics, coefficient of determination and the value of Akaike information criterion, for 2006 and 2019.

Based on the value of the coefficient of determination, it can be concluded that in 2006 the double logarithmic form with constant elasticity best described the dependence of expenditures for package holidays on household income, while in 2019 the quadratic functional form represented the best example of the studied dependence. However, if the value of the Akaike information criterion is taken into account, then the conclusion can be drawn that Engel's curve with constant elasticity best approximates the dependence of household expenditures for tourist arrangements on the level of household income in 2006 and 2019.

Table 3: Estimated parameters of Engel curves in 2006 and 2019 Model

Year

2006

2019

p-value

0.000

0.000

Equation Linear

F

421.198

309.956

Prob > F

0.0000

0.0000

R

0.084

0.127

AIC

78892.704

45055.320

2

Equation Log-log

p-value

0.000

0.000

F

41.493

69.999

Prob > F

0.0000

0.0000

R

0.198

0.131

AIC

498.540

1319.004

2

Equation Log-lin

p-value

0.000

0.000

F

42.003

61.917

Prob > F

0.0000

0.0000

R

0.200

0.117

AIC

498.129

1326.097

2

47

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HINIĆ. H., BUGARČIĆ. M., LUKIĆ. R.  ECONOMETRIC EXAMINATION OF THE IMPACT OF INCOME ON HOUSEHOLD EXPENDITURES FOR PACKAGE HOLIDAYS IN SERBIA

Model

Year

2006

2019

p-value

0.000

0.000

F

156.461

338.873

Prob > F

0.0000

0.0000

Equation Lin-log

R

0.033

0.137

AIC

79141.836

45030.198

p-value

0.000

0.000

F

390.871

182.972

Prob > F

0.0000

0.0000

R2

0.146

0.146

AIC

78575.776

45008.798

p-value

0.000

0.000

F

38.134

297.417

Prob > F

0.0000

0.0000

2

Equation Quadratic

Equation Recipro-cal

R

0.008

0.122

AIC

79257.747

45066.306

p-value

0.000

0.000

F

30.600

69.620

Prob > F

0.0000

0.0000

R

0.153

0.130

AIC

507.573

1319.334

2

Equation Logreciprocal

2

Based on the estimated parameters of the selected econometric specifications, the income elasticities of expenditures for package holidays in Serbia for each specification and for each year of the analyzed period (2006-2019) were evaluated.

Based on the numerical value of income elasticity estimated using the double-logarithmic form, as the dominant best representative of the studied dependence, it can be seen that in the observed period there was a decrease in elasticity from 1.13 to 1.02. Except for the years 2014 and 2017, when the elasticities estimated on the basis of the log-log functional form were 0.77 and 0.93, respectively, the income elasticities of expenditures in all other years of the analyzed period were higher than one, which is in line with the basic postulate of economy of tourism and tourist demand theory. However, based on Wald’s F-test, it was found that the yield elasticity did not differ significantly from one, thus confirming the basic research hypothesis. When interpreting this result, it should be taken into consideration that when calculating the parameters of log-log functions, households where no expenditures for package holidays were registered are excluded from the analysis.

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HINIĆ. H., BUGARČIĆ. M., LUKIĆ. R.  ECONOMETRIC EXAMINATION OF THE IMPACT OF INCOME ON HOUSEHOLD EXPENDITURES FOR PACKAGE HOLIDAYS IN SERBIA

Such conclusions were reached by Sadeghi, Jamshidi, and Tayyebi (2005) who examined the impact of income on Iranian household expenditures on travel arrangements, and Song, Eugenio-Martin, and Campos-Soria (2011) who conducted a survey on a sample of households from 15 European countries.

If, on the other hand, the results obtained using linear, linear-logarithmic and logarithmic-inverse functions are taken into account, then it can be reliably concluded that the elasticity of expenditures for package holiday arrangements is significantly higher than one. In all years of the observed period, the income elasticities estimated on the basis of the linear and lin-log functions are significantly higher than one. The average values of elasticity in the observed period estimated on the basis of these functions are 2.9 and 2.2, respectively. Income elasticities estimated on the basis of the logarithmic-inverse function in all years of the observed period except in 2014, are higher than one. Their average value is 1.3. This result is in line with the postulate of the tourist demand theory, according to which the share of expenditures for package holiday arrangements in the income of consumers increases with the increase of income.

Table 4: Income elasticities of expenditures for package holidays in Serbia, estimated on the basis of different forms of Engel curves, 2006-2019.

Year

Linear

Log-Log

Log-Lin

Lin-Log

Quadratic

Reciprocal

Logreciprocal

2006

3.52

1.13

0.00

2.29

-2.15

0.55

1.48

2007

2.24

1.03

0.00

1.79

1.68

0.46

1.25

2008

3.14

1.26

0.00

2.14

-2.39

0.66

1.58

2009

2.46

1.02

0.00

1.99

1.80

0.69

1.11

2010

2.85

1.18

0.00

2.30

1.28

0.82

1.85

2011

3.54

1.14

0.00

2.60

-2.25

0.90

1.24

2012

3.08

1.12

0.00

2.42

3.08

0.69

1.27

2013

3.72

1.04

0.00

2.49

-4.29

0.73

1.11

2014

2.93

0.77

0.00

2.16

0.00

0.76

0.85

2015

3.15

1.24

0.00

2.19

-1.58

0.69

1.27

2016

3.23

1.15

0.00

2.18

-1.50

0.64

1.06

2017

2.47

0.93

0.00

2.03

1.94

0.75

1.11

2018

2.25

1.08

0.00

2.06

2.51

0.78

1.29

2019

2.51

1.02

0.00

2.14

2.51

0.82

1.32

In addition to the impact of income on expenditures for package arrangements, the impact of socioeconomic and demographic characteristics of households (regional affiliation and household size, level of education of the household head) was also examined. Based on the value of the F-test used to test the significance of differences in the average level of household expenditure belonging to different regions, it was found that there is a statistically significant difference (F (3, 6346) = 18.99, Prob> F = 0.0000) in average household expenditure for package arrangements of households belonging to Belgrade, Vojvodina, Šumadija and Western Serbia and Southern and Eastern Serbia. Using the F-test, the hypothesis was tested on the significance of the impact of the level of education of the household head (with modalities: no education, elementary, secondary and higher education) on expenditures for package holidays in Serbia in 2019 and found that the level of education of the household head significantly determines differences in household expenditures for package holidays (F (3.6346) = 14.85; Prob> F = 0.0000).

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HINIĆ. H., BUGARČIĆ. M., LUKIĆ. R.  ECONOMETRIC EXAMINATION OF THE IMPACT OF INCOME ON HOUSEHOLD EXPENDITURES FOR PACKAGE HOLIDAYS IN SERBIA

In the same way, the assumption that household size represents a statistically significant source of variation in household expenditures for package holidays has been proven - in 2019 where F (3, 346) = 13.21; and Prob> F = 0.0000.

Table 5. provides basic indicators of the impact of socio-economic and demographic characteristics of households on expenditures for package holidays in Serbia in 2019.

In accordance with the result of the F-test, which showed that the level of education of the household head affects expenditures on package holidays, significant differences in income elasticities were obtained as indicators of the intensity of the impact of income on expenditures for different households with different levels of education. Thus, for example, the income elasticity in 2019 of households in which the head has higher education was 0.66, while for households with the lowest level of education of the household head, the income elasticity was 1.50. Therefore it can be concluded that in the first category of households, package holidays have the status of a necessary good, and in the categories with a lower level of education, they enjoy the status of a luxury good. This result corresponds to the results obtained by researchers Alegre and Pou (2004) in Spain, Subanti et al. (2018) in Central Java Province in Indonesia, and Haq, Ullah and Sajjad (2019) in Pakistan.

Table 5: Characteristic values of the studied socio-economic and demographic segments of households in Serbia, 2019.

Variable

Level of education of the head of the household Number of household members

Region

Income

Modality

Share of households which have expenditures on package holidays

Average expenditures

Average i ncome

Income elasticities (Log-log)

No education

0.02

375

35503

1.50

Elementary

0.04

573

49938

1.11

Secondary

0.09

1341

68734

1.10

Higher

0.16

2927

90017

0.66

1

0.03

458

32244

1.32

2-4

0.09

1480

70819

1.08

5-7

0.19

2817

114039

1.64

>7

0.15

2166

122404

2.53

Belgrade

0.11

1802

75660

1.05

Vojvodina

0.09

1183

62193

0.75

Šumadija and Western Serbia

0.07

1267

65479

1.00

Southern and Eastern Serbia

0.08

1361

63402

1.26

6894 – 37856*

0.00

3826

32352

-0.04

37856 – 72265*

0.04

7163

59461

-0.46

72265 – 759320

0.22

17554

144873

1.09

*Note: insufficient number of observations for reliable conclusions

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HINIĆ. H., BUGARČIĆ. M., LUKIĆ. R.  ECONOMETRIC EXAMINATION OF THE IMPACT OF INCOME ON HOUSEHOLD EXPENDITURES FOR PACKAGE HOLIDAYS IN SERBIA

The estimated income elasticities show that the size and regional affiliation of the household are variables that also determine to a statistically significant extent the variations in expenditures for package holidays depending on the amount of income. In households with a small number of members, income elasticities are significantly lower compared to the income elasticities of households with a larger number of members. Significance of the household size variable was also confirmed in studies by Alegre and Pou (2004) in Spain, Song, Eugenio-Martin and Campos-Soria (2011) in 15 European countries, Subanti et al. (2018) in Central Java Province in Indonesia, Odeny (2019) in Kenya, Haq, Ullah and Sajjad (2019) in Pakistan. The significance of the impact of regional household affiliation on the dependence of expenditures on package holidays on income has been confirmed by other researchers, including Subanti et al. (2018) in Central Java Province in Indonesia, and Haq, Ullah and Sajjad (2019) in Pakistan.

CONCLUSIONS Summarizing the results, the following conclusion can be drawn: first, in 2006 only 3.6% of surveyed households had expenditures for package holidays, while in 2019 the share of households with expenditures for package holidays in the total number of households reached 8.7%, which confirms the well-known view that the level of economic development of the country and the real disposable income, in addition to free time, are key determinants of tourist demand; second, the log-log Engel curve is a functional form that best represents the dependence of expenditures for tourist arrangements on the level of household income in Serbia; third, income levels, size and regional affiliation of the household, and the level of education of the household head are explanatory variables that explain the variations in household expenditures in Serbia to a statistically significant extent; fourth, the average value of income elasticity of expenditures for tourist arrangements based on log-log Engel's functions forthe analyzed period (2006-2019) is not significantly higher than one. When interpreting this result, the limitation should be taken into consideration that when calculating the log-log parameters of the function, parameters for the households which in certain years of the analyzed period did not have expenditures for tourist arrangements were omitted; fifth, on the basis of linear and linear-logarithmic function, whose parameters are estimated on the basis of overall observations, i.e. based on complete samples, the resultsobtained show that income elasticities are significantly higher than one, which is in line with Engel's third law, which postulates that with the increase in income, the share of expenditures for package holiday arrangements increases; sixth, over time income elasticities tend to fall which corresponds to the fact that with the increase in income, which occurs over time, income elasticity decreases.

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HINIĆ. H., BUGARČIĆ. M., LUKIĆ. R.  ECONOMETRIC EXAMINATION OF THE IMPACT OF INCOME ON HOUSEHOLD EXPENDITURES FOR PACKAGE HOLIDAYS IN SERBIA

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Tourism Economics, 20(3), 595-610. https://doi.org/10.5367/te.2013.0280 Wang, Y. & Davidson, M. C. (2010). A review of micro-analyses of tourist expenditure. Current issues in Tourism, 13(6), 507-524. https://doi.org/10.1080/13683500903406359 Wu, D. C., Li, G., & Song, H. (2012). Economic analysis of tourism consumption dynamics: A time-varying parameter demand system approach. Annals of Tourism Research, 39(2), 667-685. https://doi.org/10.1016/j.annals.2011.09.003 Zavod za statistiku Republike Srbije. (2009). Anketa o potrošnji domaćinstava Zavoda za statistiku Republike Srbije. Beograd: Zavod za statistiku Republike Srbije.

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EKONOMETRIJSKO ISPITIVANJE UTICAJA PRIHODA NA RASHODE DOMAĆINSTVA Rezime: Predmet ovog rada je ekonometrijsko ispitivanje uticaja prihoda na rashode, odnosno, na potražnju za paket aranžmanima domaćinstava u Srbiji. Cilj ovog rada je da kvantifikuje uticaj prihoda na troškove domaćinstva za paket aranžmane u zemlji i inostranstvu na osnovu alternativnih funkcionalnih oblika Engelovih krivih i iz njih izvedenih elasticiteta. Početna hipoteza istraživanja je da sa povećanjem prihoda domaćinstva udeo izdataka za turističke aranžmane u ukupnim izdacima domaćinstava u Srbiji ostaje približno nepromenjen. Kao izvori podataka korišćene su Аnkete o potrošnji domaćinstava u Srbiji, koje su se sprovodile svake godine, počev od 2006. (do 2019). Na osnovu različitih funkcionalnih oblika Engelovih krivih, procenjeni su parametri uticaja prihoda na troškove za paket aranžmane, a zatim su procenjene i elastičnosti dohotka. Pored uticaja prihoda, ispitivan je i uticaj kvalitativnih karakteristika domaćinstava, a posebno nosilaca domaćinstava na izdatke za turističke aranžmane. Uz pomoć odgovarajućih statističkih testova dokazana je osnovna hipoteza istraživanja i kvantifikovan je uticaj socioekonomskih i demografskih karakteristika domaćinstava na potražnju za paket aranžmanima.

54

Ključne reči: elastičnost prihoda, paket aranžman, Engelova kriva, značaj parametra, veštačka varijabla.

EJAE 2021, 18(1): 55 - 72 ISSN 2406-2588 UDK: 005.332.8:336.761 339.13:796.332.093.1.062 DOI: 10.5937/EJAE18-28284 Original paper/Originalni naučni rad

MARKET REACTIONS TO FOOTBALL MATCH RESULTS: THE EFFECT OF VENUES AND COMPETITION TYPES Krismon Dwi Apredianto, Apriani Dorkas Rambu Atahau*, Andrian Dolfriandra Huruta Department of Management, Satya Wacana Christian University, Republic of Indonesia

Abstract: This study seeks to investigate the stock market reaction to football match results in different venues and competition types, especially for three major football clubs: Manchester United, Juventus FC, and Borussia Dortmund. We use a parametric paired sample t-test. The findings show that investors take not only match results but also venues and competition types into consideration when making investment decisions. This study indicates the stock markets of football clubs are semi-strong efficient markets. Different market reaction to match results for each football club studied implies the importance of understanding the specific characteristics of those clubs in making investment decisions.

The findings suggest the football club investors need to consider their clubs’ competitive performance in different venues and competition types when making portfolio investment decisions. This study complemented the previous assumption on the importance of venues, and competition types match results in making investment decisions.

Article info: Received: March 11, 2020 Correction: October 12, 2020 Accepted: December 31, 2020

Keywords: abnormal returns, trading volume, efficient market theory, football clubs, stock markets.

INTRODUCTION Sports have already become an industry, not only a game. According to Collignon, Sultan, and Santander (2011), today's sports industry is worth between €350 billion and €450 billion worldwide, while football contributes the most with the annual income of €28 billion. According to Worldatlas (2018), the third most popular sport in the world is field hockey, with 2 billion fans in European, Asian, and African countries. Cricket ranks second with 2.5 billion fans, and it is popular in former British colonies such as India, Sri Lanka, and Bangladesh (Singh, 2013). The most popular sport in the world is football, with 4 billion fans. Although it has been popular in the European and American continents (Love & Walker, 2013), it has been gaining increasing popularity in Asia and Africa.

Football is the most popular professional sport in Europe. It is also highly covered by the media and involves a vast amount of financial resources (Demir & Danis, 2011; Rowe & Gilmour, 2010). *E-mail: apriani.rambu@uksw.edu

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APREDIANTO. K. D., ATAHAU. A. D. R., HURUTA. A. D.  MARKET REACTIONS TO FOOTBALL MATCH RESULTS: THE EFFECT OF VENUES AND COMPETITION TYPES

Clubs receive their revenues mainly from competitions in which they participate. However, they also receive a substantial amount of revenues from merchandising, sponsors, and media contracts (Demir & Rigoni, 2017). More intense domestic and international competitions motivate clubs to generate more revenues (Demir & Danis, 2011). Also, the rapid growth of the football industry encourages football clubs to be more profit-oriented and even publicly listed on stock exchanges (Berkowitz & Depken, 2017).

According to Demir & Rigoni (2017) and Gallagher & O’Sullivan (2011) financial markets enable clubs to transfer their risks to other parties by offering commensurate returns. Also, financial markets facilitate separation of ownership and control to create optimal allocations of scarce resources. Further, Sun and Wu (2015) argue that such termination enables clubs to generate funds to improve their financial positions.

In 1983, Tottenham Hotspurs was the first football club that listed on a stock exchange. Afterward, many other clubs followed. Accordingly, favorable match results likely lead to better financial performance because competitive success attracts media attention and sponsors (Sakınç, 2014; Bell, Brooks, Matthews, & Sutcliffe, 2012; Rowe & Gilmour, 2010). Similarly, a new information regarding the sporting success is able to help predicting subsequent changes in the stock price of Borussia Dortmund. Before 2000, there were 22 English Premiere League clubs listed on the European stock exchanges. However, at present, based on Footballbenchmark (2017), there are only 22 clubs worldwide that listed on European stock exchanges.

Since the change of the century, 14 English football clubs have delisted because of significant share price drops (Wilson, Plumley, & Ramchandani, 2013). Different from other listed firms that publish their financial information at least quarterly, football clubs arguably inform their operational performance through their weekly match results (Basu & Sondhi, 2015).

In 2011, UEFA (the governing body of European football) issued a regulation called "Financial Fair Play" to ensure that football clubs’ financial conditions were healthy. The rule regulates the clubs’maximum amount of expenses relative to their revenues. In this respect, costs refer to all charges, excluding stadium investments, young player development, and women’s football development. Specifically, clubs are now allowed to have deficits (the difference between expenses and revenues) at most $ 5 million per three seasons. According to UEFA (2015), using the nominal value of maximum deficit and not a percentage, and excluding long-term investments (such as investments for the stadium and young player development) in expense calculation offer opportunities for small clubs to grow. UEFA expects that the regulation facilitates clubs to have more stable financial conditions and to make better long-term investment projections.

Football clubs do not only deal with financial matters but also have to play football matches every week. Hence, this paper seeks to investigate the effect of football clubs’ competitive sports performance (win or lose) on their stock market returns and trading volumes. Football clubs arguably participate in at least two competitions in a season, namely the domestic and international leagues, while the international or continental associations are more prestigious than the domestic ones. Consequently, fans will considerably expect that their football clubs win the international leagues rather than the domestic ones. According to UEFA (2015), continental leagues (UEFA Champions and Europa Leagues) offer clubs the revenues up to $15.25 million only for their participation.

Football clubs will potentially receive more revenues depending on their performance in these competitions (Dimic, Neudl, Orlov, & Äijö, 2018). Match venues can also affect fans’ expectations for their clubs’ performance. Fans whose clubs play in their home countries expect more to have their clubs win the matches (Basu & Sondhi, 2015).

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EJAE 2021  18 (1)  55 - 72

APREDIANTO. K. D., ATAHAU. A. D. R., HURUTA. A. D.  MARKET REACTIONS TO FOOTBALL MATCH RESULTS: THE EFFECT OF VENUES AND COMPETITION TYPES

Demir and Danis (2011) analyze this issue before but with different results. In particular, Demir and Rigoni (2017) find that stock markets react more reliably to the effects of European competitions than to that of the domestic ones. However, Demir and Danis (2011) find that stock markets do not react differently to the results of the European and local (Turkey) leagues. Based on these inconsistent results, this paper aims to add empirical evidence to this issue and confirm previous findings. Additionally, we use football clubs from different countries considering that Wilson, Plumley, & Ramchandani (2013), Demir and Danis (2011), Demirhan (2013), Fung, Demir, Lau, & Chan (2015) only focus on single countries (England and Turkey, consecutively) and suggest future studies to use football clubs from other European country leagues.

Also, this paper analyzes match results’ effects on football clubs not only with abnormal returns but also with trading volume. The inclusion of trading volume in this research since this issue is relatively understudied. To our best knowledge, only Benkraiem, Pryor, & Louhichi (2011) use trading volume in their analysis whereas according to O’Hara, Saar, & Zhong (2018), the trading volume provides information that cannot be provided by stock prices because it informs the process of stock returns.

Hence, investors are motivated to use trading volume information to indicate market behaviors (O’Hara et al., 2018). As such, trading volume and abnormal returns have informational roles for investors’ decisions (Benkraiem et al., 2011).

This paper aims to ask the following research questions. First, do stock markets react differently (in terms of abnormal returns and trading volume) to match results (win vs. loss) in the domestic and international leagues? Second, do stock markets react differently (in terms of abnormal returns and trading volume) to match results (win vs. loss) in home and away venues? It is expected that this study would potentially inform investors on making investment decisions in football clubs to respond to match results, since football clubs arguably have two unique investor types - the economic rationales investors (Duque & Ferreira, 2010).

The findings provide evidence on the importance of venues and competition types’ match results in making investment decisions. The same match results (wins or losses) from different competition or venue types will prompt a different investors’ reaction (Gerlach, 2011; Bernile & Lyandres, 2011; Dimic et al., 2018). Market reactions to winning at home matches are higher than winning at away events.

However, markets react more to losing at away competitions than at home matches. In line with Fan, Lei, and Zhang (2018), we also find that investors are more sensitive to losses than winning the game.

LITERATURE REVIEW Market Efficiency An efficient market fully reflects all the information relevant to investors’ decisions. This concept argues that investors always consider publicly available information in their investment decisions. In an efficient market, future income or cash flows reflect firms’ stock values (Demir & Danis, 2011). In competitive capital markets, numerous interactions between buyers and sellers lead to price equilibriums.

The presence of new information will create new equilibrium prices (Gitman & Zutter, 2014). Consequently, investors likely react to new information (Floros, 2014).

In this regard, football clubs offer a unique setting because investors arguably receive new information weekly from match results (Galloppo & Boido, 2020). The weekly match result information potentially has both direct and indirect effects on clubs’ future income (Floros, 2014).

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Hence, one can observe the stock market effects of football match results after the matches end.

Accordingly, clubs’ current share prices have already reflected the expected match results (Bell et al., 2012). Unexpected results will affect share prices because the investors did not anticipate the results beforehand (Demir & Danis, 2011). As such, stock prices contain three elements in an efficient market, namely all historical information, information on announced future events, and information on predicted future events (Floros, 2014). Also, there are three forms of market efficiency, namely the weak form, the semi-strong form, and the stable form of an efficient market (Marwala & Hurwitz, 2017).

Trading Volume Activity Trading volume activity also indicates capital markets’ reactions to information (Benkraiem et al., 2011) because it reflects the dynamic relations between informed and uninformed investors in arranging their trading strategies (Abuzayed, 2013). Technical analysis usually relies on trading volume in its strategy.

High trading volumes indicate better market conditions (Dimic et al., 2018; Fung et al., 2015). Trading volume is a variation of the event study (Fung et al., 2015). Because the measure of trading volume activity divides the number of shares traded with the number of outstanding shares, investors can base their investment decisions by assuming that shares with high trading volume activities potentially offer high returns.

Abnormal Returns Abnormal returns are excesses of actual returns over expected returns (Hartono, 2013). Positive (negative) differences indicate that real gains are higher (lower) than due returns. According to Hartono (2013), abnormal returns are affected by events. In our paper, we use football match results as the events to investigate.

Market Reaction to Football Match Results Markets likely react to football clubs’ match results (Castellani, Pattitoni, & Patuelli, 2013). Further, Demir and Danis (2011) observe the “loss effect” that refers to asymmetric market reaction to losses relative to wins. They also document that the market reacts differently to gains or losses from international and domestic matches. They further argue that clubs will generate more revenues when they win international competitive games than domestic ones, because the international leagues are more reputable than the domestic ones. Accordingly, higher league reputation will lead to higher fans’ expectations and the market reacts differently to the results of international and domestic matches (Basu & Sondhi, 2015; Cottingham, 2012). In other words, fans expect more that their clubs win international matches than domestic ones. Consequently, the results of international matches will affect the market more than those of domestic ones (Basu & Sondhi, 2015). Based on these arguments, we propose the first hypotheses: H1a: Market reactions to wins in the international leagues are higher than those to wins in the domestic matches.

H1b: Market reactions to losses in the international leagues are higher than those to losses in the domestic matches.

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This study also predicts that markets react differently to wins or losses in away and home matches.

Psychologically, the home-court advantage in professional sports matches motivates home teams more to win than away teams (Škrinjarić & Barišić, 2019). According to Jamieson (2010), more than 50% of home teams win professional sports games. The cheers from home fans highly motivate home teams to play better (Castellani et al., 2013). Home teams are also arguably more familiar with their venues so that they can perform better and more effectively. Also, teams often experience fatigue because they have to travel from their home countries. Fans expect that their football club will win when the teams play at home more than at away matches (Basu & Sondhi, 2015). In this regard, Demir and Danis (2011), Castellani, Pattitoni, & Patuelli (2013), Godinho and Cerqueira (2018) found the market reacts significantly to unexpected results. Based on the arguments, we propose the second hypotheses: H2a: Market reaction to wins at home is higher than market reactions to wins at away matches.

H2b: Market reaction to losses at home is higher than market reactions to losses at away matches.

METHODOLOGY This study uses stock market-related and match-results data. We generate match results data from the transfermarket.com database that collects match-related data (dates, venues, and results). Meanwhile, this study gathers stock market-related data from finance.yahoo.com, investing.com, and Bloomberg. com that includes daily trading volume, outstanding shares, and closing prices.

Further, the type of this research is a case study for the matches of the 2018/2019 season on three football clubs (Yin, 2014). We selected clubs for the analysis based on the following criteria: First, the club must have played in the European leagues in the 2018/2019 season. Second, the club has won titles in the domestic and European leagues. Third, the club must have come from the four most significant leagues based on the total market value as suggested by transfermarket.com. This criteria is used to filter the club with the same fans expectation to win the games or competition.

Table 1: Sampling Technique No.

Criteria

Total

1

played in the european competitions in the 2018/2019 season

14

2

has won the domestic league and continental competitions

5

3

from 4 biggest leagues based on total market value

3

final sample

3

Table 2: Research Sample No.

Stock code

Club name

1

nyse.manu

Manchester United

2

juve.mi

Juventus

3

bvb.de

Borussia Dortmund

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The data is analyzed using event study because event study analyzes the information content of events that leads to market reaction. As our study aims to investigate the market reaction on the event of football match results in different venues and competition types, we decided to apply event study as it best suits the study objective. These methods were also applied by Demir & Danis (2011) who studied / whose study examined issues regarding football match event.

We use one-day windows for the event period, i.e., one day (t-1) before and one day after the matches (t+1). Using a one-day event period is appropriate to mitigate the potential risk of overlapping events that confound the results (Berument & Ceylan, 2012; Castellani et al., 2013; Scholtens & Peenstra, 2009).

One-day event periods are considered representative for investigating market reactions to particular circumstances and thus is common in the literature (Demir & Rigoni, 2017). This research does not include the trading volume and abnormal returns on the match days (t0) since most matches are played on the weekends, and stock markets are closed during the weekend (Bell et al., 2012). In particular, we only focus on win or lose match results since Demir and Danis (2011) find that there’s no significant effect of the draw results on market reaction.

We calculate abnormal returns and trading volume from the first trading day before and after the match days because most of the domestic matches are played on weekends (Saturday or Sunday).

However, for the international events that are usually performed midweek (Wednesday or Thursday), the observation will start from the first trading day before and after the match days. This study focuses on the matches of the 2018/2019 season.

Further, abnormal returns are the differences between actual and expected returns. Following Hartono (2013), we calculate abnormal returns with the following formula.

(1) Where: = abnormal return i stock at t = actual return i stock at t = expected return i stock at t Actual returns are returns that exist at a certain point in time and the differences between current prices and previous prices (t-1). Actual returns computed by using the following formula.

(2) Where: = daily return i stock at t = daily price i stock at t = daily price i stock at t-1 Meanwhile, expected returns are returns expected by investors. We use the market-adjusted model to determine expected returns. The model uses market return index to predict stocks’ returns that allows ones not to form estimation models. The following is the formula for calculating expected returns.

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(3) Where: = expected return i stock at t = stock market return at t The three clubs listed on three different stock markets, namely the New York Stock Exchange (NYSE), Borsa Italiana (FTSE.MIB), and Xetra Dax Stock exchange (DAX). The following is the formula for calculating stock market returns.

(4) Where: = stock market return = stock price index at t = stock price index at t-1 We use the closing share prices to calculate actual returns on the day before the match days (t-1), and after the match days (t+1). We then calculate average abnormal returns to indicate stocks’ reactions with the following formula.

(5) Where: = average abnormal return = abnormal return = numbers of observed days This study calculates trading volume by dividing the number of shares traded and outstanding shares.

(6) Where: = average abnormal return = abnormal return = numbers of observed days

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RESULTS AND DISCUSSION

This paper analyzes the matches of three clubs, namely Manchester United, Borussia Dortmund, and Juventus, in the domestic and international (European) competitive events in the 2018/2019 season.

Tables 3-5 below display the descriptive statistics of those three clubs.

Table 3: Descriptive Statistics: Manchester United (NYSE.MANU) Venue/ league type

Match result win

Home lose win Away lose win Domestic lose win International lose

Variable

N

Minimum

Maximum

Mean

Std. Deviation

abr

22

-.0259764

.0308186

.001971424

.0124856939

tva

22

.0004600

.0098400

.001853182

.0021456831

abr

12

-2.0103128

-1.9807113

-1.996471695

.0103805225

tva

12

.0006100

.0043800

.001443333

.0010365970

abr

24

-.0351680

.0435164

.002084506

.0167626217

tva

24

.0004500

.0059000

.002029583

.0018315579

abr

18

-.0237183

.0540031

.001538821

.0189249688

tva

18

.0004200

.0040100

.001347778

.0010452501

abr

38

-.0259764

.0435164

.001895368

.0131007662

tva

38

.0004600

.0098400

.002086316

.0021206990

abr

20

-2.0103128

.0389498

-.599380132

.9392407876

tva

20

.0004200

.0036500

.001238500

.0007744354

abr

8

-.0351680

.0308186

.002671936

.0219813504

tva

8

.0004500

.0022200

.001275000

.0007013864

abr

10

-2.0038466

.0540031

-1.194235891

1.0343914430

tva

10

.0004900

.0043800

.001681000

.0014046862

Manchester United played 17 home matches with win and loss results. The table informs that the minimum value of Manchester United’s abnormal returns for home matches is -2.0103, the one after they lost to Tottenham Hotspurs. Meanwhile, the maximum value of Manchester United’s abnormal returns for home matches is 0.0308 just before they beat Young Boys. The mean value (standard deviation) of Manchester United’s abnormal returns when they win their home matches is 0.0019 (0.0124). The table also informs that Manchester United played 21 away matches with win and loss results. The minimum value of Manchester United’s trading activities for away matches is 0.0004 before they lose to Manchester City. Meanwhile, the maximum value of Manchester United’s trading activities for away matches is 0.0059 before they win against Cardiff. The mean value (standard deviation) of Manchester United’s trading activities when they win their away matches is 0.0020 (0.0018).

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Table 4: Descriptive Statistics: Juventus (JUVE.MI) Venue/ league type

Match result win

Home lose win Away lose win Domestic lose win International lose

Mean

Std. Deviation

.1684419

.017617766

.0494727762

.0737788

.015725944

.0123265885

-.1800351

.0385325

-.034048940

.0998283673

.0080084

.0664487

.031458465

.0249151872

-.0862160

.0499920

-.005823667

.0305375425

Variable

N

Minimum

abr

36

-.0872857

tva

36

.0057358

abr

4

tva

4

abr

30

Maximum

tva

30

.0022582

.1488069

.016493842

.0259816149

abr

12

-1.0117988

.0279917

-.099379827

.2895587609

tva

12

.0043525

.0437904

.016408087

.0129515418

abr

54

-.0862160

.1613694

.009141981

.0376148846

tva

54

.0022582

.0327188

.013806402

.0076291381

abr

8

-1.0117988

.0279917

-.126200298

.3583474342

tva

8

.0043525

.0344787

.016739791

.0106246730

abr

10

-.0872857

.1684419

-.004057001

.0708757783

tva

10

.0057358

.1488069

.029514420

.0466973742

abr

8

-.1800351

.0385325

-.039893912

.0720150433

tva

8

.0050997

.0664487

.023601571

.0220664136

In the 2018/2019 season, Juventus played 20 home matches with loss and win results. Table 4 suggests that Juventus has 40 data (20 before the match days and 20 after the match days). The minimum value of Juventus’ abnormal returns for home matches is -0.1800 after they lose to Ajax. Meanwhile, the maximum value of Juventus’ abnormal returns for home matches is 0.1684, after they have won against Atletico Madrid. The mean value (standard deviation) of Juventus’ abnormal returns when they win their home matches is 0.1761 (0.4947).

Table 5: Descriptive Statistics: Borussia Dortmund (BVB.DE) Venue/ league type

Match result win

Home lose win Away lose win Domestic lose win International lose

Variable

N

abr

32

Minimum -.0241014

Maximum .0445967

Mean

Std. Deviation

.005527546

.0151612343

tva

32

.0016452

.0075516

.003998602

.0016016328

abr

4

-.1011456

.0042191

-.027022906

.0496520305

tva

4

.0015730

.0135591

.005740424

.0053880869

abr

22

-.0266678

.0438501

.003891087

.0161342348

tva

22

.0016088

.0095233

.004742095

.0021812539

abr

10

-.1005046

.0165489

-.018793002

.0351244556

tva

10

.0014832

.0135591

.005077828

.0034130872

abr

46

-.0241014

.0445967

.004594769

.0146218545

tva

46

.0016088

.0095233

.004104381

.0018903220

abr

8

-.1005046

.0087675

-.018454982

.0338654777

tva

8

.0014832

.0135591

.004160483

.0039067988

abr

8

-.0266678

.0438501

.006390753

.0206478334

tva

8

.0036338

.0075516

.005434981

.0014030099

abr

6

-.1011456

.0165489

-.024730298

.0458910166

tva

6

.0034613

.0135591

.006742686

.0035633925

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Borussia Dortmund played 18 home matches in the 2018/2019 season with win and loss results. The minimum value of Borussia Dortmund’s trading volume for home matches is 0.0015, the day before they lost to Schalke 04. Meanwhile, the maximum value of Borussia Dortmund’s trading volume for home matches is 0.0135, the day before they lose to Tottenham Hotspurs. The mean value (standard deviation) of Borussia Dortmund’s trading volume when they lose their home matches is 0.0057 (0.0053).

Market Reactions to Wins (Losses) in the International Leagues and Domestic Matches To test the first hypothesis, we run the parametric paired sample t-test or non-parametric Wilcoxon signed-rank test. The test investigates significant abnormal returns and trading volume differences between the day before and after the clubs win or lose in domestic or international matches. The significance values that are greater than 0.05 imply that the hypothesis is not empirically supported, and there are no significant differences in abnormal returns and trading volume.

Table 6: Market Reactions in the international leagues and domestic matches: Manchester United (NYSE.MANU) League type Domestic International Domestic International Domestic International Domestic International

Match result

Variable

win abr lose win tva lose

Mean difference

Sig. (2-tailed)

.0047242213

.310

-.0009463645

.964

.0102421076

.101

.0147886562

.347

.0004705263

.388

-.0001050000

.388

-.0004090000

.110

.0007460000

.416

Table 6 shows domestic and international matches are greater than 0.05. Hence, there are no significant differences in abnormal returns in domestic and continental leagues. For the win results, the abnormal returns difference of domestic matches is higher than that of international matches.

Conversely, for the loss results, the abnormal returns difference of international leagues is higher than that of domestic leagues.

For trading volume, the results of the paired sample t-test and Wilcoxon signed-rank tests inform no significant differences in trading volume before and after both domestic and international leagues because all the significance values are higher than 0.05. For the win results, the trading volume difference of domestic matches is higher than that of international matches. Meanwhile, for the loss results, the trade difference of international leagues is higher than that of domestic leagues.

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Table 7: Market Reactions in the international leagues and domestic matches: Juventus FC (JUVE.MI) League type Domestic International Domestic International Domestic International Domestic International

Match result

Variable

win abr lose win tva lose

Mean difference

Sig. (2-tailed)

-.0109675731

.237

.0203939264

.674

-.2422619130

.407

.0676581275

.308

.0001965726

.896

-.0410215060

.187

.0077133268

.146

-.0144704055

.416

Table 7 demonstrates all the significance values of Juventus’ abnormal return and trading volume differences for both domestic and international leagues are higher than 0.05, implying that there are no significant differences in abnormal returns and trading volume before and after the domestic and continental matches. Wins at international (domestic) matches exhibit positive (negative) abnormal return differences. For the loss results, the abnormal return difference of domestic matches is higher than that of international matches, although the effect is negative. Meanwhile, the win results in international leagues exhibit higher trading volume differences (although negative) than winning in domestic leagues.

For the loss results, the international matches have a higher trading volume difference than the domestic matches.

Table 8: Market Reactions in the international leagues and domestic matches: Borrusia Dortmund (BVB.DE) League type Domestic International Domestic International Domestic International Domestic International

Match result

Variable

win abr lose win tva lose

Mean difference

Sig. (2-tailed)

-.0085624750

.010

.0204960493

.322

.0172822020

.531

-.0023169823

.969

-.0002427007

.524

.0010744383

.111

-.0033446438

.338

.0027284523

.478

The paired sample t-test and Wilcoxon signed-rank test for Borussia Dortmund find that almost all significance values are higher than 0.05, suggesting no trading volume and abnormal return differences before and after matchdays, both for domestic and international leagues. Based on Table 8, only winning at domestic matches exhibits significant abnormal return difference (significance value smaller than 0.05). Hence, abnormal returns are significantly different between the day before and after the matches.

Further, the results reveal that winning at international matches has a more positive effect on Borussia Dortmund’s abnormal returns than at domestic matches. For the loss results, losing at domestic matches has a more positive effect than at international matches. In terms of trading volume, winning at international matches exhibits a higher trading volume difference than at domestic matches. Also, this paper documents that the mean difference in the trading volume of losing at domestic matches is higher (negatively) than losing at international matches.

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The findings demonstrate that market reactions to wins at international matches are greater than at domestic matches. However, the markets react differently (positively or negatively) to match results for each club. Thus, hypothesis 1a is supported. Meanwhile, hypothesis 1b is not supported because the markets react more to losing at domestic matches than at international ones. Also, for all clubs, the markets react negatively to losing at away matches. Our findings are thus consistent with Scholtens and Peenstra (2009).

Market Reactions to Wins (Loss) in the Home and Away Venues To test the second hypothesis, we run the parametric paired sample t-test or non-parametric Wilcoxon signed-rank test. The test investigates significant abnormal return or trading volume differences between the day before and after losing or winning matches at home or away venues. The significance values that are greater than 0.05 imply that the hypothesis is not empirically supported, and there are no significant abnormal returns and trading volume differences.

Table 9: Market Reactions in the home and domestic away venues: Manchester United (NYSE.MANU) Type Home Away Home Away Home Away Home Away

Condition

Variable

win abr lose win tva lose

Mean difference

Sig. (2-tailed)

.0045630669

.490

.0029817508

.690

.0044054683

.557

.0166590608

.074

.0002200462

.722

.0005091667

.526

-.0000033333

.996

-.0000377778

.924

Based on Table 9, all of the significance values for Manchester United’s abnormal return differences are greater than 0.05, implying that there are no significant abnormal return differences the day before and after the home and away matches. The mean difference in abnormal returns for winning at home matches (0.0045) is higher than at away matches (0.0029). Also, the abnormal return means the difference for losing at away matches (0.0044) is higher than at home (0.0166).

For the trading volume variable, all significance values are greater than 0.05, thus also indicating no significant trading volume differences between the day before and after home and away matches.

The mean difference for winning at away matches (0.0002) is higher than at home (0.0005). However, for the loss results, the biggest decrease in trading volume (-0.00003) occurs when Manchester United lost at away matches. When losing at home matches, the trading volume of this club also decreases (-0.00000) but much lower than at away matches.

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Table 10: Market Reactions in the home and domestic away venues: Juventus FC (JUVE.MI) Type Home

Condition

Variable

win

Away

abr

Home

lose

Away Home

win

Away

tva

Home

lose

Away

Mean difference

Sig. (2-tailed)

-.0186858044

.213

.0096164000

.448

.0734047075

.666

-.1408707595

.451

-0.00403125

.528

-0.00789665

.820

-.0115401260

.783

-.0006580105

.934

Table 10 shows all significance values of abnormal returns of Juventus’ home and away matches are higher than 0.05, implying no significant abnormal return differences the day before and after home and away matches. The market reacts positively to wins at away matches because the mean difference in abnormal returns is 0.0096. However, the market reacts negatively to wins at home matches because the abnormal return means the difference is negative (-0.0186). In sum, the market reacts most to wins at home matches, as indicated by the highest mean difference in abnormal returns.

Further, losing at away matches has a greater effect on abnormal return change than at home matches.

The mean difference in abnormal returns for losing at away matches is negative (-0.1408), suggesting that the market reacts negatively to Juventus’ loss at away matches. For trading volume, Table 13 illustrates that all significance values of mean differences at home and away matches are greater than 0.05. Hence, there are no significant differences in trading volume the day before and after home and away matches.

Table 11: Market Reactions in home and domestic away venues: Borrusia Dortmund (BVB.DE) Type Home Away Home Away Home Away Home Away

Condition

Variable

win abr lose win tva lose

Mean difference

Sig. (2-tailed)

-.0052558599

.165

-.0028053610

.328

-.0509004780

.522

.0327957634

.183

-.0001924588

.696

.0001631797

.728

.0036512720

.599

-.0024991524

.370

Juventus’ trading volume is more affected by wins at away matches than at home matches. The mean difference in the trading volume of winning at away matches is -0.0078 while at home matches it is -0.0041. Thus, the market reacts more negatively to wins at away matches. When Juventus lost, the market reacted more negatively to losses at home matches. The mean difference trading volume of losing at home matches is -0.0115 and at away matches is -0.0006.

Table 11 shows all of the significance values of abnormal returns of Borussia Dortmund’s home and away matches are greater than 0.05, suggesting no significant abnormal return differences the day before and after the home and away matches.

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The market reacts more negatively to wins at home matches than at away matches. The abnormal return difference for wins at home matches is negative (-0.0052). Although the abnormal return difference for losses at away matches is also negative (-0.0028), the magnitude is smaller.

Our results also indicate that the market reacts differently to losses at home and away matches.

When Borussia Dortmund loses at home, the mean difference in abnormal returns is negative (-0.0509).

However, when Borussia Dortmund loses at away matches, the mean difference in abnormal returns is positive (0.0327).

For trading volume, the results of our paired sample t-test and Wilcoxon signed-rank test indicate no significant trading volume differences the day before and after home and away matches as indicated by all of the significance values of trading volume that are greater than 0.05. Winning at home negatively affects trading volume for Borussia Dortmund, as suggested by the negative trading volume mean difference (-0.0001). However, the market reacts positively to wins at away matches as the positive trading volume mean difference (0.0001). When Borussia Dortmund losses, the market reacts positively if the losses are at home matches (positive trading volume mean difference), but negatively to losses at away matches.

Abnormal returns and trading volume change at greater magnitudes when these three clubs win at home matches. The results support Benkraiem et al. (2011) who find abnormal returns for homematch wins but not away-match ones. However, the winning results of those clubs at home matches have different effects on abnormal returns and trading volume (Fung et al., 2015). Specifically, markets react positively to Manchester United and Juventus’ wins at home matches, but negatively to Borussia Dortmund’s. For losses, the market reacts stronger to losses at away matches (both in terms of abnormal returns and trading volume). However, market reactions to these three clubs’ losses differ. Hence, these findings are different from Benkraiem et al. (2011) who observe that the market reacts more to losses at home matches. We also find that for these clubs, markets react stronger to wins and losses at away matches likely because of heterogeneous features of investors’ opinions (Demir & Rigoni, 2017). The trading volume also changes because investors may revise their portfolio investment decisions following the match results (Fung et al., 2015).

Markets also react more to wins at international matches than at domestic ones (Fung et al., 2015).

Our results are thus different from Demir and Danis's (2011) who document that winning at international matches does not significantly affect clubs’ abnormal returns. In particular, we find that market reacts negatively to Manchester United’s wins at international matches and positively to Borussia Dortmund’s wins at international matches (both in terms of trading volume and abnormal returns). Meanwhile, Juventus’ wins at international matches positively (positively) affect its abnormal returns (trading volume).

This paper also finds that the market reacts differently to the losses of these three clubs. Specifically, the market reacts positively to Manchester United’s losses at domestic matches at a greater magnitude and negatively to Borussia Dortmund’s losses at domestic matches (both in terms of abnormal returns and trading volume). However, the market reacts stronger to Juventus’ losses at domestic matches (in terms of abnormal returns) and international matches (in terms of trading volume). Hence, these findings complement Scholtens and Peenstra (2009) who cannot find market reactions to domestic matches.

This study shows market reactions to winning at home matches are higher than winning at away matches. Hence, hypothesis 2a is empirically supported. Our results support Benkraiem et al. (2009).

However, hypothesis 2b is not supported because markets react more to losing at away matches than at home matches. Investors are more sensitive to losses. Thus, this research is in line with Fan, Lei, and Zhang (2018).

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Because losses potentially predict future losses better than wins (Berkowitz & Depken, 2017), losses are a more credible signal than wins. Thus, our results support Bell et al. (2012). This paper also demonstrates that clubs’ current share prices already incorporate expected results.

CONCLUSION Sports have been a big and lucrative industry, with football as the biggest sport in terms of growth.

Many football clubs opt to sell their shares publicly to support their financial needs. However, unlike shares of firms from other industries, football clubs’ share prices also likely depend on other information such as match results. Hence, this paper investigates 113 matches of three large football clubs in the 2018/2019 season by focusing on the effects of the match results on abnormal returns and trading volume.

The findings demonstrate that market reactions to wins at international matches are greater than at domestic matches. However, the markets react differently (positively or negatively) to match results for each club. Meanwhile, the markets react more to losing at domestic matches than at international ones.

This study also shows that market reactions to winning at home matches are higher than winning at away matches. However, markets react more to losing at away matches than at home matches. We also find that investors are more sensitive to losses. As such, this research is in line with Fan, Lei, and Zhang (2018). Because losses potentially predict future losses better than wins (Berkowitz & Depken, 2017), losses are a more credible signal than wins. This paper also demonstrates that clubs’ current share prices already incorporate expected results. Therefore, our results support Bell et al. (2012). The findings also indicate that the stock markets for these three football clubs are a semi-strong efficient market in which share prices reflect all historical information and public information such as match results.

We have observed that investors take match results into account when making investment decisions.

Hence, the clubs need to play excellently to avoid negative shocks in market reactions (Payne, Tresl, & Friesen, 2018). Also, we support Dimic et al. (2018) who find that competition and venue types affect market reactions. These results confirm that football club investors consider their clubs’ competitive performance when making portfolio investment decisions.

There are several limitations of this paper. This study does not classify the matches based on the importance of the match opponents and the expected results of the matches. Also, this research does not use any control variable in the analysis, such as betting odds. Lastly, using only three clubs in the analysis limits the generalizability of our results.

Accordingly, we suggest future studies to include control variables to gain a better understanding of the research issue. Next, we leave to future studies to incorporate rivalries between clubs and the importance of the matches into the analysis. Further, using more football clubs as the research sample will also increase the generalizability of the research results, as will using other country’s football club that has already sold its shares publicly.

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TRŽIŠNE REAKCIJE NA REZULTATE FUDBALSKE UTAKMICE: UTICAJ MESTA DOGAĐAJA I VRSTE TAKMIČENJA Rezime: Cilj ovog istraživanja je da se istraži reakcija berze na rezultate fudbalskih utakmica na različitim mestima i vrstama takmičenja, posebno za tri glavna fudbalska kluba: Manchester United, Juventus FC i Borussia Dortmund. Koristimo parametarski upareni uzorak t-testa. Rezultati pokazuju da investitori uzimaju u obzir ne samo rezultate utakmica već i mesta održavanja i vrste takmičenja prilikom donošenja investicionih odluka. Ova studija pokazuje da su berze fudbalskih klubova polujaka efikasna tržišta. Različita tržišna reakcija na rezultate mečeva za svaki proučeni fudbalski klub podrazumeva važnost razumevanja specifičnih karakteristika tih klubova u donošenju investicionih odluka.

Rezultati takođe sugerišu da investitori fudbalskih klubova moraju da uzmu u obzir konkurentske performanse svojih klubova na različitim mestima i vrstama takmičenja prilikom donošenja odluka o portfolio investicijama. Ovo istraživanje je dopunilo prethodnu pretpostavku o važnosti mesta održavanja, a tipovi takmičenja podudaraju se sa rezultatima u donošenju investicionih odluka.

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Ključne reči: abnormalni povrat, obim trgovanja, efikasna teorija tržišta, fudbalski klubovi, berze.

EJAE 2021, 18(1): 73 - 88 ISSN 2406-2588 UDK: 005.332:316.7 005.936.43 DOI: 10.5937/EJAE18-26618 Original paper/Originalni naučni rad

THE IMPACT OF THE METACOGNITIVE AND BEHAVIORAL FACTORS OF CULTURAL INTELLIGENCE ON FOREIGN BRAND ACCEPTANCE Stefan Zdravković*, Jelena Peković University of Kragujevac, Faculty of Economics, Kragujevac, Serbia

Abstract: The aim of the paper is to show the influence of cultural intelligence in the acceptance of foreign brands and how these insights can help multinational companies in shaping and presenting their brands so that they are better accepted in different cultural environments. Cultural intelligence consists of four factors, two of which have been analyzed for the purposes of this paper - the metacognitive and behavioral factors.

The metacognitive cultural dimensions are usefulfor the individual uses to obtain certain knowledge about a culture. Behavioral cultural intelligence represents the ability to adapt an individual to verbal and non-verbal behaviors according to the situation in culturally diverse environments. The results of the empirical research proved a statistically significant positive impact of the behavioral element of cultural intelligence on the acceptance of foreign brands by consumers, while the influence of the metacognitive factor on the acceptance of foreign brands is not statistically significant, which is different from most of the empirical studies conducted so far.

Article info: Received: May 18, 2020 Correction: July 28, 2020 Accepted: October 23, 2020

Keywords: cultural intelligence, metacognitive factor, behavioral factor, foreign brand.

JEL Classification: D91, Z13

INTRODUCTION Globalization is an integral part of human life today. The rise of cultural diversity has led to changes in people's lives and in work organizations, making the world not so simple and superficial. The term cultural intelligence is most often mentioned in the context of multinational companies. Research shows that the high level of development of cultural intelligence is a very important factor within multinational companies, where employees come from different cultural backgrounds, which increases innovation and creativity and contributes to achieving better financial performance of the company. On the other hand, within schools and faculties, pupils and students develop their level of cultural intelligence through certain programs such as exchange, travel and learning a foreign language, which ultimately increases their level of education (Hu et al., 2017). *E-mail: szdravkovic034@yahoo.com

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Important part of management research is understanding how people from different backgrounds can work through their differences. The rapid globalization has led to a growing group of employees facing challenges and obstacles in their everyday work. Organizationsare required to appear with a new type of manager who is capable of understanding divergence at the surface level, in order to become outstanding in this day economy. Managers who have a high degree of cultural intelligence can positively influence the company’s financial performance with their skills and ideas (Alshaibani & Bakir, 2017). Although we understand the benefits of having this type of manager, little research has been conducted on this topic until the early 21st century. Ang & Van Dyne (2015) state that cultural intelligence will have an increasing influence in multinational companies and that there is a need for more research in this field in the literature.

The notion of cultural intelligence is particularly salient in multinational companies in two aspects.

The first aspect is represented by employees coming from culturally diverse backgrounds. Globalization has imposed the need for employees to have the skills and experience to work in culturally diverse environments. The second aspect represents the operations of companies in the international market, where they present their brands to consumers from different cultures (Aldhaheri, 2017). Consumers are influenced by many of the psychological, social, and personal factors that shape their perception of the brand. A brand can be defined as a label, logo, character, or any other function that identifies and distinguishes one seller from other sellers. Consumers often choose to buy global brands such as CocaCola (Iversen & Hem, 2011). An important segment that influences consumer perception of a brand is cultural intelligence. Consumers who exhibit high levels of consumer ethnocentrism and low levels of cultural intelligence (which allows for the ability and flexibility to embrace cultural diversity) are averse to everything that comes from other cultures, and therefore to brands (Han & Guo, 2018; Pratono & Arli, 2020). On the other hand, a high degree of consumer cultural intelligence has a conclusive result on the acceptance of foreign brands (Ang & Van Dyne, 2015; Frias-Jamilena et al., 2018b).

Ang & Van Dyne (2015) state that cultural intelligence is composed of four factors: metacognitive, cognitive, motivational and behavioral. Metacognitive factor is a term referring to a higher thinking ability to obtain knowledge and control over a cognitive factor, which represents knowledge of cultural dissimilarities. Motivational factor is ability to encourage efforts to function in international circumstances, and behavioral factor represents ability to flexibly behave in international interactions (Ang et al., 2007).

In other words, cognitive and meta-cognitive factors are of a cognitive nature, while motivational and behavioral factors are of an emotional nature.

Considering the fact that the consumer behavior process is an extremely complex marketing concept and is influenced by many factors, the subject of the research is to test the effect of metacognitive and behavioral cultural intelligence on the acceptance of foreign brands by consumers. The primarily goal of the paper is to regulate whether the metacognitive and behavioral factors of cultural intelligence exert a statistically significant influence on consumers' decision to accept foreign brands. In addition, the paper examines whether the behavioral factor of cultural intelligence is more pronounced in men than in women.

Based on multiple regression analysis and T test for two independent samples, a decision will be made to confirm or reject the research hypotheses.

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LITERATURE REVIEW

Concept of Cultural Intelligence Cultural intelligence can be explained as the capacity to function in a diverse environment since cultural interconnection is a widespread distinction of people's lives (Thomas & Van Dyne, 2018).

Also, cultural intelligence emphasizes a person's prospective to be successful in an extensive range of cross-cultural circumstances and increases an individual’s ability to adapt quickly to new cultural settings (Afsar et al., 2019).

Cultural intelligence is often identified with social and emotional intelligence and is indistinguishable to social and emotional intelligence because it is a form of mutual experiences. Social intelligence implies the ability to understand other people. On the other hand, a person who owns high level of emotional intelligence comprehends what is the meaning to be a human and at the same time what makes us so different (Moon, 2010). Cultural intelligence is linked to social and emotional intelligence, but it studies and explores individuals, groups of people, and relationships between them from a different perspective. Thus, a person with a high degree of emotional or social intelligence occasionally possess a high of so-called cultural intelligence (Moon, 2010).

Cultural intelligence is alike and different from common cognitive capacity. Overall cognitive capacity is a key predictor of performance in all jobs and settings. The difference with common cognitive capacity is that this capacity involves only cognitive intellect and excludes motivation, behavior, and metacognitive. Empirical evidence indicates that cultural intelligence is associated with given production in an inter relational context than with contingent ability. Thus, cultural intelligence is gradually increasing by predicting production in intercultural circumstances above general cognitive capacity (Ang et al., 2007).

The influence of cultural intelligence manifests itself in various aspects. Helen Kurpis & Hunter (2017) point out that is necessary for the management of a company to consist of people who come from different cultures. Management conceived in such a way is highly creative, innovative and can give interesting business ideas that can improve the company’s position in the market, and in financial terms. Also, the study points out that is necessary to increase the level of cultural intelligence in students through activities such as exchange. Students can go to another country, meet other cultures, and interact and communicate with people from other cultures. Students achieve numerous benefits, by getting to know other cultures, such as expanding their knowledge, learning foreign languages, increasing their ability to cope in an intercultural environment and the like.

Factors and Method for Measuring Cultural Intelligence Cultural intelligence contains four factors: metacognitive, cognitive, motivational, and behavioral (Ang & Van Dyne, 2015). Metacognitive processes are used to obtain knowledge and awareness of one’s cognitive process. People with a higher level of development of metacognitive cultural intelligence can quickly adapt to the values, laws, and regulations of a particular local culture (Tuan, 2016). Cognitive cultural intelligence refers to the specific knowledge of the values and beliefs as well as the economic and legal systems of another culture (Vlajcic et al., 2019).

Motivational factor is ability to encourage efforts to function in international circumstances and establishing interaction with people from other cultures (Jyoti & Kour, 2015). Managers of companies that have a high degree of motivational cultural intelligence achieve good business performance (Lorenz, Ramsey & Richey, 2018). People with high levels of motivational cultural intelligence have self-confidence 75

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and want to interact with people who come from different cultural backgrounds (Li, 2020). The behavioral factor of cultural capacity refers to the demonstration of verbal and non-verbal abilities during the interaction with people from diverse backgrounds (Ang & Van Dyne, 2015). People with a high degree of behavioral cultural intelligence are very resourceful in intercultural communication (Shaik, Makhecha & Gouda, 2020). Caputo, Ayoko & Amoo (2018) emphasize that in order to adapt to a culturally different environment, it is necessary for an individual to possess appropriate verbal, but also non-verbal competencies such as body language, expression of movement and the like. This implies an extensive and flexible threshold for behavior.

Individual differences in cultural intelligence in individuals are measured using a variety of methods, such as the self-report method, the observer report, performance-based measures, and others. The selfassessment method is most commonly used and is based on a list of items applicable to various factors of cultural intelligence (e.g. "I check the accuracy of my cultural knowledge while communicating with people from different cultures"). It has already been mentioned in the paper that cultural intelligence is important from two aspects for multinational companies. Employees need to come from culturally diverse backgrounds and possess a variety of skills, as well as the company’s brands to be accepted in foreign markets by consumers. Zhou et al. (2018) in their paper provide an overview of the statements that can be used to check the level of cultural intelligence of employees in a company. Alon et al. (2016) in their work presented a model for measuring business cultural intelligence. Ang et al. (2007) provide an overview of the statements that can be used to check the level of cultural intelligence of consumers.

Most of the previous works that have dealt with market research and examining the degree of development of consumer cultural intelligence have used this scale.

Concept of Foreign Brand Market globalization plays a crucial role in mitigating the importance of national borders by fostering economic, political, and personal interaction. Due to the process of globalization, the once fractured markets of countries have been notably altered in the following aspects: a) economy - huge investment rate, b) technology - the growth of the Internet and contemporary communication technologies, c) social life - the rise of tourist and business trips in the world (Zabkar et al., 2017). Accordingly, internationally active companies show interest in understanding consumer motivation with respect to reactions to global brands.

Brand can be defined as a name, symbol, logo that differentiates the goods of one seller from the goods of other sellers (Mandler, 2019). Brand value refers to the financial amount of an established brand. It occurs when consumers have two alternative products of the same quality level at their disposal but buy a branded product even though it is more expensive because it reduces the risk when buying (Chailan & Ille, 2015). Brands make it possible to identify the company's products and services, as well as differentiate them from the competition. They are an effective and persuasive way of communicating the benefits and values that a commodity or assistance can provide. A brand is a guarantee of quality, origin and performance and significantly influences the decision of consumers in the buying process (Mandler, 2019).

In addition to companies, branding can be done by the state and then it is a matter of national branding. Rookwood (2019) questioned how organizing the 2022 FIFA World Cup will affect the image of Qatar's host country. Since this is a planetary, "mega" event, it is expected that most people will follow the events, especially those who love football.

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The state will generate significant revenue from sponsors, TV rights. The arrival of fans will have a positive impact on tourism and hospitality, and in addition, this is an ideal opportunity to present Qatar as a host country in a good light. Branding can also be done at the city level. The study by Kovačić et al.

(2019) examined what factors attract tourists to visit Banja Luka. The results showed that tourists are interested in the castle Kastel, which is the oldest historical monument in this city, the river Vrbas. Most tourists point out that they are delighted with Banja Luka kebabs, good nightlife, hospitable people, etc.

Also, celebrities can have a positive influence on the image of a city, and famous writer Petar Kočić is a native of Banja Luka. Belgrade can be mentioned as an example of branding at the city level. Foreign tourists most often visit the Kalemegdan Fortress, Aval Tower, the Museum of Yugoslavia, Tasmajdan Park, Knez Mihailova Street and other attractions (Nicic & Iguman, 2019).

Green branding, which refers to corporate social responsibility and environmental protection, is becoming increasingly popular. "Green marketing" is more popular concept and type of marketing which involves creating Eco products with distinct standards, which allows reducing the negative impact on the environment (Sarkar, 2012). Green marketing activities include reducing waste through reduction and recycling, introducing Eco-friendly packaging, reducing pollution during transport and the like.

Interconnection Between Cultural Intelligence and Acceptance of Foreign Brands Using the method of self-evaluation of cultural intelligence, two out of four factors of cultural intelligence were analyzed, metacognitive and behavioral factors. The metacognitive process of cultural intelligence was chosen for analysis because as reported by Ang & Van Dyne (2015) it represents critical part of cultural intelligence for several reasons: 1) it encourages effective thinking about people and circumstances independently of cultural origination; 2) it is the driver of active change in the person as opposed to the rigid view of culturally limited thinking and assumptions; 3) guide the individual towards adapting and revising their own strategies that will be more appropriate in accepting desired outcomes during intercultural encounters. It is also a metacognitive factor as a central part of cultural intelligence that allows for the adaptability, resourcefulness, and flexibility of the individual in the new cultural environment. Taking into account previous empirical research, the meta-cognitive factor of cultural intelligence is one that should facilitate the acceptance of foreign brands, i.e. brands that come from other cultures, and the goal was to analyze whether it influences and to what extent the acceptance of foreign brands.

The behavioral factor of cultural intelligence increases social interplay and focuses on people’s will and its ability to alter his or her conduct to adapt to cultural diversity. The importance of behavioral economics is borne out by the fact that psychologist Daniel Kahneman and economist Vernon Lomax Smith received the Nobel Prize in Economics in 2002 - Kahneman for incorporating psychological research into economic science, especially in the fields of human action and risk-taking decision making, and Vernon for introducing laboratory experiments into empirical economic analysis. The impact and importance of the behavioral economy is also reflected in the fact that Kahneman was one of the members of the current presidential election team. Because of the actuality and importance of behavioralism in the world of psychology, economics and beyond, it was chosen to analyze behavioral factor as well as the metacognitive factor of cultural intelligence and to determine whether it has any influence on the acceptance of foreign brands.

The metacognitive factor of cultural intelligence mirrors a person's mental awareness and consciousness throughout intercultural interconnection. Ang et al. (2007) explain that metacognitive cultural intelligence refers to how people perceive their behavior prior to interaction with individuals 77

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from divergent cultures. Metacognitive skills can foster out – of – the box thinking by assuming that metacognitively intelligent people are probably more innovative, even in a culturally different setting.

Therefore, it is assumed that individuals who are characterized by metacognitive ability will find it easier to accept people, brands, customs and products coming from different cultures. Previous empirical research (Ang & Van Dyne, 2015) has shown that the metacognitive factor leads people to adjust and amend their own plan of action that will be more suitable in accepting desired outcomes during intercultural encounters. The desired outcome in the case of this research is the acceptance of foreign brands. One of the few empirical studies (Frias-Jamilena et al., 2018b) dealing with the issue of the relationship between cultural intelligence and brands has shown that the acceptance of foreign brands is most influenced by the motivational and behavioral factor of cultural intelligence. Followed by metacognitive factor where a moderate correlation was present, while a low degree of correlation between the two variables was observed for the cognitive factor. Pratono & Arli (2020) found that there is a positive impact of cultural intelligence on the acceptance of foreign brands. Summarizing the foregoing, and in the context of the research covered in the paper, the first hypothesis can be formulated: H1: The metacognitive factor of cultural intelligence has a positive statistically significant impact on consumer acceptance of foreign brands.

The behavioral factor touches on individual's ability to introduce broad range of verbal and nonverbal action when interacting with people from diverse background (Ang et al., 2007). It lets individuals to control and adjust social behavior in intercultural confront so that there is slightest chance of misunderstanding and misjudgment. Research on intercultural communication documents greatly varies in appropriate modes of communication across cultures. Korzilius, Bucker & Beerlage (2017) emphasizes that is very important to apply adequate behavior in a certain cultural environment.

Consequently, behaviors that are suitable in communicating in one cultural situation may be unsuitable in another (Pratono & Arli, 2020). Therefore, people need to be adaptable to a different cultural setting. This flexibility in behavior is immensely principal in an intercultural context. The reason is that individuals do not have the quickest access to the notions, feelings, and motivations of others. Instead, they must come to certain conclusions on the basis of non-verbal communication signs such as facial expressions, looks, movements and the like (Caputo, Ayoko & Amoo, 2018). Cultural al teration is a person's perception in a given situation, because those who have the capacity to change their behavior (behavioral cultural intelligence) will have greater cultural conversion.

Frias-Jamilena et al. (2018a) found that the behavioral factor of cultural intelligence in tourism has a statistically significant positive impact on consumers when choosing foreign destinations. People with a high level of cultural intelligence visit foreign tourists’ destinations. They have the desire to communicate with people from other cultures, try the specialties of those countries, enjoy nature and adventures. Such people rarely go to places where they have already been because they have a desire to continuously meet new people, other cultures, and tourist destinations. Also, people with a high level of cultural intelligence buy foreign brands. The above researches form the basis for formulating another hypothesis in the paper: H2: The behavioral factor of cultural intelligence has a positive statistically significant impact on consumer acceptance of foreign brands.

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Correlation analysis in the research by Gooden, Creque & Chin-Loy (2017) revealed a positive correlation between the motivational and behavioral factors of intercultural intelligence, establishing that individuals with motivational property will have behavioral propensities. Motivational ability is based on the inherent motivation of an individual (Ang et al., 2007). It means that individuals who are interested in acknowledging cultural diversities, will alter their behavior to adapt to these diversities.

Previous research (Baes, 2013) has conducted gender-based testing on cultural intelligence. In the case of the motivational factor, there was analytically notable distinction in the results in the male sex. The motivational aspect of cultural intelligence represents the desire of an individual to contact people from different cultures (Shi & Shan, 2019). The statistically significant change in the results of this factor between the sexes can be explained by gender differences. This type of research has increased in the last decade and some gender differences are linked to biological and/or sociological causes. Another research has also shown that men adapt better to foreign culture than women (Ornoy, Nishri & Uziel, 2014), and research by Lee, Veasna & Wu (2013) shows that individuals with high motivational intelligence adapt better to foreign culture. When these two studies are correlated, it is concluded that the motivational factor of cultural intelligence is more pronounced in men and that they are more readily adapted to foreign culture. Respecting previous empirical research, and to link behavioral and motivational factors of cultural intelligence, a third hypothesis was formulated in this research: H3: The behavioral factor of cultural intelligence is more pronounced in male than female.

For ease of monitoring and better understanding of the work structure, the conceptual model was formulated and presented in Figure 1.

Figure 1: Research model

Metacognitive factor Acceptance of foreign brands Behavioral factor

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RESEARCH METHODOLOGY

An empirical study was conducted in January and February 2020 to examine the impact of the metacognitive and behavioral factors of cultural intelligence on foreign brand acceptance. For the collection of primary data, a survey method was used in the form of a questionnaire consisting of 14 statements.

Respondents provided answers via a five-point Likert scale and expressed a degree of agreement with a statement of grades 1 to 5. The findings were taken from relevant papers that previously addressed this issue, which is a common approach in field marketing research (Table 1 provides an overview of the findings used). The total sample size is 114 respondents. The data collected were processed by the SPSS Statistic V.23.

Analysis of the demographic profile of the sample shows that women are dominant gender in the sample. Specifically, the sample contains 48 male respondents (42.1%) and 66 female respondents (57.9%). In terms of age, younger respondents (up to 30 years) make up the largest demographic group and have a total of 57.9% in the sample. Regarding monthly income, the largest percentage of the sample is made up of respondents with monthly income over 50,000 dinars (35.1%).

Table 1: Research variables and used items Variable

Items

Source

Metacognitive factor

1. I am aware of the cultural knowledge which I use in interaction with people from different cultural places.

2. When I interact with people from unrelated cultures I always try to adjust my cultural knowledge.

3. I am aware of the cultural ability that I usein intercultural communication.

4. I check the level of my cultural ability in interaction with people with different cultural backgrounds.

Adapted to: Ang et al. (2007)

Behavioral factor

5. I change my accent and tone when intercultural situation demands it.

6. I use different breaks and silence to respond to specified cultural demands.

7. I change the pitch of my speaking when the cultural situation requires it.

8. I change my nonverbal abilities when intercultural interaction demands it.

9. I change facial expressions depending on the intercultural situation I am in.

Adapted to: Ang et al. (2007)

Acceptance of foreign brands

10. I like to buy foreign branded products.

11. I buy foreign branded products when there are other products of the same type.

12. I delay purchase when I can't find the foreign branded product I use.

13. I am ready to set aside a larger sum of money to buy foreign branded products.

14. I am ready to recommend to other foreign branded products that I use.

Adapted to: Son, Jin & George (2013); Lee & Nguyen (2017).

Source: Author's 80

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RESEARCH RESULTS

The study first applied a reliability analysis to determine whether the findings by which certain variables are measured are internally consistent. Table 2 presents the results obtained.

Table 2: Reliability analysis Variable

Cronbach’s alpha

Metacognitive factor

0.899

Behavioral factor

0.914

Acceptance of foreign brands

0.932

Source: Author's

Reliability analysis is usually performed by considering the values of the Cronbach's alpha coefficient.

For a variable to have an adequate degree of reliability, it is necessary that the value of this coefficient is higher than 0.70 (Nunnally, 1978), which is realized in the case of the above variables. In the second step, a descriptive statistical analysis was applied. Table 3 reveal the values of arithmetic means (M) and standard deviations (SD) for the used statements.

Table 3: Results of descriptive statistics Items

M

SD

1. I am aware of the cultural knowledge which I use in interaction with people from different cultural places.

2.27

0.91

2. When I interact with people from unrelated cultures Ialways try to adjust my cultural knowledge.

2.97

1.42

3. I am aware of the cultural ability that I use in intercultural communication.

3.5

0.88

4. I check the level of my cultural abilityin interaction with people with different cultural backgrounds.

3.26

1.04

5. I change my accent and tone when intercultural situation demands it.

2.97

1.32

6. I use different breaks and silence to respond to specified cultural demands.

2.82

1.89

7. I change the pitch of my speaking when the cultural situation requires it.

2.96

1.62

8. I change my nonverbal abilities when intercultural interaction demands it.

2.96

1.43

9. I change facial expressions depending on the intercultural situation I am in.

2.98

1.51

10. I like to buy foreign branded products.

2.83

1.46

11. I buy foreign branded products when there are other products of the same type.

3.11

1.65

12. I delay purchase when I can't find the foreign branded product I use.

3.54

1.69

13. I am ready to set aside a larger sum of money to buy foreign branded products.

2.96

1.61

14. I am ready to recommend to other foreign branded products that I use.

3.39

1.41

Source: Author's

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Respondents expressed the most favorable attitudes on the basis of the statement “I delay purchase when I can't find the foreign branded product I use” (the highest arithmetic mean 3.54), while the most unfavorable attitudes the respondents expressed on the basis of the statement “I am aware of the cultural knowledge which I use in interaction with people from different cultural places” (lowest value of arithmetic mean 2.27). Respondent's attitudes are the most homogeneous based on the statement “I am aware of the cultural ability that I use in intercultural communication” (the lowest standard deviation value is 0.88). Respondent's attitudes are the least homogeneous based on statement “I use different breaks and silence to respond to specified cultural demands” (highest standard deviation value 1.89). Table 4 shows the results of the correlation analysis.

Table 4: Results of correlation analysis Metacognitive factor

Behavioral factor

Acceptance of foreign brands

Metacognitive factor

1

0.328*

0.422*

Behavioral factor

0.328*

1

0.659*

Acceptance of foreign brands

0.422*

0.659*

1

Source: Author's Note: * coefficients are statistically significant at the level 0.01

The results show that all values of Pearson's linear correlation coefficient are statistically significant.

The behavioral factor of cultural intelligence has a greater association with foreign brand acceptance than the metacognitive factor of cultural intelligence.

Table 5 presents the results of multiple regression analyzes, which measured the impact of the metacognitive and behavioral factors of cultural intelligence on foreign brand acceptance.

Table 5: Results of multiple regression analysis (dependent variable: Acceptance of foreign brands) β

Sig.

VIF

Metacognitive factor

0.124

0.217

1.526

Behavioral factor

0.386*

0.001

1.386

Source: Author's Note: * coefficients are statistically significant at the level 0.01;R2=0.448

The coefficient of determination (R2) is 0.448, which means that 44.8% of the variability of acceptance of foreign brands is explained by this regression model. The data are appropriate for conducting multiple regression analysis because the value of the VIF coefficient in all pairs is less than 5, so there is no problem of multicollinearity (Field, 2000). Based on the presented results, it is concluded that the behavioral factor of cultural intelligence has a statistically significant influence on the acceptance of foreign brands (β = 0.386, p <0.01), while the influence of the metacognitive factor is not statistically significant (β = 0.124, p> 0.01). Based on the above, it can be concluded that hypothesis H1 was not confirmed, while hypothesis H2 was confirmed. For testing the third hypothesis, a T test was conducted for two independent samples, through which the expression of behavioral factor was analyzed based on the gender criterion (Table 6).

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Table 6: Results of T test for two independent samples: the criterion for comparison is gender of respondents Factor name

Women

Man

T

sig.

AS

SD

AS

SD

1. I change my accent and tone when intercultural situation demands it.

2.95

1.59

3.00

0.84

-0.184

0.850

2. I use different breaks and silence to respond to specified cultural demands.

2.94

2.01

2.67

1.71

0.768

0.444

3. I change the pitch of my speaking when the cultural situation requires it.

2.94

2.01

3.00

0.84

-0.208

0.835

4. I change my nonverbal abilities when intercultural interaction demands it.

2.45

1.34

3.67

0.95

-5.067*

0.000

5. I change facial expressions depending on the intercultural situation I am in.

3.45

1.51

2.33

1.26

4.249*

0.000

∑BH Behavioral factor of cultural intelligence

2.95

1.69

2.93

1.12

-0.442

0.709

Source: Author's Note: * coefficients are statistically significant at the level 0.01

The results of the t test for the two independent samples shown in Table 6, it is evident that for the first three findings there is no statistically significant difference in the behavioral factor of cultural intelligence among men compared to women. However, forth statement “I change my nonverbal abilities when intercultural interaction demands it” and in fifth statement “I change facial expressions depending on the intercultural situation I am in”, statistically significant differences between women and men occur.

With the fourth statement “I change my nonverbal abilities when intercultural interaction demands it” men agree to a greater extent (arithmetic mean 3.67) than women (arithmetic mean 2.45). Also, regarding this statement, men also have more homogeneous views, which show a lower standard deviation value of 0.95, while this value in women is 1.34. In the fifth statement “I change facial expressions depending on the intercultural situation I am in”, women agree to a greater extent (arithmetic mean is 3.45) than men (arithmetic mean is 2.33). But, based on standard deviation data that women's attitudes in this statement are more heterogeneous (standard deviation is 1,51) compared to men's attitudes (standard deviation is 1,26).

Comparing the summary data for arithmetic means for women and men, it is concluded that there is no statistically significant difference in the expression of behavioral cultural intelligence factors for women and men, and that the gender factor has no effect (2.95 for women, while for men the arithmetic mean is 2.93). Based on the above, it can be concluded that hypothesis H3 has not been confirmed.

CONCLUSION Through terminological definition and analysis of metacognitive and behavioral factors of cultural intelligence, the aim was to determine how metacognitive and behavioral factors of cultural intelligence influence the acceptance of foreign brands. Also, the aim is to determine how these findings can help multinational companies in shaping and presenting their brands so that they are better accepted in different cultural environments. Despite the need to improve the ability to work and function in other intercultural environments to reach multinational company brands with greater reach and response from 83

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consumers in different cultural settings, the effect of metacognitive and behavioral cultural intelligence factors on foreign brand acceptance has been addressed in a small number of worldwide research, and especially in the territory of the Republic of Serbia, which poses s a key originality and contribution of this work. The theoretical implications of the paper are reflected in the expansion of existing knowledge about the influence of the metacognitive and behavioral factor of cultural intelligence on the acceptance of foreign brands and its results can be used for further research in this field.

Three hypotheses were put forward in the paper. The results of multiple regression analyzes showed that the metacognitive factor of cultural intelligence had no statistically significant influence on foreign brand acceptance, thus refuting hypothesis H1. Also, the results of the same analysis showed that the behavioral factor of cultural intelligence had a statistically significant effect on the acceptance of foreign brands, thus confirming hypothesis H2. The results of the T test indicated that the behavioral factor of cultural intelligence was equally represented in both sexes, thus refuting the H3 hypothesis, which suggested that the behavioral factor of cultural intelligence was more pronounced in men. The first hypothesis sought to determine whether the metacognitive factor of intercultural intelligence influences foreign brand acceptance. The results of the multiple regression analyzes showed that the metacognitive factor had no statistically significant influence on the acceptance of foreign brands, thus refuting the hypothesis H1 and not confirming the results of previously conducted empirical studies that showed the influence of the metacognitive element of cultural intelligence on the acceptance of foreign brands (Frias-Jamilena et al., 2018b; Pratono & Arli, 2020). Previous research (Pratono & Arli, 2020) has also shown that metacognitive abilities grow as a function of age that is, with aging, they are able to adapt to new situations and things. Taking into account research results in this study, it is concluded that the results were performed by statistical analyses showing that the metacognitive factor of cultural intelligence does not affect the acceptance of foreign brands, resulting from the fact that the majority of the sample are young respondents and middle-aged respondents.

A second hypothesis was defined to identify whether the behavioral factor of cultural intelligence influences foreign brand acquisition. Given that cultural adjustment is a person's sense of fit in a given situation, multiple regression analyses have shown that those individuals with a higher degree of ability to change their behavior in different cultural settings (behavioral cultural intelligence) will be more willing to embrace brands that come from foreign countries, as confirmed by previous empirical research (Frias-Jamilena et al., 2018a; Pratono & Arli, 2020). This confirms the hypothesis H2.

A third hypothesis has been put forward to determine whether the behavioral factor of cultural intelligence is more pronounced in men. Specifically, the hypothesis is based on an individual's intermotivation, which states that individuals who are interested in acknowledging cultural diversities, will alter their behavior to adapt to these diversities (Ang et al., 2007), as well as previous empirical research that has shown that men adapt better to foreign culture than women (Ornoy, Nisri & Uziel, 2014).

However, after the T test, the result showed that the behavioral factor of cultural intelligence was not more pronounced in men, but that it was the same in both sexes, that is, the sexuality of the behavioral factor did not depend on gender, thus rejecting hypothesis H3. This was only confirmed by previous research (Baes, 2013) where in the case of cognitive, metacognitive, and behavioral factors there was no statistically significant difference in scores between men and women.

The results of the empirical research conducted have important practical implications. The results obtained confirm the view that the behavioral factor of cultural intelligence influences the acceptance of foreign brands. This knowledge can help multinational companies in shaping their brands, so that when they enter culturally different markets, they have better acceptance of their brands with consumers.

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This implies that as companies continue their operations in the international market, the behavioral factor of cultural intelligence will play an essential part in their success. In different circumstances, the metacognitive factor of cultural intelligence did not show statistical significance in the acceptance of foreign brands. Bearing in mind that metacognitive abilities increase with aging, and that less than a third of respondents in the work belonged to the older age, it can be implied that the statistically negative result was due to the sample where the demographic group represented the younger age group.

Also, an important implication is that there are no dissimilarities in the behavioral factor of cultural intelligence between different genders. Psychology has shown that behavioral modeling can influence the behavior of participants by subverting behavior that leads to the desired goal, in this case the acceptance of foreign products. Since there is no difference in behavior between men and women, this can be used to encourage desired consumer behavior that will lead to the achievement of the goals of multinational companies.

Despite its contribution, the research conducted for the purpose of this paper has several limitations, which at the same time provides directions for future research. First limitation is a small sample of respondents with a total of 114 respondents. Compared to the surveys previously conducted in this area, there is a need to enlarge the number of interviewed respondents. Another limitation is that the survey was conducted using a survey method where there is a danger of giving socially desirable answers. The third limitation is the absence of the remaining two factors (cognitive and motivational factors) that, in addition to the metacognitive and behavioral factors, form an integral part of cultural intelligence.

I Infuture studies authors should use other factors.

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Ornoy, H., Nishri, G. L., & Uziel, L. (2014). Relationship between the Personality and Gender and the Adjustment to a Foreign Culture: Comparative Research between Expatriates and Business Trip Travelers. International Journal of Business Administration, 5, 122–134. DOI:10.5430/ijba.v5n3p122 Pratono, A.H., & Arli, D. (2020). Linking global consumer culture and ethnocentric consumerism to global citizenship: exploring the mediating effect of cultural intelligence. International Journal of Sociology and Social Policy, 40(7/8), 659-675. DOI:10.1108/IJSSP-10-2019-0212 Rookwood, J. (2019). Access, security, and diplomacy: perceptions of soft power, nation branding and the organizational challenges facing Qatar’s 2022 FIFA World Cup. Sport, Business and Management, 9(1), 26-44.

DOI:10.1108/SBM-02-2018-0016 Sarkar, A. N. (2012). Green Branding and Eco-innovations for Evolving a Sustainable Green Marketing Strategy. AsiaPacific Journal of Management Research and Innovation, 8(1), 39–58. DOI: 10.1177/2319510X1200800106 Son, J., Jin, B., & George, B. (2013). Consumers' purchase intention toward foreign brand goods. Management Decision, 51(2), 434-450. DOI:10.1108/00251741311301902 Shi, X., & Shan, X. (2019). Cross-cultural impact on financial company’s online brand personality. Marketing Intelligence & Planning, 37(5), 482-496. DOI:10.1108/MIP-06-2018-0233 Shaik, F.F., Makhecha, U.P., & Gouda, S.K. (2020). Work and non-work identities in global virtual teams: Role of cultural intelligence in employee engagement. International Journal of Manpower, 42(1), 51-78.

DOI:10.1108/IJM-03-2019-0118 Tuan, L.T. (2016). From cultural intelligence to supply chain performance. International Journal of Logistic Management, 27(1), 95-121. DOI:10.1108/IJLM-01-2014-0009 Thomas R., & Van Dyne, L. (2018). A bi-factor theory of the four-factor model of cultural intelligence: metaanalysis and theoretical extensions. Organizational Behavior and Human Decision Processes, 148, 124–144.

DOI: 10.1016/j.obhdp.2018.07.005 Vlajcic, D., Marzi, G., Caputo, A., & Dabic, M. (2019). The role of geographical distance on the relationship between cultural intelligence and knowledge transfer. Business Process Management Journal, 25(1), 104-125.

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UTICAJ FAKTORA PONAŠANJA I METAKOGNITIVNIH FAKTORA KULTURNE INTELIGENCIJE NA PRIHVAĆENOST STRANOG BRENDA Rezime: Cilj rada je da pokaže uticaj kulturne inteligencije na prihvatanje stranih brendova i kako ti uvidi mogu pomoći multinacionalnim kompanijama u oblikovanju i predstavljanju svojih brendova kako bi bili bolje prihvaćeni u različitim kulturnim sredinama. Kulturna inteligencija sastoji se od četiri faktora, od kojih su dva analizirana za potrebe ovog rada - metakognitivni faktori i faktori ponašanja. Metakognitivne kulturne dimenzije su korisne za pojedinca da bi se steklo određeno znanje o nekoj kulturi. Kulturna inteligencija u ponašanju predstavlja sposobnost prilagođavanja pojedinca verbalnom i neverbalnom ponašanju u skladu sa situacijom u kulturno raznolikim sredinama. Rezultati empirijskog istraživanja pokazali su statistički značajan pozitivan uticaj bihejvioralnog elementa kulturne inteligencije na prihvatanje stranih brendova od strane potrošača, dok uticaj metakognitivnih faktora na prihvatanje stranih brendova nije statistički značajan, što se razlikuje od većine prethodno sprovedenih empirijskih studija.

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Ključne reči: kulturna inteligencija, metakognitivni faktori, faktori ponašanja, strani brend.

Klasifikacija jela: D91, Z13

EJAE 2021, 18(1): 89 - 105 ISSN 2406-2588 UDK: 330.144(594)"2010/2018" DOI: 10.5937/EJAE18-26921 Original paper/Originalni naučni rad

THE VULNERABLE FINANCIAL ISSUE: CAPITAL FLIGHT IN INDONESIA Muhammad Basorudin*, R. Dwi Harwin Kusmaryo, Sri Hartini Rachmad, Gantjang Amannullah, Serly Rachmadani Hamid Badan Pusat Statistik - Statistics Indonesia, Jakarta, Republic of Indonesia

Abstract: Indonesia is a developing country with a high demand for capital from both domestic and international sources. However, international capital flows are needed the most. For non-Western countries, especially Indonesia, capital flight is an unfavourable financial problem. This research aims to summarise capital flight from Indonesia and analyse the impact of macroeconomic and non-macroeconomic determinants through capital flight. Macroeconomic determinants include budget deficits, economic growth, inflation rates, and exchange rates. Nonmacroeconomic determinants are the degree of trade openness, interest rate differences, and dummy ratings. The data comes from the Bank of Indonesia, OECD, Moody's, and BPS-Statistics Indonesia. The coverage of this research is the Indonesian quarter from 2010 to 2018. This period complies with the latest procedures of the sixth edition of the Balance of Payments Manual (BPM 6). In this research, the measurement of the capital flight is the World Bank’s residual method, trade misinvoicing method, and combined method. This research finds that, compared with other economics, non-macroeconomics is the most influential determinant of capital flight from Indonesia.

Article info: Received: Jun 8, 2020 Correction: August 5, 2020 Accepted: December 11, 2020

Keywords: capital flight, combined method, non-macroeconomics, Indonesia.

INTRODUCTION After winning the 2019 presidential election, President Jokowi made a breakthrough in the National Development Plan (RPJMN) for 2020-2024. He will move Jakarta, the capital of Indonesia, to the island of Borneo. This policy will require a large budget, and make investors interested in investing their assets in Indonesia. He also built a track for the MotoGP event in Mandalika, a special economic zone in West Nusa Tenggara, from 2021-2023. The project will attract more investors from all over the world.

*E-mail: m.basorudin@gmail.com

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BASORUDIN. M. R., KUSMARYO. D. H., RACHMAD. S. H., AMANNULLAH. G., HAMID. S. R.  THE VULNERABLE FINANCIAL ISSUE: CAPITAL FLIGHT IN INDONESIA

In order to obtain large amounts of investment from all over the world, the Jokowi government has simplified investment regulations to create a business climate in Indonesia that is simpler than ever.

The BKPM Investment Coordination Committee has developed a "one-stop service" system aimed at coordinating the permits and approvals of various ministries and commissions. This service is composed of 22 ministries and lasts 3 hours. Thanks to this innovation, Indonesia’s Ease of Doing Business (EoDB) has risen from 114th (2014) to 72nd (2018) among 190 countries/regions.

Unfortunately, Indonesia recently lost its capital. Due to the social problems of the opposition, many capitalists or investors are moving/have moved their assets abroad. Several ministers were involved in corruption and criminal scandals during the previous government. This situation worsened the political situation. Thus, many investors continuously and abnormally invest funds from Indonesia in foreign countries. This situation is called "capital flight".

Capital flight is also classified as illegal capital flow. According to the 2017 Global Financial Integrity (GFI) report, Indonesia’s illicit capital inflows are huge, amounting to USD 271.65 billion between 2005 and 2014.

At the moment, Indonesia and 209 other countries are facing the same problem: the COVID-19 pandemic. As of April 9, 2020, Indonesia had confirmed as many as 3,293 COVID-19 cases. The Asian Development Bank (ADB) predicts that, due to COVID-19, Indonesia’s economy will grow by 2.5% in 2020. From an investment perspective, the lack of clear emergency measures will expose the business world and investors to unprecedented uncertainty. The market crash shows that the investment risk here has increased.

Now, due to the COVID-19 pandemic, foreign investors have recently dumped over USD 5 billion in Indonesian stocks and bonds (Samboh, 2020). Since Indonesia's business and investment environment is not good enough, this poor situation may cause capital flight to increase rapidly. The Indonesian government must foresee the negative impact of COVID-19 on the investment environment.

In an emerging country like Indonesia, capital movements play a key role in economic activity. The international flow of capital leads to the expansion of business activities, changes in economic structure, economic growth, the balance of payments, employability, economic stability in some countries, and the enhancement of competitiveness and the creation of globally competitive products (Sovran & Hadzic, 2016). If a country has financial problems, such as capital flight, this may reflect a vote of no confidence in Indonesian assets. Ultimately, Indonesia's economy will be disrupted.

Capital flight will bring some adverse effects, such as reduced income and a decline in the ability of banks to create funds for investment projects. Most importantly, capital flight contributes to the income from non-Western countries to Western countries (Henry, 2013). In addition, capital flight greatly decreases private investment, but does not affect public investment (Yalta, 2010). Based on previous explanations, the purpose of this research is to summarise the investment potential of Indonesia in the short-term and long-term. In addition, this research also analyses the impact of macroeconomic and non-macroeconomic determinants of capital flight in Indonesia.

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BASORUDIN. M. R., KUSMARYO. D. H., RACHMAD. S. H., AMANNULLAH. G., HAMID. S. R.  THE VULNERABLE FINANCIAL ISSUE: CAPITAL FLIGHT IN INDONESIA

LITERATURE REVIEW Previous Studies

There are many determinants to capital flight. Some research found that the determinants of capital flight belonged to macroeconomics. An increase in budget deficit may also influence capital flight (Baek & Yang, 2010; Mccaslin, 2013; Han et al., 2012).

On the other side, high inflation rates have a positive impact on capital flight (Ndikumana et al., 2014; Gouider and Nouira, 2014). At the same time, economic growth is a decisive factor for negative impacts, and therefore helps decrease capital flight (Gouider and Nouira, 2014; Cheung, Steinkamp and Westermann, 2016; Ndikumana et al., 2014). On the other hand, the exchange rate does not influence the capital flight of some non-Western countries (Geda & Yimer, 2016; Adetiloye, 2012). Risks and returns (such as financial stability, domestic tax rates) and other macroeconomic determinants can also affect capital flight (Efobi & Asongu, 2016).

The degree of trade openness is also a statistically significant variable for trade misinvoicing. Its coefficient is positive (Cheung, Steinkamp, & Westermann, 2016; Mccaslin, 2013). This variable serves as a proxy for the existence and severity of trade misinvoicing. However, this currency is one of the most widely used determinants of capital flight.

In some ASEAN countries, there is a long-term equilibrium relationship between economic openness and GDP, as foreign direct investment, imports, and exports show the most important aspects of international economic integration, and the information is particularly important. There is a causal relationship between foreign direct investment, imports, exports, and GDP. In general, foreign direct investment, imports, and exports will significantly affect and lead to economic growth in the short-term and long-term (Vogiatzoglou & Nguyen, 2016).

The results of econometric analysis based on a sample survey of 30 African countries from 1970 to 2015 also show that foreign direct investment flows are positively correlated with capital flight, which implies that the phenomenon of capital flight may be driven by foreign direct investment. However, there is no evidence that foreign direct investment will have spillover effects. The stock of foreign direct investment in the past does not affect capital flight. On the other hand, high natural resource rents are related to high capital flight, and the quality of the system cannot alleviate this connection (Ndikumana & Sarr, 2019).

In some European countries (1996-2009), capital flight was determined by interest rate differentials and investors’ perceptions of their economic conditions, as well as their access to funds that could be transferred overseas. Loans and capital inflows (Brada, Kutan and Vukšić, 2013).

Political risks and financial crises have a positive impact on capital flight. At the same time, external debt, foreign direct investment (FDI), and the stock market have a negative effect on capital flight (S.-L. Liew et al., 2016). Then again, political risk is also an important determinant for capital flight, especially corruption (Baek & Yang, 2010). Deposit interest rates and inflation also have an eloquent effect on capital flight (Mccaslin, 2013). Debt changes, lagging inventory, and capital flight (lagging) have a significant and positive impact on capital flight; while inflation (lagging) and economic growth will have a negative impact (Ndikumana et al., 2014).

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BASORUDIN. M. R., KUSMARYO. D. H., RACHMAD. S. H., AMANNULLAH. G., HAMID. S. R.  THE VULNERABLE FINANCIAL ISSUE: CAPITAL FLIGHT IN INDONESIA

In European countries such as Germany, traditional determinants of capital flight (such as the interest rate differential of guarantees) play only a limited role, while factors addressing the crisis, such as the uncertainty of economic policy, the collateral policy of the European Central Bank, and currency misalignment, are the driving factors causing this situation. Investors clearly evade safety (Cheung, Steinkamp, and Westermann, 2020).

In another part of the world, Latin America has experienced many interdependence problems, such as financial crises, unsustainable differences in power, sudden cessation of capital flows, capital flight, and the collapse of growth rates. Using data from eight Latin American countries during 1993-2015, capital flight is an interesting variable, showing a positive impact on sovereign bond spreads. Indeed, in order to increase debt repayment capabilities, Latin American countries are required to curb and prevent capital flight (Dachraoui, Smida, and Sebri, 2020).

In the long-term, only the debt stock, i.e., the difference between the nominal exchange rate and the interest rate (IR), has no significant impact on capital flight. In addition, in the long-term, political turmoil will have a negative impact on Nigeria’s capital flight (Geda & Yimer, 2016). Changes in inflation, Corruption Perception Index (CPI), debt, exchange rate and GDP have a positive impact on capital flight. However, changes in foreign direct investment have a negative impact on capital flight.

Only inflation, CPI and exchange rate changes have eloquent consequences for capital flight (Wujung & Mbella, 2016).

The ratio of capital flight (lagging), government debt ratio, economic growth, the real effective exchange rate (REER), Indonesia-US IR difference and virtual sovereign rating have a significant impact on capital flight (Basorudin, Kusmaryo and Rachmad, 2020). Looking at recent research, trade opening, political risks, economic growth and capital control have had negative impacts on capital flight. On the other hand, exchange rate regime, inflation rate and Strategic Economic Dialogue (SED) have a positive impact on capital flight (Cheung, Steinkamp, and Westermann, 2016).

In addition, some recent research shows that non-macroeconomic determinants lead to capital flight.

Country risk, political turmoil, corruption and war are non-macroeconomic determinants that lead to capital flight (Geda & Yimer, 2016; Baek & Yang, 2010; Ndikumana, etc., 2014). Some research also have shown that economic crises, political risks and corruption are the main determinants of capital flight (Gunter, 2017; Cheung, Steinkamp and Westermann, 2016; Ndoricimpa, 2018; Efobi and Asongu, 2016).

Many kinds of research have shown that governance factors are one of the most influential determinants of capital flight. They are tax regulations, lower limits, much corruption, poor governance and abuse of political power (Ndikumana et al., 2014). However, these determinants are confusing and complicated to measure in Indonesia. Therefore, this research uses measurable, alternative non-macroeconomic determinants. The determining factor is the sovereign rating (Basorudin, Kusmaryo and Rachmad, 2020). Other research also used dummy variables as a another non-macroeconomic determinant of capital flight (Han et al., 2012).

Due to the complexity of capital flight, this research proposes non-macroeconomic determinants.

This problem is the effect of the behaviour of the wealthy in the country. They want to flee their assets and wealth abroad for profit. They may stay away from investing their wealth simply because they are afraid of losing it. Both macroeconomic determinants and non-macroeconomic determinants are the main causes of capital flight.

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BASORUDIN. M. R., KUSMARYO. D. H., RACHMAD. S. H., AMANNULLAH. G., HAMID. S. R.  THE VULNERABLE FINANCIAL ISSUE: CAPITAL FLIGHT IN INDONESIA

Capital Flow Paradigm: Lucas Paradox

The neoclassical theory believes that capital should flow from Western countries to non-Western countries (Qolbi & Kurnia, 2015). However, the Lucas paradox criticizes this theory. According to Lucas' paradox, capital will not flow from the United States to India. Other research also found that capital flows from non-Western countries to Western countries. This situation indicates capital flight.

This contradictory issue with the neoclassical theory is called the "Lucas Paradox." The theoretical explanation of the Lucas paradox can be divided into two categories. They are fundamental differences and flaws in the international capital market (Alfaro, Sebnem and Volosovych, 2008). This is a further explanation of Lucas's paradox: 1) Fundamental Differences a. Missing factors of production One of the shortcomings of neoclassical theory is that it ignores the existence of other factors, such as human capital and land, which have a positive impact on the return of capital. This is why capital does not flow from Western countries to non-Western countries. b.Government policy Government policies may be another obstacle to capital flow and capital return. c. Institutional structures It is believed that, by protecting the property rights of investors from government influence, institutions will have an impact on investment decisions, thereby affecting the performance of the national economy.

2) International Capital Market Imperfections a. Asymmetric information Generally, the recipient of capital has more knowledge than the investor. If the investor does not have further information, the investor will tend to reduce investment (underinvestment).

As a result, capital will not flow from Western countries to non-Western countries. b. Sovereign risk Sovereign risk is the perception non-residents have of domestic economic conditions (Ariefianto & Soepomo, 2011). Normally, the sovereign risk of non-Western countries is high, so investors worry about no return on capital. As a result, capital will not flow to non-Western countries.

METHODOLOGY Measuring Capital Flight Calculating the amount of capital flight required calculation through the following approaches: 1. Residual Method 2. Dooley Method 3. Trade Misinvoicing 4. Hot Money Method 5. The Asset Method 93

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BASORUDIN. M. R., KUSMARYO. D. H., RACHMAD. S. H., AMANNULLAH. G., HAMID. S. R.  THE VULNERABLE FINANCIAL ISSUE: CAPITAL FLIGHT IN INDONESIA

The above five measurement methods are divided into two categories: indirect measurement and direct measurement. The first three methods are indirect measurement, while the other methods are direct measurement. The residual method has been used in some recent research. In addition, since the measurement methods of capital flight include multiple types of personal capital flows, the residual method is the most appropriate measurement method in many situations (Wujung & Mbella, 2016).

Unfortunately, the measurement of the residual method ignores the flow of funds, just like debt forgiveness. The residual method developed by the World Bank is as follows: (1) ED means foreign debt, FI means net foreign investment, CAD means current account deficit, and R means reserves. When the difference between the source of funds and the source of use is positive, it is called capital flight. Conversely, negative values are called inward capital flight.

In addition, some research points out that import and export activities are used to promote capital flight. For example, insufficient export invoices, excessive import invoices, and vice versa (Cheung, Steinkamp, and Westermann, 2016). In this research, the residual method and trade misinvoicing method is considered to be a feasible conceptual measure. These two measurements have been widely used in the literature.

On the other hand, direct measurement, especially hot money, has caused some criticism. Its measurement is based on errors and omissions. Errors and omissions consist of errors in compiled data, calculation errors, and unreported imports. In addition, the hot money method and other methods have also produced very inconsistent results (Mahmood, 2013). There is further criticism of direct measurement, especially asset methods. The metric assumes that national depositors have been reported, but this does not always happen in reality (Yalta, 2009).

Now, some research shows that import and export activities are used to promote capital flight (Cheung, Steinkamp and Westermann, 2016). Trade misinvoicing refers to illegal capital flows abroad, such as money laundering, smuggling, etc. (Yalta, 2009). In the trade misinvoicing valuation method, it is assumed that importers and exporters report that the value of imported goods is too high and the value of exported goods is too low (Yalta, 2009).

Where EM is export misinvoicing, is Partner countries reported the value of import from Indonesia, and is Indonesia's total export value to the country i.

(2) On the other hand, IM is an import invoicing, is Indonesia's total import value from the country i, Partner countries reported value of export to Indonesia. And the export misinvoicing and import misinvoicing are called trade misinvoicing measurement of capital flight. In this research, the value of CIF is assumed to be 10 per cent commonly adopted in recent kinds of research on trade misinvoicing (Cheung, Steinkamp and Westermann, 2016).

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BASORUDIN. M. R., KUSMARYO. D. H., RACHMAD. S. H., AMANNULLAH. G., HAMID. S. R.  THE VULNERABLE FINANCIAL ISSUE: CAPITAL FLIGHT IN INDONESIA

A positive sign on export misinvoicing indicates a net outflow (export under-invoicing), while a negative sign indicates net inflow. However, the positive sign on imports misinvoicing indicates a net outflow (importer over-invoicing), while a negative sign indicates net inflow. In this research, we expected the positive sign of exports and imports misinvoicing (Ndikumana et al., 2014).

The mechanism for trade misinvoicing is similar to the black market. Importers will be subject to higher tariffs to bring their capital "black money" to tax havens abroad. Similarly, exporters will take this "black money" abroad (Mahmood, 2013).

In this research, the World Bank residuals and trade misinvoicing methods are feasible conceptual measures. These two measurements have been widely used in the literature. Therefore, in this research, we will use well-known metrics, residual methods and trade invoice errors to measure capital flight.

Then, we will also use a new measurement, the combined method. However, this method is a modification of the capital flight method in the kinds of research of Cheung, Steinkamp and Westermann (2016) and Wujung & Mbella (2016).

Data Sources These data are collected from the Bank Indonesia, BPS-Statistics Indonesia, OECD, Bloomberg and Moody's. In addition, the coverage of this research is the Indonesian quarter from 2010 to 2018. This period is suitable for the latest guidance of the balance of payments manual sixth edition program. The independent variables in this research are macroeconomic variables and non-macroeconomic variables.

They are the budget deficit as a percentage of GDP (budget ratio), economic growth, inflation rate, exchange rate, interest rate difference (US and Indonesia), trade openness and false sovereign ratings.

In this research, we used modified new dummy variables. When Indonesia's sovereign rating value is lower than "Baa3", the virtual value is zero. This means that Indonesia's business climate is at a speculative level. When the rating is "Baa3" or higher, the dummy has a value of 1. Therefore, the business climate is in a good state or investment level.

Estimation Technique The estimation technique used in this research is ordinary least squares (OLS). Previous kinds of research have used this technique to estimate capital flight. Therefore, the regression model of capital flight is as follows: (3) Where: CF

= Capital flight (QtoQ)

RDef

= Budget deficit to GDP ratio (QtoQ)

Eco

= Economic growth (QtoQ)

Inf

= Inflation rate (QtoQ)

ER

= Exchange rate (QtoQ)

IR_Diff = Interest rate differences (US-Indonesia) (QtoQ) 95

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BASORUDIN. M. R., KUSMARYO. D. H., RACHMAD. S. H., AMANNULLAH. G., HAMID. S. R.  THE VULNERABLE FINANCIAL ISSUE: CAPITAL FLIGHT IN INDONESIA

Open

= Trade openness (QtoQ)

DR

= Dummy Rating (QtoQ) = Error (QtoQ)

RESULTS Figure 1: Capital flight (billion USD) in different measurements a. World Bank Residual (WBR)

b. Trade Misinvoicing (TM)

c. Combined Method (CM)

d. Combined Method Ratio to GDP (CMR)

Source: Authors’ calculation Notes: The calculation of capital flight is based on the World Bank’s residual (a), trade misinvoicing (b), and combined method (c) and combined method ratio to GDP (d). The combined method (CM) is the sum of the World Bank’s residual and trade misinvoicing. ButHowever, the CM ratio is the total capital flight from CM to real GDP (with 2010 as the base year).

Figure 1 shows that WBRs do not match each other. Since 2011, there has been a capital reversal.

However, other methods will not work. This difference is the result of Indonesia's reduction of trade restrictions and liberalization. This means that there is still a lot of illegal capital flows in trade. Many exporters and importers have benefited from trade. The measurement results may show different aspects.

The World Bank's method is that of processing international transactions from the balance of payments statistics, while the TM method concentrates on false reporting of trade transactions.

In Figure 1, after the 2008 global economic crisis, the capital flight of Indonesia (WBR) increased rapidly. From the first quarter of 2010 to the second quarter of 2011, the scale of its capital outflow was larger than that in subsequent periods. The reason for this situation is the global economic downturn and domestic economic instability.

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BASORUDIN. M. R., KUSMARYO. D. H., RACHMAD. S. H., AMANNULLAH. G., HAMID. S. R.  THE VULNERABLE FINANCIAL ISSUE: CAPITAL FLIGHT IN INDONESIA

Determinants of Capital Flight

Table 1 lists the influence of determinants of capital flight, including macroeconomic factors and non-macroeconomic factors. Generally, the exchange rate is statistically significant among the three methods. This result is different from previous kinds of research (Adetiloye, 2012). In that research, when the exchange rate decreases, Nigeria would have 60.22 units of capital flight (WBR) reduction.

Therefore, from this result, we recommend that Indonesian capital no longer portray the exchange rate as a reason for fleeing foreign capital. After the 2008 economic crisis, the IDR appreciated between 2009 and 2011. This situation shows that Indonesia’s stabilization policy is firm in this crisis. As a result, only a few capitals have fled their assets abroad.

In addition, apart from the use of the CM method, the budget deficit (Rdef) does not statistically significantly affect capital flight. However, signs of budget deficits indicate a positive correlation with capital flight. This result is related to previous kinds of research (Han et al., 2012; Mccaslin, 2013). The increase in the budget deficit will increase capital flight from Hong Kong and European countries. This is caused by many price increases or corruption, and will increase investor risk aversion and deleveraging.

As a result, there has been a large amount of capital flight.

Another Other research supports the revolving door relationship between capital flight and debt.

More notably, the research found that, due to capital flight, countries with weak institutions are more likely to accumulate debt, which in turn generates financing needs (Cerra, Rishi, & Saxena, 2008).

In 33 sub-Saharan African countries from 1970 to 2004, for every dollar of external loans provided to Africa during that period, approximately 60 cents were lost in the same year as capital flight. This finding indicates the existence of an extensive debt. Encouraging capital flight (Ndikumana and Boyce, 2011 ).

Therefore, fiscal policy plays an important role in defining the optimal relationship between expenditure and revenue in the budget. The government should use long-term financing if there is a deficit. In addition, public debt management is one of the main goals of every country, and an economy that lacks an appropriate strategy will face serious problems (Kalaš*, Andrašić, & Pjanić, 2016).

Table 1: Comparison of Capital Flight Measurements WBR

TM

CM

(1)

(2)

(1)

(2)

(1)

(2)

Rdef

2.9759 (1.3801)

2.3459 (1.0396)

2.4294 (0.7813)

-0.1174 (-0.0919)

0.0047* (2.0919)

0.0013 (1.3019)

Eco

-0.7581 (-1.1671)

-0.6206 (-0.9279)

0.0231 (0.0246)

0.1813 (0.4785)

-0.0007 (-1.1441)

-0.0002 (-0.8297)

Inf

1.0326 (1.1385)

0.6780 (0.6895)

-1.6059 (-1.2278)

-0.9322 (-1.6733)

0.0008 (0.8658)

0.0003 (0.6373)

ER

-1.9781* (-3.3276)

-1.5369* (-2.3168)

2.1709* (2.5325)

0.6877** (1.8297)

-0.0002 (-0.3669)

-0.0006* (-2.0591)

0.2643 (0.2075)

-2.2214* (-3.0778)

0.0001 (0.2415)

Open

-487.3208 (-0.5889)

5983.4550* (12.7623)

3.4279* (9.5717)

DR

-8.3476** (-1.8086)

17.8978* (6.8444)

-0.0021 (-1.0738)

IR Diff

F-Stat

0.0041

0.0111

0.1693

0.0000

0.1669

0.0000

Adj R

0.3180

0.3327

0.0818

0.8704

0.0829

0.8459

2

Notes: the estimated coefficients are statistically significant:* at 5% level, ** at 10% level.

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BASORUDIN. M. R., KUSMARYO. D. H., RACHMAD. S. H., AMANNULLAH. G., HAMID. S. R.  THE VULNERABLE FINANCIAL ISSUE: CAPITAL FLIGHT IN INDONESIA

Economic growth will not have an impact or influence on capital flight. However, the signs of the coefficients are the same as in some previous kinds of research. If Indonesia’s economic growth increases by one percentage point, it will reduce capital outflows to USD 620 million (WBR). This result also conforms to the Solow-Swan theory. In this theory, capital accumulation becomes an important determinant of economic growth (Mankiw, 2016). If there is a lack of capital, capital accumulation will decrease and Indonesia's economy will not be able to reach a stable state. Finally, Indonesia’s economy becomes unstable, which can accelerate capital flight.

Then, foreign exchange reserves also play an important role in reducing capital flight. In the central bank’s loss function, the greater the relative weight of the foreign exchange reserve target, the greater the range of attractiveness of stable equilibrium. In a stable equilibrium, the economy is less susceptible to capital flight (Kato, Proaño, and Semmler, 2018).

Since 2014, Indonesia's economic growth has stagnated at 5 per cent. It is evident that there are still many investors who get a lot of profit in Indonesia, but they do not reinvest their profit. Lewis explained that this phenomenon is caused by the fact that there is no reinvestment. The reinvestment in Indonesia is less profitable because the investors have to repay tax like a new investment. Besides, there are many overlapping regulations. Lewis's theory that this problem will cause capital flight from Indonesia. Finally, Indonesia’s economy is quite difficult to achieve significant growth.

The results of this research are also related to the research of Gouder & Nouira (2014) and Ndikumana et al. (2014). Both researchers found that every 1 percentage point increase in economic growth in nonWestern countries will reduce capital flight by 0.004 units, while 39 countries in Africa will decrease by 280 million US dollars.

At the same time, compared with other measurements, the TM method gives different results. The economic growth coefficient is positive in the TM method, but its coefficient is not statistically significant.

This means that the calculation takes into account import and export factors, because economic growth also includes import and export value. Except for economic growth, the inflation rate is not statistically significant in any form of measurement (through capital flight). Except for the TM method, the sign of the inflation coefficient is positive.

Except for economic growth, the inflation rate is not statistically significant in any form of measurement, through capital flight. The coefficient of the inflation rate is related to the research hypothesis and previous research. Except for the TM method, it has positive signs. In the TM method, its sign is negative. This result is different because, economically speaking, inflation is not closely related to import and export activities. These activities are closely related to the exchange rate.

According to Table 1, a 1% increase in the inflation rate will increase capital flight by US$1.04 billion and US$680 million (WBR). The Indonesian government has been controlling prices, especially basic demand prices, so Indonesia’s inflation rate is relatively low. Since the economic crisis in 1998 and 2008, the government has been vigorously stabilizing prices. When the economic crisis happened in 2008, the rate of inflation in Indonesia had reached 11.06%. Through macro-prudential policies to stabilize prices, the rate of inflation fell by 2.78%. In addition, the rate of inflation in the past three years has reached the target (Bank Indonesia, 2018). This situation shows that Indonesia's price control is good enough. Therefore, investors or capitalists no longer believe that inflation is the main reason for their flight abroad.

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This result can also cope with previous research and research hypotheses. Almost all previous kinds of research found that the inflation rate does not influence capital flight (Ndikumana et al., 2014; Gouider & Nouira, 2014). Then, a 1 percentage point increase in the inflation rate will increase capital flight by 0.003 units in non-Western countries, (Gouider & Nouira, 2014). At the same time, in PIGS countries, the inflation rate increased by 1 percentage point, and capital flight would increase to USD 13.75 billion (Mccaslin, 2013).

Initially, researchers speculated that macroeconomic factors were the biggest factor affecting capital flight from Indonesia. However, according to Table 1, in these types of measures, capital flight through Indonesia, these factors are not statistically significant. In almost all measures, the exchange rate is the only important macroeconomic factor for capital flight. The budget deficit is only significant for the CM method.

After adding non-macroeconomic factors, certain non-macroeconomic factors have statistical significance in various measures of capital flight. However, macroeconomic factors still do not affect capital flight. We believe that non-macroeconomic factors are the biggest factor influencing capital flight from Indonesia.

Then, as can be seen from Table 1, inflation is not statistically significant. Many previous kinds of research have shown that inflation is one of the most influential factors for capital flight. In Indonesia, the inflation rate for ten years is very low. The government has also stabilized the normal level of consumer prices.

After the 2008 economic crisis, the inflation rate (YoY) dropped sharply from 11.06% to 2.78%, and the inflation target was also between 4.5% ± 1% (Bank Indonesia, 2018). Next, when the inflation rate reached the highest value of 4.35% (2013: third quarter). This shows that the inflation rate does not lead to capital flight.

Fictitious sovereign ratings similar to non-macroeconomic factors are also considered to be one of the determinants for capital flight. According to the results, sovereign ratings (virtual ratings) will not take effect through capital flight. Through capital flight, virtual ratings (DR) are statistically significant.

It can reduce capital flight by USD 8.35 billion (WBR).

In other research, when the sovereign rating of Indonesia reaches investment level (D = 1), it may reduce the capital flight to 5.07 billion U.S. dollars. From this result, it can be seen that the influence of the sovereign level is more effective in reducing capital flight than other determinants (Basorudin, Kusmaryo and Rachmad, 2020).

This result is consistent with the theory of Lucas's paradox. In this paradigm, when sovereign risk is reduced, capital will flow to Indonesia. Since 2012, the sovereign rating has been upgraded from the speculative-grade level (Ba1) to the investment-grade level (Baa3). The example of Lucas Paradox indicates that the political system is the main factor influencing the economy.

In Indonesia, the political risk is high enough. There are still many worst cases such as corruption, crimes, instability, bad supervision, and others. These problems can lead the capital flow out from Indonesia rapidly. Although Indonesia is an emerging market country, the status of this political institution seems to have improved since 2012. The investment environment is at investment grade (DR = 1). Therefore, there is some capital flight.

Through WBR, the coefficient of determination (R-squared) in this research is below 0.4. If non-macroeconomic factors are used as an explanatory variable, R-squared will increase sharply, and in various capital flight methods, R-squared will reach 0.8. Therefore, non-macroeconomic factors are factors that have recently had a greater impact on capital flight than macroeconomic factors.

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On the other side, the R-Squared of TM and CM are similar where, without non-macroeconomic determinants, their R-Squared is below 0.1. It means that the non-macroeconomics determinant may be one of the most influential factors of capital flight in Indonesia. Some previous kinds of research also yielded the same result with relatively small determinant coefficients (Wujung & Mbella, 2016; Mccaslin, 2013; Shalizi, 2015; Gouider & Nouira, 2014; Baek & Yang, 2010; Ndikumana et al., 2014).

This relatively small coefficient of certainty does not mean that the factors used in this research are not suitable for measuring capital flight. This is because the research ignores other factors widely used in previous kinds of research, such as foreign debt and foreign direct investment (FDI). These two factors have exact statistical significance for capital flight, and can increase the coefficient of determination.

This is because they are used to calculate the components of capital flight through the residual method (identity). If these variables are included in the formula, the result will be unreliable.

In addition, in recent kinds of research, the other non-macroeconomic variables, such as political turmoil or system quality, economic risk and corruption, also have a significant impact on capital flight (Ndikumana et al., 2014; Geda & Yimer, 2016; Baek & Yang, 2010). At the same time, these factors are difficult to measure in Indonesia.

In another region, such as in Africa, capital flight is not always explained by capital flows, and vice versa. This is due to the influence of geography, economy and currency regions (Wujung & Mbella, 2016). The problem of capital flight can be solved by adopting a variety of policies, such as removing the control of overlapping regulations, simplifying the business process of the entire region (EoDB), equalizing the tax rate on reinvestment to a maximum of zero, and real-time monitoring of business processes.

In the 37 African countries from 1996 to 2020, corruption control and institutional governance have had a negative impact on capital flight, while the impact of the rule of law is not significant. In summary, corruption control is the most effective governance weapon against capital flight (Asongu & Nwachukwu, 2017).

On the other hand, in the seven countries of the Commonwealth of Independent States (1995-2005), the liberalization of the trade and financial sectors accelerated capital flight by making capital easier to transfer abroad. In the short-term, strengthening supervision rather than liberalization of the external sector seems more likely to combat capital flight (Brada, Kutan, and Vukšić, 2011). However, financial liberalization policies by themselves may not help reduce the capital flight of 21 emerging market economies from 1980 to 2004 (Yalta & Yalta, 2012).

DISCUSSION, CONCLUSION, AND RECOMMENDATIONS: Discussion Due to non-macroeconomic factors, such as political issues, corruption and poor regulation, Indonesia’s most influential capital flight problem exists. So it can explain why the business climate in Indonesia is still not good, especially the regulations on starting a business, enforcing the contracts and paying taxes. There are a lot of plans to enhance the business climate for reducing the capital flight in the short-term and long-term .

Fiji is a small developing country in the Asia-Pacific region. The government of Fiji needs to formulate policies that focus on the long-term security and a stable business and political environment.

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Some of these policies may include making the domestic business and investment environment more attractive, reforming preferential tax policies for foreign investment, retaining qualified and skilled talent, eliminating institutional deficiencies in the banking system, and effectively implementing banking and customs regulations related to the transfer of financial capital (Gani, 2016).

In the short term, the creative economy is a good opportunity for Indonesia to reduce its capital flight. Indonesia is one of the largest creative economies in the world. In addition, Indonesia is also developing a digital economy for its economic future. Thomas T. Lembong, head of the Investment Coordination Committee, said that the digital economy is a sector that saves the pace of international investment . Research by Google and Temasek predicts that, by 2025, Indonesia’s digital economy will reach USD 130 billion U.S. dollars, and by 2019 it will reach USD 40 billion U.S. dollars, an average annual growth rate of 49%.

In some African countries, both from an absolute and conditional perspective, countries with lower capital flight rates are catching up with countries with higher interest rates. Consistent with the intuition that stimulated the analysis of policy coordination, two inferences can be drawn: (i) Convergence means that it is feasible to adopt a common policy to combat capital flight, and (ii) Completely within a specified time frame ( 100%) convergence reflects the implementation (or coordination) of feasible policies without distinguishing nationality or region (Asongu, Uduji and Okolo-Obasi, 2020). Domestic credit expansion and financial liberalization in some European countries are reducing capital. Some solutions for the exodus . Domestic credit has become an important source of financing for capital flight.

Paradoxically, financial liberalization promotes, rather than reduces, capital flight by reducing costs and increasing funds that can be transferred abroad (Brada, Kutan, and Vukšić, 2013). In some countries in Latin America, governments require various implementations. Strategies, from the establishment of effective judicial and political institutions to the control of macroeconomic factors to promote growth (Dachraoui, Smida and Sebri, 2020 ).

In addition, many policymakers seem to prefer domestic alternatives to cross-border mergers. We constructed a model in which cross-border mergers reduce the wages set by labour unions, domestic mergers have non-labour cost synergies, and policy evaluators care more about workers than capital owners. Restrictive cross-border mergers and acquisitions policies may be counterproductive, because they do not necessarily lead to domestic mergers and acquisitions, but capital flight (Lommerud, Meland, and Straume, 2011).

Conclusion and Recommendation Regarding the theory of macroeconomics and empirical data evaluation, this research is a novelty of controversial economic theory . This research shows that macroeconomic variables are not the main cause of capital flight in Indonesia, but non-macroeconomic variables such as country risk, corruption, tax evasion and tax avoidance have become the main important factors in how the dynamics of capital flight in Indonesia are driven.

Statistics show that, when the inflation rate rises by one percentage point, variables such as economic growth, inflation rate and sovereign rating do not have a significant impact on capital flight. Capital flight will increase by USD$1.04 billion and 6.7% (WBR) respectively. The fictitious rating can save USD 8.34 billion U.S. dollars.

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The most important findings and future actions of policies can be put forward based on this research as follows: capital flight policy actions should consider both macroeconomic and non-macroeconomics, and at the same time be related to the specific problems of capital flight, in order to find the best contemporary solutions. The rResearchers also believe that the problem of capital flight can be solved by adopting a variety of policies, such as removing the control of overlapping regulations, simplifying the business process (EoDB) of the entire region, equalizing the tax rate of reinvestment to zero, and monitoring business processes in real-time.

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OSETLJIVO FINANSIJSKO PITANJE: ODLIV KAPITALA IZ INDONEZIJE

Rezime: Indonezija je zemlja u razvoju sa velikom potražnjom za kapitalom kako iz domaćih, tako i iz međunarodnih izvora. Međutim, međunarodni tokovi kapitala su najpotrebniji. Za zemlje u razvoju, posebno za Indoneziju, odliv kapitala predstavlja ozbiljan finansijski problem. Ovo istraživanje ima za cilj analizu odliva kapitala iz Indonezije i procenu uticaja makroekonomskih i nemakroekonomskih odrednica. Makroekonomski faktori uključuju budžetski deficit, ekonomski rast, stopu inflacije i kurs. Nemakroekonomski faktori su stepen otvorenosti privrede, razlike u kamatnim stopama i veštački rejting. Podaci dolaze iz Indonezijske banke, Organizacije za ekonomsku saradnju i razvoj, Mudisa i BPS-Statističkog zavoda Indonezije. Ovim istraživanjem obuhvaćen je period od 2010. do 2018. Podaci su u skladu sa najnovijim procedurama šestog izdanja Priručnika za platni bilans (BPM 6). U ovom istraživanju, metode za merenje odliva kapitala su rezidualna metoda Svetske banke, metoda pogrešnog fakturisanja i kombinovana metoda. Ovo istraživanje otkriva da, u poređenju sa drugim ekonomijama, faktori koji nisu makroekonomske prirode su najuticajnija odrednica odliva kapitala iz Indonezije.

Ključne reči: odliv kapitala, kombinovani metod, mikroekonomija, Indonezija.

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EJAE 2021, 18(1): 106 - 125 ISSN 2406-2588 UDK: 159.9.072-057.875(497.11) 005.342-057.875(497.11) DOI: 10.5937/EJAE18-27707 Original paper/Originalni naučni rad

THE ENTERPRISE POTENTIAL, INDIVIDUAL ENTREPRENEURIAL ORIENTATION AND ENTREPRENEURIAL INTENTIONS OF STUDENTS IN SERBIA Jelena Rajković*, Jasmina Poštin, Marko Konjikušić, Aleksandra Jagodić Rusić, Hadži Strahinja Stojković, Milan Nikolić University ’’Union Nikola Tesla’’, Faculty of Engineering, Belgrade, Serbia

Abstract: In this paper are given the findings of the research of the effects of five variables on the dimensions of the enterprise potential, individual entrepreneurial orientation, the Theory of Planned Behaviour (TPB) and dimension entrepreneurial intention, by students. The effects of the following variables are observed: Gender, One of my parents has a private business, The year in which the student is studying, Student success in learning and studying and The financial opportunities to start a new business. The respondents are studying at seven faculties in Serbia. The sample included 488 respondents. The most influential variable on the dimension entrepreneurial intentions is the financial opportunities to start a new business. Also, men have more pronounced entrepreneurial intentions. Success in studies positively influences entrepreneurial potentials and proactivity, while the possession of finance positively influences entrepreneurial intentions and risk readiness. In cases of successful studies and the possession of finance, women are more motivated, more determined, and have more pronounced entrepreneurial intentions (especially in the case of having financial resources).

Although men may show a more preference to become entrepreneurs, women approach entrepreneurship more realistically and decisively.

Article info: Received: July 29, 2020 Correction: September 7, 2020 Accepted: October 19, 2020

Keywords: Enterprise potential, Individual entrepreneurial orientation, Entrepreneurial intentions, Students, Serbia.

JEL Classification: L26, D23

INTRODUCTION Entrepreneurship represents one of the leading aspects of socio-economic development (Coulibaly, Erbao, & Mekongcho, 2018), and plays a key role in economic growth (Mahfud, Triyonoa, Sudiraa, & Mulyani, 2020). More precisely, entrepreneurship contributes to job creation, increases productivity and competitiveness, encourages companies to innovate and act effectively (Mortan, Ripoll, Carvalho, & Bernal, 2014), creates wealth and reduces unemployment (Paul, & Shrivatava, 2016).

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*E-mail: j.rajkovic24@gmail.com

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RAJKOVIĆ. J., POŠTIN. J., KONJIKUŠIĆ. M., RUSIĆ. J. A., STOJKOVIĆ. H. S., NIKOLIĆ. M.  THE ENTERPRISE POTENTIAL, INDIVIDUAL ENTREPRENEURIAL ORIENTATION AND ENTREPRENEURIAL INTENTIONS OF STUDENTS IN SERBIA

Because of all this, it is not surprising that state institutions in many countries, at the highest levels, increasingly view entrepreneurship as an instrument for achieving economic growth and technological progress (Fellnhofer, & Kraus, 2015). However, entrepreneurship is popular, not only from the perspective of state-level institutions, but also from the perspective of students as potential entrepreneurs, as well as academicians, as researchers in this field (Mwasalwiba, 2010).

Much of the research in the field of entrepreneurship is related to entrepreneurial intentions, their recognition, understanding and possibility to manage. Entrepreneurial intentions are important and deserve attention because, as proven, they confirm entrepreneurial behaviour (Koe, 2016; Sahinidis, Stavroulakis, Kossieri, & Varelas, 2019). Delanoë‐Gueguen and Fayolle (2018) point to the importance of the dynamics that connect entrepreneurial intentions and entrepreneurial actions. According to (Delanoë‐Gueguen, & Liñán, 2018), the greatest and lasting impact in this process (transition of entrepreneurial intentions in entrepreneurial actions) is the pursuit of providing the job security.

Entrepreneurial intentions represent a theoretical construct, which is very often used in order to explain the transition of an individual from ordinary thinking to concrete entrepreneurial action. Extensive literature in this area indicates that the importance of developing and nurturing initial entrepreneurial desires has been recognized, in order to enable the translation of these desires into immediate economic and social values (Donaldson, 2019). Similarly, Nowiński and Haddoud (2019) note that discovering everything that makes people engage in entrepreneurial activities occupies the special attention of researchers in the field of entrepreneurship and management, for over the last thirty years. Understanding these mechanisms is of great importance in forming appropriate programs to encourage and support entrepreneurial ventures. For example, research among students in Pakistan (Samo, & Huda, 2019) has shown that support from governing structures and academic community has a strong, positive impact on entrepreneurial intentions, while industry impact is positive but not significant.

There are numerous influences on entrepreneurial intentions, as well as research examining these influences. What is important for this paper is that there are numerous research papers that deal with the influences of the gender of respondents on entrepreneurial intentions, for example (Santos et al., 2016; Huezo-Ponce, & Saiz-Álvarez, 2020; Vamvaka, Stoforos, Palaskas, & Botsaris, 2020; Nguyen, 2018), furthermore there are research papers that deal with influences of family background on entrepreneurial intentions (Jena, 2020; Herman, 2019; Palmer, Fasbender, Kraus, Birkner, & Kailer, 2019; Sahinidis, Stavroulakis, Kossieri, & Varelas, 2019; Hatak, Harms, & Fink, 2015); research papers that deal with the influences of the age of the respondents on entrepreneurial intentions (Minola et al., 2016; Tsai et al., 2016; Nguyen, 2018) and research papers that deal with the influences of having the finances to start their own business on entrepreneurial intentions (Kim, Longest, & Aldrich, 2013; Rodriguez, Tuggle, & Hackett, 2009; Shinnar, & Young, 2008; Sieger, & Minola, 2017). The general impression is that similar research in Serbia has not yet been done to a sufficient extent.

The aim of this paper was to explore the impact of different variables on enterprise potential dimensions, individual entrepreneurial orientation dimensions, the Theory of Planned Behaviour (TPB) dimensions and entrepreneurial intention dimension (entrepreneurial dimensions). The research was carried out on students in Serbia. The effect of the following five variables is observed: Gender, One of my parents has a private business, The year in which the student is studying, Student success in learning and studying and The financial opportunities to start a new business. It should be noted that the students are similar in age, and therefore, in this part the focus is the year in which the student is studying, in order to see any changes in the initial and final years of study. It should be noted here that, in previous research, the year of study has not been sufficiently considered as an influential factor on entrepreneurial intentions.

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RAJKOVIĆ. J., POŠTIN. J., KONJIKUŠIĆ. M., RUSIĆ. J. A., STOJKOVIĆ. H. S., NIKOLIĆ. M.  THE ENTERPRISE POTENTIAL, INDIVIDUAL ENTREPRENEURIAL ORIENTATION AND ENTREPRENEURIAL INTENTIONS OF STUDENTS IN SERBIA

The possession of finance for opening one’s own company variable is very important for the transition conditions in Serbia, where young people face serious financial constraints for starting their own business. Finally, the success in studies variable is, also, particularly interesting because it has not been widely considered so far in the context of entrepreneurial intentions.

The paper has a pronounced theoretical and practical contribution. The theoretical contribution and importance of the paper stem from the fact that, in many researches of various influences on entrepreneurial intentions, the influences of two variables have not been sufficiently observed so far: The year in which the student is studying and, especially, Student success in learning and studying. The practical contribution and importance of the paper is to consider the impact of the given five control variables on the entrepreneurial intentions of students in Serbia. Based on this acknowledgment, it will be possible to define certain, specific proposals, which may be useful to the relevant state institutions, which deal with the promotion of entrepreneurship in Serbia.

THEORY Enterprise Potential Enterprise potential presents the latent inclination of an individual to engage in entrepreneurship.

The presence of such potentials does not imply the obligatory presence of a complete determination for a person to engage in entrepreneurship, or that this will happen, but only that such an individual possesses the characteristics and attitudes that favour the launch of an entrepreneurial venture. According to Athayde (2009) enterprise potential determination is a significant and frequently explored topic in the field of entrepreneurship. In this paper, enterprise potential is monitored and measured using the model established by Athayde (2009). This model contains the following constructs: leadership, creativity, achievement and personal control. A large number of references point to the importance of these dimensions for entrepreneurship.

Individual Entrepreneurial Orientation According to Mueller and Thomas (2000), entrepreneurial orientation represents a group of individual characteristics, which have a connection with entrepreneurship. According to these authors, entrepreneurial orientation includes locus of control and innovativeness. This concept of was originally developed by Miller (1983) and basically has three constructs: risk taking, innovativeness and proactiveness. Observed at the individual level, entrepreneurial orientation is analogously transformed into individual entrepreneurial orientation (Robinson, & Stubberud, 2014), which is important in studying the entrepreneurial intentions of individuals. A positive link between individual entrepreneurial orientation and entrepreneurial intentions is generally confirmed (Bolton, & Lane, 2012; Koe, 2016; Muñoz-Bullón, Sánchez-Bueno, & Vos-Saz, 2015; Reijonen, Hirvonen, Nagy, Laukkanen, & Gabrielsson, 2015).

Risk taking is a feature that is more present in entrepreneurs than other people (Brandstätter, 2011; Caliendo, Fossen, & Kritikos, 2014; Karabulut, 2016). Innovativeness is also more prominent in entrepreneurs than other people (Çolakoğlu & Gözükara, 2016), and also has an impact on entrepreneurial intentions (Mueller, & Thomas, 2000; Padilla-Meléndez et al., 2014). Similarly, proactivity, as a personal trait, has a positive link with entrepreneurial intentions (Usaci, 2015; Paul, & Shrivatava, 2016).

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RAJKOVIĆ. J., POŠTIN. J., KONJIKUŠIĆ. M., RUSIĆ. J. A., STOJKOVIĆ. H. S., NIKOLIĆ. M.  THE ENTERPRISE POTENTIAL, INDIVIDUAL ENTREPRENEURIAL ORIENTATION AND ENTREPRENEURIAL INTENTIONS OF STUDENTS IN SERBIA

The Theory of Planned Behavior and Entrepreneurial Intention

According to Ajzen (1991) and Liñán and Chen (2009) the Theory of Planned Behavior (TPB) is based on three motivation factors that affect behaviour: a) Attitude towards entrepreneurship (Personal Attitude) represents the personal opinion about how nice, good and useful it is to have your own company. b) Subjective norm represents an assessment of the extent to which there is or is not social pressure from the environment to undertake a business venture. c) Perceived behavioural control represents assessment of the degree of ability to start an entrepreneurial business, and later to run it successfully.

Within the extended Theory of Planned Behaviour (TPB), entrepreneurial intentions are seen as the effort that an individual will invest in performing an entrepreneurial activity (Paul, & Shrivatava, 2016).

In doing so, such attitudes towards entrepreneurship predict entrepreneurial intentions, and in turn entrepreneurial intentions predict planned entrepreneurial behaviour (Ajzen, 1991). TPB dimensions have a significant impact on entrepreneurial intentions (Roy, Akhtar, & Das, 2017; Zhang, Wang, & Owen, 2015; Moriano, Gorgievski, Laguna, Stephan, & Zarafshani, 2012).

According to Fayolle and Liñán (2014) entrepreneurial intention is a characteristic that leads a person to open up his own company, start an entrepreneurial career and become self-employed. The study of entrepreneurial intentions provides a significant opportunity to understand and predict the ability for entrepreneurship (Rauch, & Hulsink, 2015; Karimi, Biemans, Lans, Chizari, & Mulder, 2016).

Variables which Influence Entrepreneurial Intentions There are numerous research studies about the effects of various variables on the emergence of entrepreneurial intentions and entrepreneurship in general. Among other things, the effects of variables such as gender, the existence of an entrepreneurial tradition in the family, age and the possession of finance for starting a business are examined.

Gender One of the most frequently studied topics in entrepreneurship is the impact of gender on the startup of an entrepreneurial venture and its success. According to the Global Entrepreneurship Monitor 2013, in 2012 more than 187 million out of 400 million entrepreneurs were women (Poggesi et al., 2016). The authors conclude that female entrepreneurship is a key component of the business sector worldwide. One group of studies deals with the comparison of women and men who are already entrepreneurs. According to (Jayawarna et al., 2015), there are many similarities between male and female entrepreneurs, which disperse the assumptions and prejudices about the shortcomings and weaknesses of women in the role of entrepreneurs.

The second group of studies deals with the examination of the entrepreneurial characteristics and intentions in both women and men. According to Santos et al. (2016), men and women share a similarity in entrepreneurial intentions, although they are somewhat more pronounced in men. In society, men are more encouraged to undertake entrepreneurial ventures, so women feel that entrepreneurship is not an acceptable career choice for them. Similarly, male students in Norway and Turkey show a much higher degree of entrepreneurial intentions than female students (Shneor et al., 2013). According to Diaz-Garcia and Jiménez-Moreno (2010) men put more thought into creating a company than determination to establish one.

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RAJKOVIĆ. J., POŠTIN. J., KONJIKUŠIĆ. M., RUSIĆ. J. A., STOJKOVIĆ. H. S., NIKOLIĆ. M.  THE ENTERPRISE POTENTIAL, INDIVIDUAL ENTREPRENEURIAL ORIENTATION AND ENTREPRENEURIAL INTENTIONS OF STUDENTS IN SERBIA

In doing so, there are no significant differences in entrepreneurial intentions for women and men.

Recent research from different parts of the world also show similar tendencies: in Mexico men and women have similar levels of entrepreneurial intentions (Huezo-Poncen, & Saiz-Álvarez, 2020), while in Greece (Vamvaka, Stoforos, Palaskas, & Botsaris, 2020) and Vietnam (Nguyen, 2018), men still have slightly higher entrepreneurial intentions.

Family Background The existence of a family background is also a significant topic in research into the impact on entrepreneurial intentions and entrepreneurship in general. These studies mostly confirm the positive relationship between these variables, for example (Jena, 2020; Herman, 2019; Palmer, Fasbender, Kraus, Birkner, & Kailer, 2019; Sahinidis, Stavroulakis, Kossieri, & Varelas, 2019). According to Kotlar and De Massis (2013) in family firms there are various goals, which is a consequence of the overlapping and interaction between family, ownership and business aspects. If a young person grows up in such an environment, one can assume that entrepreneurship and work in their own company is a natural phenomenon, and the entrepreneurial environment is thus a natural environment in which it will be easier to develop entrepreneurial intentions.

Similarly, a supportive family background increases the likelihood of someone starting an entrepreneurial venture (Jayawarna et al., 2014). Jain and Wajid Ali (2012) point out that those entrepreneurs who have a family business background show significantly more prominent features such as: achievement, innovativeness, locus of control and risk-taking. Also, according to Altinay et al. (2012) an existence of an entrepreneur in the family has an impact on the intentions to start a business venture.

Age A somewhat smaller number of studies are directed on examining the impact of age on entrepreneurial intentions. According to Minola et al. (2016), self-employment motivation depends on age. In terms of both desirability and feasibility beliefs, motivation to open your own business reaches its peak in young years, experiencing a pronounced reduction towards late years. Similarly, Tsai et al. (2016) found that years of age have a negative impact on assessment of their own capabilities and entrepreneurial intention. A somewhat different result was obtained in the research (Jain, & Wajid Ali, 2012), which states that younger entrepreneurs are more proactive than others, while entrepreneurs in their middle age have a stronger locus of control and high willingness to take risks than others. According to Nguyen (2018), age does not affect entrepreneurial intentions.

Financial Opportunities The financial opportunities to start a new business can often affect entrepreneurial intentions. However, not much research has been dedicated to this topic. The reason for this may be that these surveys are mainly carried out in developed countries where the possession of money is not such a limiting factor due to the higher standard of living of potential entrepreneurs, and the better regulated financial incentives of the society. Perceived capability positively affects entrepreneurship intention (Tsai et al., 2016). Perceived capability Estimated capability may be partly understood as the possession of finance for launching entrepreneurial ventures, so it can be indirectly concluded that having finances affect entrepreneurial intentions. Pfeifer et al. (2016) showed that family wealth and family business exposure have a positive impact on the entrepreneurial intentions of Croatian students.

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RAJKOVIĆ. J., POŠTIN. J., KONJIKUŠIĆ. M., RUSIĆ. J. A., STOJKOVIĆ. H. S., NIKOLIĆ. M.  THE ENTERPRISE POTENTIAL, INDIVIDUAL ENTREPRENEURIAL ORIENTATION AND ENTREPRENEURIAL INTENTIONS OF STUDENTS IN SERBIA

The disposal of financial capital, in the general case, has a positive relationship with self-employment (Kim, Longest, & Aldrich, 2013; Rodriguez, Tuggle, & Hackett, 2009; Shinnar, & Young, 2008). However, according to Sieger and Minola (2017), family financial support for an entrepreneurial venture can create some pressure on a potential entrepreneur and diminish his or her actual entrepreneurial intentions.

On the basis of the introductory and theoretical considerations, the six research questions were asked: RQ1: Is there a statistically significant influence of the GEN – gender variable on the observed entrepreneurial dimensions?

RQ2: Is there a statistically significant impact of PAR – One of my parents has a private business variable on the observed entrepreneurial dimensions?

RQ3: Is there a statistically significant impact of YEA – The year in which the student is studying variable on the observed entrepreneurial dimensions?

RQ4: Is there a statistically significant impact of SUC – Student success in learning and studying variable on the observed entrepreneurial dimensions?

RQ5: Is there a statistically significant impact of FIN – The financial opportunities to start a new business variable on the observed entrepreneurial dimensions?

RQ6: Is there a moderating impact of GEN – gender variable in the relation between the influence of the variables of YEA – The year in which the student is studying, SUC – Student success in learning and studying and FIN – The financial opportunities to start a new business on the observed entrepreneurial dimensions?

METHODOLOGY Survey Instruments (Measures) Enterprise potential. The measurement of entrepreneurial potentials was carried out using a questionnaire based on the Attitude Toward Enterprise (ATE) test (Athayde, 2009). The questionnaire comprises 18 items (4 dimensions). The respondents evaluate the items on a seven-point Likert scale.

Individual entrepreneurial orientation. The measurement of individual entrepreneurial orientation was carried out using the Individual Entrepreneurial Orientation (IEO) questionnaire (Bolton, & Lane 2012). The questionnaire comprises 10 items (3 dimensions). The respondents evaluate the items on a seven-point Likert scale.

The Theory of Planned Behaviour and entrepreneurial intention. The measurement of Theory of Planned Behaviour and entrepreneurial intentions dimensions was realized using the Entrepreneurial Intention Questionnaire (EIQ) (Liñán, & Chen, 2009). The questionnaire comprises 20 items (4 dimensions).

The respondents evaluate the items on a seven-point Likert scale.

Participants The respondents were students in Serbia. This survey includes students from seven faculties of technical and economic orientation (University of Belgrade and University of Novi Sad). The sample consists of undergraduate and master's students, from the first to the fifth year of study, whereas this distribution is even. Students included in the research have no previous entrepreneurial experience.

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RAJKOVIĆ. J., POŠTIN. J., KONJIKUŠIĆ. M., RUSIĆ. J. A., STOJKOVIĆ. H. S., NIKOLIĆ. M.  THE ENTERPRISE POTENTIAL, INDIVIDUAL ENTREPRENEURIAL ORIENTATION AND ENTREPRENEURIAL INTENTIONS OF STUDENTS IN SERBIA

The survey was conducted through an anonymous questionnaire. The questionnaires were completed either during or after classes. A total of 700 questionnaires were distributed to students, 532 of which were returned.

A number of returned questionnaires were not completely filled in, so these questionnaires were removed. There were 44 such questionnaires. Thus, 488 questionnaires remained for statistical analysis and this number is the final scope of the research sample. The percentage of successfully returned and completed questionnaires by respondents is 69,7%. The sample of 488 questionnaires obtained this way can be considered representative, both in qualitative and quantitative terms.

According to the observed variables, the sample has these characteristics: ◆ There were 337 (69.1%) female students and 151 (30.9%) male students in the sample. ◆ The age of the respondents ranges from 18 to 32, with the average age of the respondents being 21.38 (standard deviation 1,962). ◆ The research included undergraduate and master’s students, from the first to the fifth year of study. There were 157 first-year students (32.17%), 88 second-year students (18.03%), 122 thirdyear students (25.00%), 70 fourth-year students (14.34%) and 51 fifth-year students (10.45%). ◆ When considering the question (statement): I am an excellent and very successful student, 5 (1.02%) students said they did not agree at all, 25 (5.12%) students said they did not agree, 155 (31, 76%) of the students expressed a neutral attitude, 227 (46.52%) of the students said to agree and 76 (15.57%) of the students stated that they completely agreed. ◆ When considering the question (claim): One of my parents has their own private business, 106 (21.7%) students said yes, while 382 (78.3%) students said that their parents do not have a private business. ◆ When considering the question (statement): I have the necessary finances to open my own company, 213 (43.65%) students said they did not agree at all, 102 (20.90%) students said they did not agree, 81 (16, 60%) student expressed a neutral attitude, 76 (15.57%) students agreed and 16 (3.28%) students completely agreed with the statement.

RESEARCH RESULTS For statistical data processing were used: t-tests, correlation and regression analysis and hierarchical regression analysis. The impact of the variables GEN - the respondents’ gender and PAR - one of my parents has a private business on the observed entrepreneurial dimensions was assessed by means of t-tests because these are categorical variables. The impact of the variables YEA - the year in which the student is studying, SUC - student success in learning and studying and FIN - the financial opportunities to start a new business on the observed entrepreneurial dimensions was tested by correlation and regression analysis because these are continual variables.

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RAJKOVIĆ. J., POŠTIN. J., KONJIKUŠIĆ. M., RUSIĆ. J. A., STOJKOVIĆ. H. S., NIKOLIĆ. M.  THE ENTERPRISE POTENTIAL, INDIVIDUAL ENTREPRENEURIAL ORIENTATION AND ENTREPRENEURIAL INTENTIONS OF STUDENTS IN SERBIA

Descriptive Statistics

The descriptive statistics is shown in Table 1.

Table 1: Descriptive statistics.

Abbr.

N

Min

Max

Mean

Deviation Std.

α

Leadership

LEA

488

1

7

3.88

1.265

.837

Creativity

CRE

488

1

7

5.54

1.158

.792

Achievement

ACH

488

1

7

4.25

1.259

.809

Personal control

PC

488

1

7

4.11

1.205

.702

Risk taking

RT

488

1

7

4.16

1.335

.777

Innovativeness

IN

488

1

7

4.86

1.142

.799

Proactiveness

PR

488

1

7

4.98

1.217

.786

Personal attitude

PA

488

1

7

4.80

1.231

.860

Subjective norm

SN

488

1

7

5.60

1.248

.846

Perceived behavioral control

PBC

488

1

7

4.14

1.189

.894

Entrepreneurial intention

EI

488

1

7

3.78

1.486

.937

Student success in learning and studying

SUC

488

1.00

5.00

3.70

.828

Financial opportunities to start a new business

FIN

488

1.00

5.00

2.13

1.073

Dimensions and items

T-test analysis (examining the impact of variables GEN – Gender and PAR – One of my parents has a private business) Table 2 shows the t-test analysis. This analysis shows whether the respondents of different gender, as well as those whose parents have a private business and those who do not, differ in relation to particular entrepreneurial dimensions. The bold values in Table 2 indicate the dimensions where there is a statistically significant difference in the average grades, relative to a particular categorical variable.

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RAJKOVIĆ. J., POŠTIN. J., KONJIKUŠIĆ. M., RUSIĆ. J. A., STOJKOVIĆ. H. S., NIKOLIĆ. M.  THE ENTERPRISE POTENTIAL, INDIVIDUAL ENTREPRENEURIAL ORIENTATION AND ENTREPRENEURIAL INTENTIONS OF STUDENTS IN SERBIA

Table 2: T-test of the average scores of the observed entrepreneurial dimensions for two categorical variables (GEN - Gender; PAR - One of my parents has a private business).

Abbr.

Mean Total

PAR - One of my parents has a private business

GEN - The respondents’ gender Male

Female

t-test

Sig. (2-tailed)

No

Yes

t-test

Sig. (2-tailed)

LEA

3.88

4.10

3.79

2.709

.007

3.85

3.89

-.307

.759

CRE

5.54

5.28

5.65

-3.309

.001

5.53

5.54

-.093

.926

ACH

4.25

4.26

4.25

.134

.893

4.31

4.24

.507

.07

PC

4.11

4.30

4.02

2.617

.009

4.09

4.11

-.252

.801

RT

4.16

4.27

4.12

1.248

.158

4.20

4.15

.349

.727

IN

4.86

4.82

4.88

-.582

.562

4.86

4.86

.043

.966

PR

4.98

4.83

5.05

-1.950

.005

5.09

4.96

1.015

.311

PA

4.80

4.89

4.76

1.106

.269

4.77

4.81

-.252

.801

SN

5.60

5.38

5.69

-2.466

.014

5.70

5.57

.986

.325

PBC

4.14

4.30

4.07

1.913

.005

4.29

4.10

1.433

.153

EI

3.78

4.09

3.65

3.012

.003

3.75

3.86

-.654

.513

Correlation and regression analysis (examining the impact of variables YEA – The year in which the student is studying, SUC – Student success in learning and studying and FIN – The financial opportunities to start a new business) The correlation coefficients between the continual variables YEA - the year in which the student is studying, SUC - student success in learning and studying, FIN - the financial opportunities to start a new business and the observed entrepreneurial dimensions are presented in Table 3. Pearson’s correlation was used.

Table 3: Correlation coefficients.

YEA - The year in which the student is studying SUC - Student success in learning and studying FIN - Financial opportunities to start a new bus. *p<0.05; **p<0.01.

114

LEA

CRE

ACH

PC

RT

IN

PR

PA

SN

PBC

EI

.022

-.094*

-.019

.047

-.046

-.038

-.048

.024

-.018

-.035

-.077

.142** .137** .199**

.098*

.015

.072

.184**

-.038

.100*

.051

-.033

.012

.141**

.069

.058

.166**

.062

.274**

.302**

.086

-.055

.060

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RAJKOVIĆ. J., POŠTIN. J., KONJIKUŠIĆ. M., RUSIĆ. J. A., STOJKOVIĆ. H. S., NIKOLIĆ. M.  THE ENTERPRISE POTENTIAL, INDIVIDUAL ENTREPRENEURIAL ORIENTATION AND ENTREPRENEURIAL INTENTIONS OF STUDENTS IN SERBIA

The predictive effects of the variables YEA - the year in which the student is studying, SUC - student success in learning and studying, FIN - the financial opportunities to start a new business (independents) on the observed entrepreneurial dimensions (dependents) were determined by Multiple Regression Analysis. This results are given in Table 4. Statistically significant values are given in bold font.

Table 4: Regression analysis.

Independent variables Dependent variables

YEA - The year in which the student is studying

SUC Student success in learning and studying

FIN - Financial opportunities to start a new bus.

R2

F

Sig.

β LEA

.004

.140*

.083

.027

4.486

.004

CRE

-.114*

.153**

-.059

.035

5.817

.001

ACH

-.046

.203**

.056

.045

7.542

.000

PC

.035

.093*

.010

.011

1.789

.148

RT

-.048

.018

.141**

.022

3.686

.012

IN

-.048

.077

.067

.012

1.956

.120

PR

-.073

.192**

.054

.042

7.073

.000

PA

.031

-.046

.167**

.030

5.040

.002

SN

-.031

.103*

.060

.015

2.378

.069

PBC

-.041

.050

.273**

.079

13.769

.000

EI

-.073

-.031

.302**

.098

17.098

.000

Gender as a Moderator The correlation analysis between the variables YEA - the year in which the student is studying, SUC - student success in learning and studying, FIN - the financial opportunities to start a new business and the observed entrepreneurial dimensions, for male and female students is presented in Table 5. In this table, the results confirming the moderating effect of gender are highlighted by bold font. Table 6 gives additional statistics only for couples in which the gender moderating effect was confirmed.

Table 5: Correlation coefficients.

GEN - Gender of the respondents Male

Fem.

LEA

CRE

ACH

PC

RT

IN

PR

PA

SN

PBC

EI

YEA - Year

-.048

-.104

-.033

-.053

-.058

-.046

-.094

.081

-.128

.014

.038

SUC - Success

.113*

.103*

.183*

.069

.086

.209*

.150*

.025

.138

.106

.027

FIN - Finance

-.005

-.066

.048

-.024

-.001

-.010

-.042

-.029

-.011

.162*

.119

YEA - Year

.048

-.095

-.014

.082

-.044

-.037

-.030

.000

.025

-.048

-.114*

SUC - Success

.199**

.110*

.216**

.150**

.003

.002

.178**

-.053

.044

.056

-.017

FIN - Finance

.110*

-.029

.066

.012

.201**

.113*

.115*

.259**

.125*

.310**

.365**

*p<0.05; **p<0.01

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RAJKOVIĆ. J., POŠTIN. J., KONJIKUŠIĆ. M., RUSIĆ. J. A., STOJKOVIĆ. H. S., NIKOLIĆ. M.  THE ENTERPRISE POTENTIAL, INDIVIDUAL ENTREPRENEURIAL ORIENTATION AND ENTREPRENEURIAL INTENTIONS OF STUDENTS IN SERBIA

Table 6: Hierarchical regression analysis with GEN - gender as a moderator.

Independent

Dependent

R square change

F-change

SUC - Student success in learning and studying

IN

.009

.263

PA

.018

.689

PBC

.008

5.448

EI

.017

5.791

RT

.009

.227

FIN - Financial opportunities to start a new business

DISCUSSION Discussion of the Results of the Descriptive Statistics The results of the descriptive statistics (Table 1) show that from the observed entrepreneurial dimensions, the largest average scores are achieved for the dimensions SN - subjective norm and CRE - creativity. The high average score for SN - subjective norm is is in line with the national culture in Serbia (Vukonjanski et al., 2012), which is pronounced collectivist: promotes unity and solidarity, which, in this case, is manifested through increased human support in their surroundings.

The lowest average values were assigned to the EI - entrepreneurial intentions and LEA - leadership dimensions. Most of the other dimensions related to entrepreneurship have values around or above average. However, it is one thing to have certain characteristics, such as innovation and pro-activity and a positive attitude towards entrepreneurship, but it is quite another when it comes to firm entrepreneurial intentions. Simply, it is easier to sympathize with the business of an entrepreneur and have the desire to become an entrepreneur, rather than really having the strong desire and the will to achieve such a venture. Due to the low standard of living in Serbia, especially that of students, the very low average value for item FIN – the financial opportunities to start a new business comes as no surprise.

Discussion of the Results of the t-test (Answering RQ1 and RQ2) The results of the t-test (Table 2) show that the variables GEN - the respondents’ gender have a statistically significant influence on most of the observed entrepreneurial constructs. For men the more powerful dimensions are: LEA - leadership, PC - personal control, PBC - perceived behavioural control and EI - entrepreneurial intentions. The more pronounced dimensions for women are: CRE - creativity, PR - proactiveness and SN - subjective norm. There are no statistically significant differences in the dimensions: ACH - achievement, RT - risk taking, IN - innovativeness and PA - personal attitude.

In this section, it can be concluded that men have more pronounced leadership qualities, as well as a greater desire and need for control, all of which leads to more pronounced entrepreneurial intentions.

The result indicating that entrepreneurial intentions are more pronounced among male students. This is in agreement with most of the existing research, for example (Shneor et al., 2013; Antoncic et al., 2015; Leppel, 2016; Santos et al., 2016; Vamvaka, Stoforos, Palaskas, & Botsaris, 2020; Nguyen, 2018).

On the other hand, women are more creative and proactive, and they also have more support from their environment. Higher proactivity can be explained by being female students more conscientious.

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RAJKOVIĆ. J., POŠTIN. J., KONJIKUŠIĆ. M., RUSIĆ. J. A., STOJKOVIĆ. H. S., NIKOLIĆ. M.  THE ENTERPRISE POTENTIAL, INDIVIDUAL ENTREPRENEURIAL ORIENTATION AND ENTREPRENEURIAL INTENTIONS OF STUDENTS IN SERBIA

Greater support from the environment is probably not the result of more incentives for women to enter into entrepreneurship, but a higher level of confidence in girls in that period of life, and this is reflected in the result of greater proactivity, which indicates a higher degree of responsibility and conscientiousness. Knorr, Garzón and Martinez (2011) obtained a similar result regarding the need for achievement, according to which men and women have equal motivation to start their own business.

According to Table 2, the variable PAR - one of my parents has a private business does not show a statistically significant influence on any of the observed entrepreneurial constructs. This result is significantly different from most of the existing ones, indicating the positive impact of the existence of an entrepreneur in the family on entrepreneurial intentions and preferences for entrepreneurship (Jayawarna et al., 2014; Jain et al., 2012; Altinay et al., 2012; Jena, 2020; Herman, 2019; Palmer, Fasbender, Kraus, Birkner, & Kailer, 2019; Sahinidis, Stavroulakis, Kossieri, & Varelas, 2019). There is a similarity with research (Hatak, Harms, & Fink, 2015; Nguyen, 2018), where it was shown that the entrepreneurial intentions of an individual are not influenced by having or not parents who are entrepreneurs. Such a uniform situation, in this paper, is apparently created because students whose parents do not have a private business also have similar entrepreneurial intentions. A possible explanation is that regardless of whether their parents have a private business, students see entrepreneurship as an opportunity and consider self-employment to be a great way to gain employment and a decent standard of living. It should be borne in mind that unfavourable circumstances (a relatively high unemployment rate and low standard of living) are strongly expressed in Serbia. The second question is to what extent entrepreneurial intentions are present. As shown by the descriptive statistics, they are still below average.

The answer to RQ1: the variable of GEN – gender has a statistically significant impact on the most of the observed entrepreneurial dimensions (LEA - leadership, CRE - creativity, PC - personal control, PR - proactiveness, SN - subjective norm, PBC - perceived behavioural control and EI - entrepreneurial intentions).

The answer to RQ2: the variable of PAR – One of my parents has a private business has virtually no statistically significant influence on the observed entrepreneurial dimensions.

Discussion of the Correlation and Regression Analysis Results (Answering RQ3, RQ4 and RQ5) According to the correlation analysis (Table 3), the variable YEA - the year in which the student is studying has no statistically significant correlation, except with the CRE - creativity dimension. This correlation is negative. Younger students may overestimate their creativity and the importance of creativity for education, while older students view creativity-related issues more objectively. It should be noted that the correlation between the variable YEA - the year in which the student is studying and the dimension EI - entrepreneurial Intentions is negative, but not statistically significant. This points to the slightly negative impact of the year in which the student is studying on entrepreneurial intentions, where there is a slight drop in entrepreneurial intentions at the end of studies. At the end of their studies, students find it easier to find work within an existing company with their diploma. This result has a similarity with the results obtained in the research by Minola et al. (2016) and Tsai et al. (2016).

The variable SUC - student success in learning and studying produces statistically significant and positive correlations with all the dimensions of enterprise potential (LEA - leadership, CRE - creativity, ACH - achievement and PC - personal control), as well as with the dimensions PR - proactiveness and SN - subjective norm (Table 3). Therefore, student success in learning and studying develops and positively affects individual personality traits, which in this case are seen as entrepreneurial potentials and other dimensions related to entrepreneurship.

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RAJKOVIĆ. J., POŠTIN. J., KONJIKUŠIĆ. M., RUSIĆ. J. A., STOJKOVIĆ. H. S., NIKOLIĆ. M.  THE ENTERPRISE POTENTIAL, INDIVIDUAL ENTREPRENEURIAL ORIENTATION AND ENTREPRENEURIAL INTENTIONS OF STUDENTS IN SERBIA

However, the very existence of these potentials and traits contributes significantly to successful studying. Here, there seems to be a cyclical effect, where certain personality traits contribute to success in studies, and in turn this success serves to further enhance these same qualities.

Finally, support from the environment, and especially from family members becomes stronger thanks to success in studies. It is also interesting that SUC – student success in learning and studying has negative correlations with the constructs of PA - personal attitude and EI - entrepreneurial intentions.

Although these correlations are not statistically significant, this result points to the direction of the influence of the variable SUC – student success in learning and studying. Successful students find good jobs more easily and will be able to live more effectively with their acquired professional knowledge and developed skills, so they are less likely to think about the risks and uncertainties of entrepreneurial ventures.

The variable FIN - the financial opportunities to start a new business realizes statistically significant and positive correlations with the following dimensions: RT - risk taking, PA - personal attitude, PBC - perceived behavioural control and EI - entrepreneurial intentions (Table 3). In doing so, the strongest correlation is achieved with the EI - entrepreneurial intentions dimension. This result is consistent with some existing research, for example (Kim, Longest, & Aldrich, 2013; Rodriguez, Tuggle, & Hackett, 2009; Shinnar, & Young, 2008). It may thus be concluded that the possession of finance is the most influential variable for the existence of entrepreneurial intentions. The other correlations of this variable show that this is due to the fact that individuals who (relative/perceived) have significant financial resources also have an increased perception of their entrepreneurial abilities, a better opinion on entrepreneurship and are more willing to take risks (it is easier for them to take risks or risk is not perceived as strongly as it is by someone who does not have access to money). Practically, money helps in lifting of selfconfidence in entrepreneurial abilities and risk-taking possibilities. In addition, this result is largely the consequence of the current conditions in Serbia, which primarily refers to the low standard of living and the poor financial capabilities of most citizens. García-Rodríguez, Gil-Soto, Ruiz-Rosa and Mamour Sene (2015), also showed that desire has the greatest impact on entrepreneurial intentions in Spain (a representative of a highly developed country), while feasibility has the greatest impact on entrepreneurial intentions in Senegal (a representative of a less developed country). Serbia can be classified into the category of less developed countries, and the feasibility of engaging in entrepreneurial ventures is certainly linked to the possession of finance.

According to the regression analysis (Table 4), the corrected determination indexes R2 have quite low, but predominantly statistically significant values (0.011 to 0.098). The dimension EI - entrepreneurial intentions, followed by the dimension PBC - perceived behavioural control, are under the strongest predictive impact of the observed independent variables. The variable FIN - the possession of finance has the decisive influence on this result.

The results of regression analysis are consistent with the results of the correlation analysis. For example, according to both correlation and regression analysis, variables SUC - student success in learning and studying and FIN - the financial opportunities to start a new business have statistically significant impacts on completely different dimensions. Success in studies may develop entrepreneurial potentials dimensions (leadership, creativity, achievement, personal control) and proactivity, while the possession of finance develops the Theory of Planned Behaviour dimensions (personal attitude, perceived behavioural control), entrepreneurial intention and risk taking.

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RAJKOVIĆ. J., POŠTIN. J., KONJIKUŠIĆ. M., RUSIĆ. J. A., STOJKOVIĆ. H. S., NIKOLIĆ. M.  THE ENTERPRISE POTENTIAL, INDIVIDUAL ENTREPRENEURIAL ORIENTATION AND ENTREPRENEURIAL INTENTIONS OF STUDENTS IN SERBIA

The answer to RQ3: the variable of YEA – the year in which the student is studying has a statistically significant and negative impact on only one of the observed entrepreneurial dimensions (CRE - creativity).

The answer to RQ4: the variable of SUC – student success in learning and studying has a statistically significant and positive impact on the most of the observed entrepreneurial dimensions (LEA - leadership, CRE - creativity, ACH - achievement, PC - personal control, PR - proactiveness and SN - subjective norm).

The answer to RQ5: the variable of FIN - the financial opportunities to start a new business has a statistically significant and positive impact on some of the observed entrepreneurial dimensions (RT - risk taking, PA - personal attitude, PBC - perceived behavioural control and EI - entrepreneurial intentions).

Discussion of the Moderating Effects of GEN - Gender (Answering RQ6)

Tables 5 and 6 show relatively weak moderating influence of the variable GEN - gender on the relation between the variables YEA – the year in which the student is studying, SUC - student success in learning and studying and FIN - the financial opportunities to start a new business and the observed entrepreneurial dimensions. The statistically significant moderating impact occurs only in a small number of cases, which are concentrated around the variable FIN - the financial opportunities to start a new business. For women this variable has much more powerful and positive effects in several dimensions: RT - risk taking, PA - personal attitude, PBC - perceived behavioural control and EI - entrepreneurial intentions. Also, the variable SUC - student success in learning and studying has a more positive impact on certain dimensions for women: LEA - leadership, ACH - achievement, PC - personal control and PR - proactiveness. In these cases, the moderating effect of SUC - student success in learning and studying is not statistically significant, but the correlations in Table 5 indicate certain tendencies in this direction. Practically, the results of the correlation analysis increase for women. It is interesting that men showed a statistically significant increase in PBC - perceived behavioural control and EI entrepreneurial Intentions (T-test, Table 2).

The explanation for such results may lie in the fact that in cases of success in studying and the good financial opportunities, women are significantly more motivated and more determined, they know exactly what they want and how to use their professional and financial potentials. All this in turn leads to more positive attitudes towards entrepreneurship, greater self-confidence in assessing their own abilities, greater willingness to take risks, and, finally, stronger entrepreneurial intentions (especially in the case of having access to finance). Observed in the other direction, when women do not have the needed resources to start their own firm, they think much less about entrepreneurship. At the same time, men in such conditions still do not give up, and they continue to imagine their possible engagement in entrepreneurial work. It can be concluded that in terms of entrepreneurship women are more realistic and decisive, while men may prefer to be entrepreneurs, but they do not know how to go about achieving such a goal. This conclusion bears similarities with the result obtained by Diaz-Garcia and Jiménez-Moreno (2010), which suggests that, although men think more about setting up a company, they show less determination to do so.

The answer to RQ6: the moderating effect of the variable GEN - gender on the observed relations is not so strongly expressed, and acts in the way that for women the variables FIN - the financial opportunities to start a new business and SUC - student success in learning and studying are much more powerful and positively affect certain dimensions.

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CONCLUSION

The variable GEN - gender has a statistically significant effect on most of the observed dimensions.

For men the most powerful dimensions are: LEA - leadership, PC - personal control, PBC - perceived behavioural control and EI - entrepreneurial intentions. The more pronounced dimensions for women are: CRE - creativity, PR - proactiveness and SN - subjective norm. The variable PAR - one of my parents has a private business does not show any statistically significant influence on any of the observed dimensions. The same holds true for the YEA - the year in which the student is studying variable, which has a statistically significant and negative impact only with the CRE - creativity dimension.

The variable SUC - student success in learning and studying generates statistically significant and positive impacts with all the entrepreneurial potential dimensions (LEA - leadership, CRE - creativity, ACH - achievement and PC - personal control), as well as with the dimensions of PR - proactiveness and SN - subjective norm. The variable FIN - the financial opportunities to start a new business realizes statistically significant and positive impacts predominantly with the dimensions of the Theory of Planned Behaviour dimensions (PA - personal attitude, PBC - perceived behavioural control) and with the dimensions of EI - entrepreneurial intentions and RT - risk taking. It should be noted that the variables SUC - student success in learning and studying and FIN - the financial opportunities to start a new business have statistically significant impacts on quite another entrepreneurial dimensions.

The influences of the observed variables on dimension of EI are of special importance – entrepreneurial intentions. The most important variable for entrepreneurial intentions is FIN - the financial opportunities to start a new business. Also, men have more pronounced entrepreneurial intentions. The other variables do not have a statistically significant impact on entrepreneurial intentions, but the tendency is for entrepreneurial intentions to decline in cases where parents do not have a private business, then in the final years of study and in situations where there is significant success in studies. Under such conditions, students are more inclined to focus on their careers in existing companies.

The moderating effect of the variable GEN - gender is as follows: in the case of successful studying and the possession of finance, women are more motivated and more determined, have more positive attitudes towards entrepreneurship, highly assess their own abilities, are more ready to take risks and have stronger entrepreneurial intentions (especially in the case of the possession of finance). When women do not have the money to start their own businesses, then they think much less about entrepreneurship. Overall, men may prefer more to be entrepreneurs, but women approach entrepreneurship more realistically and decisively.

The findings in this paper are based on a sample of students in Serbia. This statement is a basic limitation of the study. However, the findings may be particularly suitable for understanding similar relationships in other transitional and less developed countries. In practical terms, the research has shown that the intensification of entrepreneurial intentions among young people in Serbia may be achieved through education and assistance from state institutions. The help of state institutions should be logistical and advisory, but it is particularly important to provide appropriate financial support to potential entrepreneurs who have good ideas, but not the funds to start an entrepreneurial venture. In addition, stronger support and more attention should be paid to groups with reduced entrepreneurial intentions, namely female students and successful students in the final years of study.

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A special contribution of this paper in the theoretical sense is that the effects of The year in which the student is studying and Student success in learning and studying on the observed entrepreneurial dimensions are examined, as these are two variables that have not been sufficiently researched in this context so far, despite numerous published papers in the field of entrepreneurial intentions. The contribution of this paper in the practical sense is visible in the consideration of the way all five observed variables influence on entrepreneurial dimensions, which resulted in appropriate proposals for encouraging entrepreneurial intentions among students in Serbia.

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Vukonjanski, J., Nikolić, M., Hadžić, O., Terek, E. &Nedeljković, M. (2012). Relationship between GLOBE organizational culture dimensions, job satisfaction and leader-member exchange in Serbian organizations.

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Entrepreneurship Research Journal, 5(1), 61-82.

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RAJKOVIĆ. J., POŠTIN. J., KONJIKUŠIĆ. M., RUSIĆ. J. A., STOJKOVIĆ. H. S., NIKOLIĆ. M.  THE ENTERPRISE POTENTIAL, INDIVIDUAL ENTREPRENEURIAL ORIENTATION AND ENTREPRENEURIAL INTENTIONS OF STUDENTS IN SERBIA

PREDUZETNIČKI POTENCIJAL, INDIVIDUALNA PREDUZETNIČKA ORIJENTACIJA I PREDUZETNIČKE NAMERE STUDENATA U SRBIJI Rezime: U ovom radu dati su rezultati istraživanja učenika o uticajima pet varijabli na dimenzije potencijala preduzeća, individualnu preduzetničku orijentaciju, teoriju planiranog ponašanja (TPB) i dimenziju preduzetničke namere studenata. Uočeni su uticaji sledećih varijabli: Pol, Jedan od mojih roditelja ima privatno preduzeće, Godina u kojoj student studira, Uspeh studenata u učenju i Finansijske mogućnosti za započinjanje novog posla. Ispitanici studiraju na sedam fakulteta u Srbiji. Uzorak je obuhvatio 488 ispitanika. Najuticajnija varijabla na dimenziju preduzetničke namere su finansijske mogućnosti za započinjanje novog posla. Takođe, muškarci imaju izraženije preduzetničke namere. Uspeh u studijama pozitivno utiče na preduzetničke potencijale i proaktivnost, dok posedovanje finansija pozitivno utiče na preduzetničke namere i spremnost na rizik. U slučajevima uspešnih studija i posedovanja finansija, žene su više motivisane, odlučnije i imaju izraženije preduzetničke namere (posebno u slučaju da imaju finansijska sredstva). Iako muškarci možda više žele da postanu preduzetnici, žene preduzetništvu pristupaju realnije i odlučnije.

Ključne reči: preduzetnički potencijal, individualna preduzetnička orijentacija, preduzetničke namere, studenti, Srbija.

Klasifikacija jela: L26, D23

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EJAE 2021, 18(1): 126 - 136 ISSN 2406-2588 UDK: 336.27(439:437.6:437.1:438) DOI: 10.5937/EJAE18-29568 Original paper/Originalni naučni rad

IS THERE ANY GOVERNMENT DEBT THRESHOLD IN FOUR SELECTED CENTRAL EUROPEAN COUNTRIES? Yu Hsing* Southeastern Louisiana University, College of Business, Department of Management & Business Administration, Hammond, Louisiana, USA

Abstract: This article shows that there is no government debt threshold in Hungary and Slovakia and that the respective government debt thresholds in Czechia and Poland are estimated to be 27.51% and 46.86%, which are far smaller than the 90% threshold suggested by Reinhart and Rogoff.

Hence, the Reinhart-Rogoff threshold is not applicable to Czechia, Hungary, Poland and Slovakia.

Article info: Received: December 14, 2020 Correction: January 12, 2021 Accepted: February 12, 2021 Keywords: fiscal expansion, government debt, Reinhart-Rogoff threshold, production function.

JEL Classification: E62

INTRODUCTION During the 2008-2009 global financial crisis, many countries pursued fiscal expansion in order to rescue their economies. For example, during 2008-2009, Czechia increased the government debt ratio from 28.25% to 33.56%. Hungary’s government debt ratio rose from 71.24% to 77.47%. Poland’s debt ratio went up from 46.30% to 49.43%. Slovakia raised the debt ratio from 28.46% to 36.29%. These debt ratios continued to rise up until 2011 in Hungary and 2013 in Czechia, Poland and Slovakia.

Whether an increase in the government debt ratio raises or haram the growth rate of real GDP has been studied extensively. Reinhart and Rogoff (2010a, 2010b) show that the turning point or threshold of the government debt ratio is 90%. Above 90%, a higher debt ratio tends to reduce the growth rate.

The objective of this paper is to determine whether the Reinhart-Rogoff proposal may apply to four selected Central European countries, namely, Czechia, Hungary, Poland and Slovakia.

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*E-mail: yhsing@selu.edu

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Is it possible to have a threshold or turning point of 90% for these four countries with debt ratios less than 90%? This paper differs from most previous studies in several aspects. First, a theoretical model based on an extended production function is presented. Second, a quadratic function is applied to test whether an inverted U-shape relationship is achievable. Third, the GARCH model is used in order to detect possible conditional heteroskedasticity in time series data.

LITERATURE REVIEW Based on the data of forty-four countries covering about 200 years, Reinhart and Rogoff (2010a, 2010b) and Reinhart, Reinhart and Rogoff (2012) find that the growth rate and the debt ratio have a weak relationship when the government debt ratio is smaller than 90% whereas a government debt ratio above 90% causes the growth rate to decline. This threshold for the government debt ratio is similar in advanced and emerging countries. However, using the same sample constructed by Reinhart and Rogoff and other specifications, Minea and Parent (2012) find that the turning point for the government debt ratio is 115%. Herndon, Ash and Pollin (2014) use the same data and find that for 20 advanced countries, the negative impact of the debt ratio above 90% on the growth rate cannot be confirmed.

According to Herndon, Ash and Pollin, during 1946–2009, countries with the debt ratios above 90% recorded a growth rate of 2.2% instead of −0.1% as claimed by Reinhart and Rogoff. The impact of the debt ratio on the growth rate varies substantially across countries and periods. Égert (2015a, 2015b) could not confirm the 90% debt threshold, indicating that the threshold was in the range of 20% - 60%. A nonlinear relationship is sensitive to the specifications of the model. Parameter estimates differ by country.

Lee, Park, Seo and Shin (2017) find no support for the 90% debt threshold. Using the post-WWII data developed by Reinhart and Rogoff, they indicate that the debt threshold is around 30%. Above 30%, the growth rate will decline by 1 percentage point.

Based on the data for eighteen OECD countries during 1980-2008, Cecchetti, Mohanty and Zampolli (2011) find that the debt threshold is 85%, indicating that a higher government debt ratio above 85% tends to have a negative effect on the growth rate. They also show that when the corporate debt ratio is greater than 90% of GDP, an adverse impact on the growth rate is found.

Using a sample of 12 countries in the euro area, Checherita-Westphal and Rother (2012) find a debt threshold between 90% and 100%, indicating that a higher government debt ratio tends to reduce the growth rate if the debt ratio is greater than 90% - 100%. The threshold starts in the range of 70% - 80% based on the confidence interval.

Based on the data for twelve countries in the euro area, Baum, Checherita-Westphal and Rother (2013) find that a higher debt ratio increases the growth rate but has no impact when the debt ratio reaches about 67%. When the debt ratio is greater than 95%, a higher debt ratio reduces the growth rate.

Afonso and Jalles (2013) examine the relationship between government debt and economic growth for 155 advanced and developing countries during 1970 – 2008. The debt threshold is estimated to be 59% for the euro zone and 79% for emerging economies. If the debt ratio increases 10%, the growth rate would decrease by 0.2% if the debt ratio is over 90% and increase by 0.1% if the debt ratio is below 30%.

Chirwa (2020) studies the relationship among government debt, growth and other related variables for ten countries in the euro area. The threshold is estimated to be 70% in the long run whereas more government debt has a negative effect on the growth rate in the short run.

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Woo and Kumar (2015) reveal that if the initial debt ratio rises by 10 percentage points, the growth rate of real per capita GDP tends to reduce by 0.2 percentage points. A higher initial government debt ratio tends to cause a larger negative effect. The negative effect is the result of decrease in labor productivity growth.

Lechtenberg (2017) analyzes the subject using a sample of 10 countries. Australia, Canada, Chile, Germany and New Zealand have had low and declining debt ratios, and a higher debt ratio has not caused a decline in economic growth of these countries. On the other hand, debt thresholds are detected for several advanced countries such as the USA , the UKU, France, Greece, and Italy. A higher debt ratio beyond the threshold tends to reduce economic growth in Greece, Italy, the UK and the USA but increase economic growth rate for France.

Yared (2019) examines the trend of growing government debt, analyzes causes, and provides answers to the issue concerned. He indicates that fiscal rules are needed for policymakers not to spend beyond means and provide flexible measures to react to economic crises.

Based on the data consisting of many developing and advanced countries, Swamy (2020) reveals that if the debt ratio rises by 10 percentage points, the average growth rate will decline by 23 basis points and that government debt and economic growth have a nonlinear relationship. The effect of government debt on the growth rate varies by country, depending upon several major macroeconomic factors and debt regimes.

Jacobs, Ogawa, Sterken and Tokutsu (2020) explore the impact of government debt on the economic growth rate for 27 European Union members and 4 OECD countries during 1995-2013. They show that economic growth Granger causes government debt, but government debt does not cause Granger economic growth. Slow economic growth causes more government debt. In high-debt economies, slow economic growth increases government debt, which causes a higher interest rate in the long run, dampens interest-rate sensitive private spending, and increases public debt. In addition, they indicate that high-debt economies show greater impacts of the growth rate on the debt ratio and that low-debt countries exhibit greater effects of the debt ratio on the growth rate.

THEORETICAL MODEL Based on the studies of Ram (1986, 1989), Goel, Payne and Ram (2008) and others, we can express the growth rate of real GDP as: (1) Where Y = real GDP, L = labor employment, K = capital, = the growth rate of Y, = the growth rate of L, = the growth rate of K, and D = the ratio of government debt to GDP. 128

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Because data for capital is not available, the growth rate of capital can be substituted by the ratio of investment spending to Y (IY) (Ram, 1986, 1989).

(2) In a linear form, the coefficient of measures the percent change in output over the percent change in labor, and the coefficient of IY represents the partial derivative of Y with respect to K. The coefficients of the first two explanatory are expected to be positive, and the coefficient of the debt ratio is unclear.

A lower and rising government debt ratio for infrastructural improvements may be conducive to economic growth whereas a higher and rising debt ratio may cause the interest rate to rise, crowd out consumption and investment spending, cause the local currency to appreciate, and hurt exports. and the debt ratio may exhibit an inverted U-shaped relationship. In that case, we can consider the following equation: (3) If there is an inverted U-shaped relationship between and the debt ratio, the coefficient of D should be positive, and the coefficient of should be negative.

The turning point of the debt ratio is given by: (4) where

is the coefficient of D and

is the coefficient of

.

EMPIRICAL RESULTS The sources of data came from the International Financial Statistics, the Eurostat, and the World Economic Outlook. The growth rate of real gross domestic product (GDP) or labor employment is expressed as a percent. Investment spending as a percent of GDP is used as data for capital is not available.

Government debt as a percent of GDP is used. This study covers the sample period from 1995 or 1996 to 2019. Data for the government debt ratio or the growth rate before 1995 or 1996 is not available.

Figure 1 exhibits scatter diagrams between the economic growth rate and the government debt ratio for these four countries during the sample period. It seems that the government debt ratio and the economic growth rate in Czechia or Poland have a nonlinear relationship whereas the government debt ratio and the economic growth rate in Hungary and Slovakia shows a negative or an unclear relationship. Hypothesis testing is required as to determine whether the above observations can be verified statistically.

Table 1 presents the estimated regressions and related statistics. Two versions with and without the quadratic term are estimated for each country. For Czechia, in version A, the quadratic term is negative and statistically significant at 1% level. The threshold or turning point is estimated to be 27.51%. A further increase in the debt ratio beyond the threshold reduces the growth rate. In version B without the quadratic term, the debt ratio has a positive and significant coefficient, which makes us draw a misleading conclusion that the government can continue increasing the debt ratio and generate a positive impact on the economic growth rate.

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Figure 1: Scatter Diagrams between the Growth Rate and the Debt Ratio

130

-0.0082 (0.0000)

0.3298

4.2813

4.6740

Debt ratio squared

R squared

Akaike info criterion

Schwarz criterion

Notes: Figures in the parenthesis are probabilities.

27.5144%

0.0506 (0.0000)

0.4483 (0.0000)

Debt ratio

Estimated debt threshold

0.4644 (0.0000)

0.5265 (0.0000)

Investment/GDP ratio

1996-2019

0.5543 (0.0000)

0.6332 (0.0000)

Growth rate of employment

Sample period

-11.7630 (0.0000)

-17.4477 (0.0000)

Constant

1996-2019

4.6900

4.3464

0.2768

Czechia B

Czechia A

Variable

Table 1: Estimated Regressions of the Growth Rate of Real GDP

None

1995-2019

4.3269

3.9369

0.6406

0.0074 (0.0000)

-0.9881 (0.0000)

0.6907 (0.0000)

0.5617 (0.0000)

17.3740 (0.0000)

Hungary A

1995-2018

4.1883

3.8470

0.5744

-0.0021 (0.9549)

0.5212 (0.0000

0.4717 (0.0001

-10.0552 (0.0558)

Hungary B

46.8577%

1995-2019

3.5294

3.1393

0.5463

-0.0262 (0.0000)

2.4553 (0.0000)

0.5508 (0.0000)

0.1099 (0.0000)

-64.4444 (0.0000)

Poland A

1995-2019

3.8776

3.5363

0.4450

-0.0389 (0.3413)

0.2650 (0.0214)

0.4112 (0.0030)

0.2538 (0.9390)

Poland B

None

1995-2019

4.8041

4.4114

0.3678

0.0088 (0.0000)

-0.8985 (0.0000)

0.1890 (0.0000)

0.7349 (0.0000)

20.5162 (0.0000)

Slovakia A

1995-2019

4.5499

4.2086

0.5413

-0.1203 (0.0000)

0.1304 (0.0002)

0.7713 (0.0000)

5.0770 (0.0007)

Slovakia B

HSING. Y.  IS THERE ANY GOVERNMENT DEBT THRESHOLD IN FOUR SELECTED CENTRAL EUROPEAN COUNTRIES?

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For Hungary, in version A with the quadratic term, all coefficients are significant. However, a positive significant sign of the quadratic term suggests that the government debt ratio and the growth rate exhibit a U-shape correlation, which is contrary to economic theory. In version B without the quadratic term, a coefficient of the government debt ratio is negative and insignificant at 10% level. In comparison, the findings by Hsing (2018) cannot be compared with the result in this paper because real GDP instead of the percent change in real GDP, is chosen as the dependent variable.

For Poland, in version A, a coefficient of the quadratic term is negative and significant at 1% level.

The threshold of the debt ratio is calculated to be 46.86%. A higher government debt ratio than this threshold tends to reduce the growth rate. In version B without the quadratic term, a coefficient of the government debt ratio is negative and insignificant at 10% level. If version B is chosen, we may draw a misleading conclusion that a higher government debt ratio will not be harmful to the growth rate.

For Slovakia, in version A with the quadratic term, a negative significant sign of the debt ratio and a positive significant sign of the quadratic term indicate a U-shape relationship, which is inconsistent with economic theory. In version B without the quadratic term, a negative significant sign of the government debt ratio suggests that a higher government debt ratio causes the growth rate to decline regardless of the level of debt ratio.

In comparison, the findings in this paper are different from and similar to some of previous studies.

Reinhart and Rogoff (2010a, 2010b) show that the threshold for the debt ratio is 90% whereas the respective thresholds of debt ratio for Czechia and Poland are estimated to be 27.51% and 46.86%.

The results for Hungary (Version A) and Slovakia (Versions A and B) are consistent with Kumar and Woo (2015) and Swamy (2020), who indicate that the debt ratio has a negative impact on the growth rate. The thresholds reported by Cecchetti, Mohanty and Zampolli (2011), Checherita-Westphal and Rother (2012), Minea and Parent (2012), Afonso and Jalles (2013), Baum, Checherita-Westphal, and Rother (2013), and Chirwa (2020) are higher than the respective thresholds of 27.51% and 45.26% for Czechia and Poland. The estimated thresholds for Czechia and Poland are close to the 30% debt threshold found by Lee, Park, Seo and Shin (2017), the threshold between 20% and 60% estimated by Égert (2015a, 2015b), and the threshold of 47.45% for Italy reported by Lechtenberg (2017).

SUMMARY AND CONCLUSIONS This article has studied the impact of the government debt ratio on the growth rate of real GDP for Czechia, Hungary, Poland and Slovakia using an extended production function. The growth rate is specified as function of the percent change in labor employment, the ratio of investment spending to nominal gross domestic product, and the government debt ratio. A quadratic form is used for the debt ratio to test whether a nonlinear inverted U-shape relationship exists.

The results have confirmed that Czechia or Poland exhibits a threshold or turning point whereas Hungary and Slovakia do not show an inverted U-shape relationship. The threshold for Czechia is estimated to be 27.51%. The growth rate and the government debt ratio have a positive relationship when the debt ratio is up to 27.51% whereas they have a negative relationship when the government debt ratio is greater than 27.51%. The estimated threshold for Poland is 46.86%, suggesting that a higher government debt ratio beyond 46.86% tends to reduce economic growth.

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These results have some policy implications. An analysis of data indicates that debt ratios in these four countries show declining trends. For example, the debt ratio in Czechia declined from 44.91% in 2013 to 31.62% in 2019. The debt ratio in Hungary declined from 80.48% to 67.52%. The debt ratio in Poland dropped from 54.23% to 47.77% in 2019. The debt ratio in Slovakia decreased from 54.74% in 2013 to 48.35% in 2019. Debt ratios in Czechia and Poland declined and moved toward the respective thresholds of 27.51% and 46.86%, suggesting that the direction of changes in debt ratio is justified.

Lower debt ratios in Hungary and Slovakia in recent years have also helped to raise the growth rate as the debt ratio has an adverse effect on the economic growth rate as shown in the estimated regression with the quadratic term in Hungary and the regression with or without the quadratic term in Slovakia.

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HSING. Y.  IS THERE ANY GOVERNMENT DEBT THRESHOLD IN FOUR SELECTED CENTRAL EUROPEAN COUNTRIES?

Shahor, T. (2018). The impact of public debt on economic growth in the Israeli economy. Israel Affairs, 24(2), 254-264. https://doi.org/10.1080/13537121.2018.1429547 Panizza, U. & Presbitero, A. F. (2013). Public debt and economic growth in advanced economies: A Survey. Swiss Journal of Economics and Statistics, 149(2), 175–204. Retrieved from: https://sjes.springeropen.com/track/ pdf/10.1007/BF03399388.pdf Panizza, U. & Presbitero, A. F. (2014). Public debt and economic growth: Is there a causal effect? Journal of Macroeconomics, 41, 21–41. https://doi.org/10.1016/j.jmacro.2014.03.009 Pegkas, P. (2018). The effect of government debt and other determinants on economic growth: The Greek experience.

Economies, 6(1), 1-19. https://doi.org/10.3390/economies6010010 Puente-Ajovín, M. & M. Sanso-Navarro. (2015). Granger causality between debt and growth: Evidence from OECD Countries. International Review of Economics and Finance, 35, 66–77. https://doi.org/10.1016/j. iref.2014.09.007 Reinhart, C. M., Reinhart, V. R. & Rogoff, K. S. (2012). Public Debt Overhangs: Advanced-Economy Episodes since 1800. Journal of Economic Perspectives, 26(3), 69–86. https://doi.org/10.1257/jep.26.3.69 Swamy, V. (2020). Debt and growth: Decomposing the cause-and-effect relationship. International Journal of Finance & Economics, 25, 141-156. https://doi.org/10.1002/ijfe.1729 Woo, J., & Kumar, M. S. (2015). Public debt and growth. Economica, 82(328), 705-739. https://doi.org/10.1111/ecca.12138 Yared, P. (2019). Rising government debt: Causes and solutions for a decades-old trend. Journal of Economic Perspectives, 33(2), 115-140. https://doi.org/10.1257/jep.33.2.115

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HSING. Y.  IS THERE ANY GOVERNMENT DEBT THRESHOLD IN FOUR SELECTED CENTRAL EUROPEAN COUNTRIES?

POSTOJI LI PRAG JAVNOG DUGA U ČETIRI IZABRANE CENTRALNOEVROPSKE ZEMLJE? Rezime: Ovaj rad pokazuje da u Mađarskoj i Slovačkoj ne postoji prag javnog duga i da se procenjuje da su pragovi javnog duga u Češkoj i Poljskoj 27,51% i 46,86%, što je daleko manje od praga od 90% koji su predložili Rajnhart i Rogof. Stoga, ovaj prag nije primenljiv na Češku, Mađarsku, Poljsku i Slovačku.

Ključne reči: fiskalna ekspanzija, javni dug, prag Rajnharta i Rogofa, proizvodna funkcija.

Klasifikacija jela: E62

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EJAE 2021, 18(1): 137 - 150 ISSN 2406-2588 UDK: 658.62 659.113.25 DOI: 10.5937/EJAE18-28972 Original paper/Originalni naučni rad

THE EFFECT OF COUNTRY - OF - ORIGIN IMAGE TROUGH QUALITY, DESIGN AND ATTRACTIVENESS RELATED TO PRODUCT ON CONSUMER LOYALTY Srđan Šapić, Jovana Lazarević*, Jovana Filipović Faculty of Economics, University of Kragujevac, Kragujevac, Serbia

Abstract: The main goal of this research is to determine whether there is an impact of country – of- origin image on consumer loyalty to products originating from countries with positive and recognizable image and whether this impact is achieved through characteristics such as product quality and design, and attractiveness related to using the same. To examine this impact, an empirical study was conducted on a sample of 150 respondents and analysis of collected data was performed in SPSS 20. The results show that information about country- of -origin image is important to consumers and has an impact on their behavior related to buying foreign product. More precisely, results show that country –of- origin image affects consumers when they choose products based on their quality and design and attractiveness that consumers feel when using these products, and also that through these characteristics country -of -origin image has effects on consumer loyalty. In accordance with the obtained results, conclusion is that it is extremely desirable for companies and governments to take into account the image that their country enjoys on the world market.

Article info: Received: October 23, 2020 Correction: December 16, 2020 Accepted: February 8, 2021

Keywords: country -of -origin image, product quality, product design, attractiveness, consumer loyalty.

INTRODUCTION The concept of country -of -origin image is one of the most famous concepts that has been researched in social sciences. Concept developed when manufacturers began to internationalize their activities and export products to foreign markets, where they had to comply with certain legal requirements and indicate product country- of -origin on packaging or product itself. Initially, this information was just a way to identify the origin of product. However, over time, consumer behavior researchers and marketers have realized that country- of -origin image, as the image that individuals have about a country, is information that is important for consumers when making decision to buy foreign product. *E-mail: jsavic@kg.ac.rs

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ŠAPIĆ. S., LAZAREVIĆ. J., FILIPOVIĆ. J.  THE EFFECT OF COUNTRY - OF - ORIGIN IMAGE TROUGH QUALITY, DESIGN AND ATTRACTIVENESS RELATED TO PRODUCT ON CONSUMER LOYALTY

Consumer country information is an indicator of product superiority, quality and acceptability. When consumers choose between several alternative foreign products, countries image they come from will influence consumers to perceive these products completely differently. However, whether and in what way consumers will use the information about country -of -origin image for product they want to buy, depends on numerous factors such as the type of product, consumer experience in relation to that or other products originating from a certain country, etc. Consumers may also be influenced by positive or negative stereotypes about certain countries that are passed on to its products (Aichner, 2014). When consumers have positive image of a country and its products, they are ready to buy such products again because in that way they avoid risk of making the wrong choice. Research shows that consumer attachment to products originating from a country towards which they have positive attitude is a consequence of positive perception of product characteristics, with products from certain countries being considered to be of high quality, attractive design and high technological development (Ahmed & d'Astous, 2015; Coudounaris, 2018; Karimov & El-Murad, 2019).

The effect of country -of -origin image on consumer perception is influenced by certain factors (Thøgersen et al., 2020). This effect primarily differs depending on type and characteristics of the product. Then, information about the economy, social and cultural system enables consumers to position countries in their consciousness and assess their image on that basis. Finally, the demographic and socio-psychological characteristics of consumers significantly affect their perception of countries and products that come from them. Despite numerous studies on effect of country- of -origin image in past decades, there are certain limitations of the same. One of the main limitations is that previous research has been conducted mainly in specific geographical areas, most often developed countries (Sharma, 2011), whose results are often generalized. Another problem related to previous research involves predominantly examining effect of country –of- origin image depending on different product categories, making it difficult to obtain generalisations when it comes to product categories that are not immediately linked to a country -of -origin image (Tseng & Balabanis, 2011). In order to overcome above mentioned limitations, it is recommended to conduct new research related to observed issues.

Based on the above, a research was conducted in order to determine whether country -of -origin image affects consumer loyalty, not emphasizing special product categories but looking at the purchase of any foreign product in the Republic of Serbia as a transition economy. Further, research takes into account the impact of country -of origin -image on quality and design as product characteristics and attractiveness that consumers feel using them, and examines how important these characteristics are when it comes to consumer loyalty. The rest of the article is divided as follows: the first part refers to literature review and includes brief overview of theoretical findings that were used in determining goal and defining research hypotheses. The second part presents methodology i.e. analyzes that were conducted during research. The following section describes a sample of respondents from whom primary data were collected. The fourth part includes obtained results and their discussion. The last part of the article presents the most important conclusions about obtained results and provides guidelines for future research.

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ŠAPIĆ. S., LAZAREVIĆ. J., FILIPOVIĆ. J.  THE EFFECT OF COUNTRY - OF - ORIGIN IMAGE TROUGH QUALITY, DESIGN AND ATTRACTIVENESS RELATED TO PRODUCT ON CONSUMER LOYALTY

LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT

Place in the world where certain product is produced or its source of origin is called country of origin (Parkvithee & Miranda, 2012). In today's world economy, many products consist of parts that are produced in different countries, but these parts are assembled into the final product in a particular country. Manufacturers take care that country in which final product is assembled is country of origin of products that consumers in most parts of the world perceive as country that produces products of high quality, reliability or technological sophistication. In this way, producers take advantage of effect of country -of -origin image and influence that it can have on perception of consumers and their decision-making when buying foreign products. There are several definitions of effect of country -of -origin image. One is that this effect is the picture, the reputation, the stereotype that businessmen and consumers attach to products of a specific country (Nagashima, 1970, p. 68). It is also defined as the influence on a buyer considering a product or service from another country due to the stereotyping of that country and its outputs (Suh et al., 2016, p. 2721). In this regard, country -of -origin image has substantially the same influence on consumer product evaluation compared to other product functions (Katsumata & Song, 2015). In general, country -of -origin image is a multidimensional category that affects consumers so that they form a perception of products they know that come from a particular country, and then transfer that perception to other products from that country. In other words, information about country of origin provide consumers a summary of constructs of the actual product atributes and have important role in product evaluation and overall consumer behaviour (Cilingir & Basfirinci, 2014).

Country –of- origin image determines products on three levels: cognitive, affective and normative (Rashid, 2017). At cognitive level, country of origin is crucial when looking at product quality, with the proviso that one can speak of design quality and manufacturing quality. In case of affective level, country of origin influences the development of symbolic and emotional connotation of product and provides consumers with benefits in form of status or pride. When we look at normative level, we actually mean the degree of consumer ethnocentrism, i.e. his desire to support development of the economy by buying domestic products. Taking into account importance of country -of -origin image for formation of consumer perceptions about products, Karimov & El-Murad (2019) indicate presence of significant relationships between country of origin and evaluation of product quality. Klöckner et al.

(2013) confirm presence of effect of country -of -origin image on quality evaluation related to pepper.

Khan et al. (2012) in their research found that consumers consider German products to have a good style, while specifically viewing German cars as a symbol of luxury status. Kreppel & Holtbrügge (2012) start from the assumption that Chinese products country -of -origin image negatively influences on perceived products attractiveness and confirm that the same differs when it comes to sociopsyhological determinants of German consumers. Starting from the above, a clear relationship can be observed between country -of origin -image and characteristics such as quality, design and attractiveness, i.e. the following can be assumed: H1: There is a statistically significant effect of country- of -origin image on product related characteristics: H1a: There is a statistically significant effect of country –of- origin image on product quality; H1b: There is a statistically significant effect of country -of -origin image on product design; H1c: There is a statistically significant effect of country -of -origin image on the attractiveness that consumers feel due to the use of a particular product.

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ŠAPIĆ. S., LAZAREVIĆ. J., FILIPOVIĆ. J.  THE EFFECT OF COUNTRY - OF - ORIGIN IMAGE TROUGH QUALITY, DESIGN AND ATTRACTIVENESS RELATED TO PRODUCT ON CONSUMER LOYALTY

According to Zeng et al. (2015), key factors for obtaining sustainable competitive advantage in the market are the ability of the company to develop innovation and quality. Quality is a property of a product on basis of which its value is measured. Antić & Stevanović (2013) explain quality as all the properties and characteristics of products, processes and activities that are related to meeting certain consumer needs. The importance of quality is reflected in the fact that it can greatly contribute to achieving a competitive advantage in the market. The first determinant of overall consumer satisfaction is precisely perceived product quality, the second is perceived value and the third is consumer expectations (Fornell et al., 1996). High-quality products whose quality is constantly maintained and improved, again and again meet or exceed the expectations of consumers, which consequently leads to their satisfaction and, in the long run, their loyalty. As consumer loyalty is the basis of long-term profitability, companies are aware of how important it is to provide consumers with product quality they expect in order to bond with the company and its products in the long run. Assumptions about product quality effects on development of consumer loyalty have been empirically confirmed in numerous studies (Erdoğmuş & Büdeyri-Turan, 2012; Mohd Suki, 2017; Esmaeilpour, 2015; Gómez et al., 2018; Shanahan et al., 2019). Product design is one of the possibilities for strengthening competitiveness of both country's economy and its companies. At the beginning of the 21st century, design, through fashion, home products to mobile phones and computers, has become a key factor in the world of consumers. As aesthetic demands of consumers grow, so does the quality of design, because it is clear that consumer’s choice of a brand can be influenced by product design as important brand-related stimuli (Ramaseshan & Stein, 2014). Lusch & Swan (2011, p. 338) define design as the set of properties of an artifact, consisting of the discrete properties of the form (i.e., the aesthetics of the tangible good and/or service) and the function (i.e., its capabilities) together with the holistic properties of the integrated form and function. The importance of design is reflected in the fact that it creates added value by differentiating products on the market, and differentiation consequently leads to creation of a competitive advantage.

Design contributes to achieving a better market position because it primarily emphasizes product quality and influences consumer behavior. Design enriches product and additionally attracts attention of consumers, which affects their desire to buy product and their willingness to recommend it (Gul Gilal et al., 2018). Consumers are considered to see aesthetically designed and engineered products as products that are easier to use compared to those that lack such features. Better designed products are more desirable in the eyes of consumers, there will be positive reactions from them and feeling of excitement, attachment and attention. The above has been empirically confirmed by authors such as Elbedweihy (2016), Kumar et al. (2018), Hsu et al. (2018) etc. The company success in market competition depends largely on product attractiveness, uniqueness and creativity (Munir et al., 2020). Attractiveness of product largely depends on design, as well as quality and price of product, and presents an incentive for consumers to make initial purchase. Attractiveness of product is often linked to its usability. When consumers start using a product, its attractiveness increases if product is easy to use and if meets consumers needs. Conversely, product that appears attractive at first glance will quickly lose such perception by consumers if it functions inadequately (Maguire, 2004). Furthermore, perceived attractiveness of product is mainly related to its design. Attractive design is one that consumers find likable, simple, harmonious. Reber et al. point out that attractiveness of product design affects consumer purchasing intentions so that an increase in attractiveness leads to an increase in preferences for product and its purchase (cited in: Giese et al., 2014). Perceived attractiveness of product can be reflected in prestige of brand for which in studies of author Jin et al. (2015) and Esmaeilpour (2015) have been shown to have positive impact on consumer loyalty. Interesting results are presented by Kim & Kim (2020) who show that the attractiveness of Airbnb's rental services has positive impact on consumer loyalty.

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In accordance with previously explained relations related to observed characteristics and consumer loyalty, the paper will examine the following: H2: Product related characteristics have positive effects on development of consumer loyalty: H2a: Product quality has positive effects on development of consumer loyalty; H2b: Product design has positive effects on development of consumer loyalty;

H2c: The attractiveness that consumers feel due to the use of a particular product has positive effects on development of consumer loyalty.

Consumer loyalty can be defined as attachment to a brand or business entity that is based on a strong positive attitude and is manifested in repeated purchases (Marinković, 2012, p. 144). Consumer loyalty brings multiple benefits to a company such as spreading positive word- of- mouth, buying other brands of the company, or greater resilience of loyal consumers to competitive strategies (Mothersbaugh & Hawkins, 2016). On the other hand, loyalty also brings benefits to consumer, such as avoiding costs related to changing the company, reducing risks when buying, better quality services, etc. (Jobber & Fahy, 2006). Consumer loyalty is influenced by emotional connection, trust and reliability, care for consumers, knowledge about product, product availability and whether product meets wishes and requirements of consumers (Maričić, 2011). There are several factors that are key to achieving consumer loyalty. The most important factor and basis of loyalty is consumer satisfaction with company's products and services which reflects a person’s judgment of a product’s perceived performance in relationship to expectations (Kotler & Keller, 2016, p. 33). Consumer satisfaction is related to product or service quality and if it is missing, consumer loyalty does not develop (adapted according to: Šadić et al., 2016).

Brand trust is also recognized as an important factor in building loyalty (Veloutsou, 2015). When looking at relationship between product origin and consumer loyalty, Esmaeilpour & Ali Abdolvand (2016) empirically confirm the effect of country -of -origin image on consumer loyalty when it comes to luxury products. Authors Šapić et al. (2018) in their work come to the conclusion that quality, design and prestige show statistically significant effects on development of consumer loyalty, while respecting effect of country -of -origin image. Taking into account previously explained relations between country -of -origin image, observed characteristics and consumer loyalty, paper will test the following: H3: Country -of -origin image shows statistically significant effects on consumer loyalty through characteristics such as quality, design and attractiveness.

RESEARCH METHODOLOGY In order to prove the set hypotheses, primary data were collected from the respondents on the territory of Kragujevac and its surroundings in the period from 10 to 25 July 2020. The survey was conducted using questionnaire technique, in person. Questionnaire is self-administered, contains 3 demographic questions and 21 seven-point scaled statements in which respondents expressed their degree of agreement with a given statement, where grade 1 indicates absolute disagreement and grade 7 absolute agreement of respondents with the statement. The sample surveyed includes 150 respondents and is segmented based on three demographic criteria such as gender, age, and education. The sample consists of 83 women or 55.3%, while men comprise 44.7% of the sample (67). The youngest respondents (18 to 35 years) make up 54% of the sample and there are 81. Respondents aged 36 to 55 have 49 and they comprise 32.7% sample, while the oldest respondents, over 56 years, make up 13.3% of the sample, i.e. there are 20.

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Regarding segmentation based on education, the largest percentage of the sample are respondents with secondary education, 44% (66). Higher education has 24.7% of respondents (37), while 47 respondents have the highest level of education and they cover 31.3% of the sample.

The statistical method was used for data analysis. The analysis was performed in the statistical software SPSS 20 (Statistical Package for the Social Sciences). In this program, appropriate statistical analyzes were conducted on basis of which conclusions were made. Reliability analysis examined the degree of internal consistency of statements that make up the observed research variables based on which basis for further statistical analysis was provided. The strength of linear or correlation dependence between the observed variables was determined by correlation analysis. Finally, by applying regression analysis, presence of effects of independent variables on dependent ones was tested, i.e. testing of research hypotheses was performed.

RESEARCH RESULTS In the continuation of the paper, results of statistical analyzes that were conducted are presented. In this regard, Table 1 presents results of research variables reliability analysis by determining the Cronbach’s alpha coefficient. A value of the observed coefficient greater than 0.7 (Nunnally, 1978) represents a minimum acceptable confidence threshold that indicates the internal consistency of statements that make up variables.

Table 1: Reliability analysis Variable

Cronbach’s alpha

Country of origin image

0.894

Quality

0.940

Design

0.893

Attractiveness

0.956

Loyalty

0.863

Source: authors’ calculation

Based on the obtained values of Cronbach’s alpha coefficient, it can be noticed that all variables consist of internally consistent statements, i.e. that they meet the initial reliability condition. The highest reliability was found for variable attractiveness, while statements of variable loyalty are the least internally consistent.

In the next step, correlation analysis was performed (Table 2) to determine degree of linear dependence between variables by calculating the Pearson coefficient. The value of this coefficient ranges from -1 to 1, where value closer to -1 indicates strong negative correlation and value closer to 1 indicates strong positive correlation.

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Table 2: Correlation analysis

Country of origin image

Quality

Design

Attractiveness

Loyalty

Country of origin image

1

0.719**

0.642**

0.452**

0.506**

Quality

0.719**

1

0.753**

0.427**

0.528**

Design

0.642**

0.753**

1

0.465**

0.500**

Attractiveness

0.452**

0.427**

0.465**

1

0.394**

Loyalty

0.506**

0.528**

0.500**

0.394**

1

Source: authors’ calculation ** Correlation is significant at the 0.01 level

The correlation matrix shows that Pearson coefficient values between each pair of variables are significant at 0.01 level, i.e. with a probability of 99%. The highest degree of linear dependence is present between variables quality and design (r = 0.753 **, p <0.01) and this is a strong correlation. The variable country -of -origin image is strongly correlated with variables quality (r = 0.719 **, p <0.01) and design (r = 0.642 **, p <0.01). Moderate correlation is present between variables quality and loyalty (r = 0.528 **, p <0.01), country -of -origin image and loyalty (r = 0.506 **, p <0.01), design and loyalty (r = 0.500, p <0.01), then design and attractiveness (r = 0.465, p <0.01), country -of -origin image and attractiveness (r = 0.452 **, p<0.01) and quality and attractiveness (r = 0.427 **, p <0.01). Finally, variables attractiveness and loyalty were correlated to the lowest degree (r = 0.394 **, p <0.01), i.e. there is a weak correlation between them.

In order to examine influence of independent variable country -of -origin image on dependent variable consumer loyalty indirectly through variables quality, design and attractiveness, regression analysis was performed. The intensity of influence of independent variable is measured by β coefficient.

Table 3: Simple regression analysis results (dependent variables: quality, design and attractiveness, respectively) Variable

β

t

sig

R2

F

Country -of-origin image

0.719

12.600

0.000*

0.518

158.770

0.642

10.200

0.000*

0.413

104.030

0.452

6.168

0.000*

0.205

38.047

Source: author’s calculation **Value is significant at 0.01 level

First, simple regression analysis was performed in order to examine effects of independent variable country -of -origin image on quality, design and attractiveness. Based on obtained results shown in Table 3, it can be concluded that variable country -of -origin image significantly affects perception of product quality, where β = 0.719, and that obtained value is statistically significant with 99% probability.

Also, 51.8% of dependent variable variability was explained by given regression model. Further, as in the previous case, country -of -origin image influences dependent variable design, where strength of that influence is β = 0.642. The value of β coefficient is significant at p<0.01 level, while 41.3% of dependent variable variability is explained by regression model. Finally, when observing influence of country image on attractiveness, it can be seen that there exists an obvious relationship (β = 0.452), as well as that obtained value of β coefficient is statistically significant at p<0.01 level.

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The observed regression model explains 20.5% of dependent variable variability. Therefore, based on the above, it can be concluded that quality is most strongly and design the weakest determined by country –of -origin image. Results confirm sub-hypotheses H1a, H1b and H1c, which ultimately indicates that first research hypothesis H1: There is a statistically significant effect of country -of -origin image on product related characteristics, has been proved.

In the next step, effects of observed product related characteristics on consumer loyalty were tested (Table 4). In order for data to be suitable for analysis in multiple regression analysis, it is necessary to meet the multicollinearity condition, which is tested by calculating the VIF coefficient value (Variance Inflation Factor), which must be less than 10 in all pairs of variables (Hair et al., 2014).

Table 4: Multiple regression analysis results (dependent variable: loyalty) Variable

β

t

sig

VIF

Quality

0.319

3.058

0.003*

2.353

Design

0.179

1.679

0.095*

2.456

Attractiveness

0.174

2.252

0.026*

1.299

Source: author’s calculation *Value is significant at 0.1 level R2=0.326; F=23.561*; (p<0.01)

Table 4 shows results of multiple regression analysis which tested effects of observed product related characteristics on consumer loyalty. The obtained data are suitable for conducting multiple regression analysis because multicollinearity condition is fulfilled, i.e. the VIF coefficient value is less than 5 in all pairs of variables. The value of determination coefficient R2 shows that 32.6% of dependent variable variability is explained by observed regression model. The obtained values of β coefficient are statistically significant at p<0.01, p<0.1 and p<0.05 level, i.e. with probabilities of 99%, 90%, and 95%, respectively.

The strongest influence on dependent variable has characteristic quality (β = 0.319), followed by design (β = 0.179), while attractiveness shows the weakest effects on dependent variable (β = 0.174). According to obtained results, it is concluded that sub-hypotheses H2a, H2b and H2c have been proven, i.e. that hypothesis H2: Product related characteristics have positive effects on development of consumer loyalty, has been proven.

Based on presented results of simple and multiple regression analysis, in the first it can be concluded that independent variable country -of -origin image is statistically significant antecedent of product quality and design and also attractiveness related to using some product. all three observed characteristics showed statistically significant effects on consumer loyalty, as well Based on the above, it is concluded that hypothesis H3: Country -of -origin image shows statistically significant effects on consumer loyalty through characteristics such as quality, design and attractiveness, has been proven.

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CONCLUSIONS

Empirical research was conducted in order to examine significance of country -of -origin perception on development of attachment, i.e. consumer loyalty, while respecting certain characteristics related to products and their usage. Results of conducted analyzes confirm direct influence of country -of -origin image on quality, design and attractiveness . If we look at the results of previous studies, it can be seen that there is an agreement with results obtained by authors such as Karimov & El-Murad (2019), Klöckner et al. (2013), Khan et al. (2012) or Kreppel & Holtbrügge (2012). It is important to point out that influence of country -of -origin image on observed characteristics differs, i.e. it is the strongest when it comes to quality, then design and the weakest when it comes to attractiveness. Then, presence of effects of observed characteristics, first quality, followed by design and attractiveness, on consumer loyalty was determined, which is in line with results of previous studies regarding presence of observed effects (Erdoğmuş & Büdeyri-Turan, 2012; Esmaeilpour, 2015; Elbedweihy et al., 2016; Mohd Suki, 2017; Kumar et al., 2018; Gómez et al., 2018; Hsu et al., 2018; Shanahan et al., 2019) but also deviates from results obtained by Šapić et al. (2018) which show that consumer loyalty is more strongly determined by design than quality. Finally, according to obtained results, it can be concluded that country -of -origin image has significant effect on consumer loyalty through characteristics such as quality, design and attractiveness, where it is possible to point out similarities with research of Šapić et al. (2018). The obtained research results fulfill existing literature and research related to observed issue and can be applied in practice to formulate appropriate marketing strategies. However, it is important to point out the existence of certain limitations. First of all, sample includes small number of respondents. Also, sample is dominated by women and younger respondents that can have an impact on certain results. The research was conducted only in Kragujevac and its surroundings, which is why opinions of respondents from other parts of the country were not taken into account. Accordingly, recommendation for future research refers precisely to overcoming these limitations, i.e. it is desirable to survey a larger number of respondents from different parts of the country where research is conducted and it is necessary to take into account structure of the sample. Also, future research can measure the effects of country -of -origin influence on consumer behavior before and after the purchase/consumption of the product.

In the pre-consumption phase, the research could reflect on the expectations and stereotypes, while in the post-consumption phase the research could focus on the overall product evaluation. Given the number of studies on country -of -origin image and its impact on product perceptions conducted in recent decades, it is safe to say that this is a concept that has significantly attracted attention not only of researchers but also all those who know that this concept can be used in business. This does not only refer to companies, but also governments. Research that deals with this issue, including research conducted in paper, show that image that consumers have of a particular country can significantly affect how they will perceive products originating from that country. Therefore, recommendation is primarily for governments to invest in improving image of their countries, because in that way they raise competitiveness of domestic economy and companies. This can be especially important for underdeveloped and developing countries, because by improving their image, they can improve their position on the international scene. Finally, it is desirable for companies to take into account results of research related to effect of country -of -origin image because it can help them in defining strategies for entering international market.

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APPENDIX

Table 5: Variables and corresponding statements Variable

Statements

Country of origin image

1. You often check country of origin when buying a certain product.

2. You prefer to buy a certain product only if it comes from a certain country.

3. When you buy a new product, the country of origin is the first information you consider.

4. If you do not have enough experience with a particular product, the country of origin helps you make the final decision to purchase that product.

5. You believe that country of origin image determines product quality.

Quality

6. The country of origin has a great influence on you when it comes to product reliability.

7. The country of origin has a great influence on you when it comes to product performance.

8. The country of origin has a great influence on you when it comes to product durability.

9. The country of origin has a great influence on you when it comes to product functionality.

Design

10. Products from a certain country are characterized by a recognizable style.

11. Products from a certain country are characterized by a large number of models and colors.

12. Products from a certain country are characterized by a recognizable slogan and logo.

13. Products from a certain country are characterized by a modern technical solutions.

Attractiveness

14. Using products from a certain country makes you more attractive in society.

15. Using products from a certain country makes you recognizable in society.

16. Using products from a certain country increases your self-confidence.

17. Using products from a certain country contributes to your style.

18. Using products from a certain country makes you feel different in society.

Loyalty

19. If you are satisfied with products from a certain country, you will continue to buy them in the future.

20. If you are satisfied with products from a certain country, you are ready to share your satisfaction with others and recommend the product.

21. If you are satisfied with products from a certain country, you are ready to pay higher price for them.

Source: authors

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EFEKAT IMIDŽA ZEMLJE POREKLA PREKO KVALITETA, DIZAJNA I ATRAKTIVNOSTI PROIZVODA NA LOJALNOST POTROŠAČA Rezime: Glavni cilj ovog istraživanja jeste da se utvrdi da li postoji uticaj imidža zemlje porekla na lojalnost potrošača proizvodima koji potiču iz zemalja pozitivnog i prepoznatljivog imidža i da li se taj uticaj ostvaruje preko karakteristika kao što su kvalitet i dizajn proizvoda kao i atraktivnost koju potrošači osećaju usled korišćenja istih. Za ispitivanje ovog uticaja sprovedeno je empirijsko istraživanje na uzorku od 150 ispitanika a analiza prikupljenih podataka izvršena je u softveru SPSS 20. Dobijeni rezultati pokazuju da je informacija o imidžu zemalje porekla proizvoda bitna potrošačima odnosno da ima uticaj na njihovo ponašanje vezano za kupovinu nekog inostranog proizvoda. Preciznije, rezultati pokazuju da imidž zemlje porekla proizvoda utiče na potrošače kada proizvode biraju na osnovu kvaliteta i dizajna istih i atraktivnosti koju potrošači osećaju kada te proizvode koriste, kao i da posredstvom ovih karakteristika imidž zemlje porekla ispoljava efekte na razvoj lojalnosti potrošača.

U skladu sa dobijenim rezultatima, nameće se zaključak da je izuzetno poželjno da preduzeća i vlade vode računa o tome kakav imidž njihove zemlje uživaju na svetskom tržištu.

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Ključne reči: imidž zemlje porekla, kvalitet proizvoda, dizajn proizvoda, atraktivnost, lojalnost potrošača.

EJAE 2021, 18(1): 151 - 172 ISSN 2406-2588 UDK: 336.748.3(4-672EU) 338.23:336.74 DOI: 10.5937/EJAE18-28714 Original paper/Originalni naučni rad

DOMESTIC CONSUMPTION AND UNCERTAINTY OF EXCHANGE RATE IN A MONETARY UNION: EVIDENCE FROM THE EURO AREA Samuel N. Okafor*, Juste S. Lokossou BaumTenpers Theoretical and Empirical Research Group, Lagaos, Nigeria

Abstract: The research seeks to uncover how real consumption reacts to real exchange rate uncertainty in the short and long run for the world’s largest monetary union- the euro zone. Twelve euro zone countries were sampled covering the period 1995Q1-2019Q4. Using generalized autoregressive conditional heteroskedasticity (GARCH) and pooled mean group (PMG), the result shows that exchange rate uncertainty significantly dampens long-run consumption while the short-run effect is mixed. In the benchmark model, a negative and significant error correction coefficient was obtained, which allows to argue that i) there is evidence of a return to the long-run equilibrium path for consumption following short run deviations and ii) the speed of adjustment to equilibrium is low, with a coefficient of ~ 4%. This suggests that, in the euro zone, convergence to long-run equilibrium is slow, as the proportion of disequilibrium corrected in one quarter, following a shock, is about 4%, which implies it would take ~17 quarters for one half of the disequilibrium, or deviations from the long-run consumption path to become corrected.

Article info: Received: October 5, 2020 Correction: October 22, 2020 Accepted: March 8, 2021

Keywords: Monetary union, euro zone, exchange rate, consumption, uncertainty, GARCH, PMG. JEL Classification: E20, E21

INTRODUCTION In January 1999, the final stage in the introduction of the European Economic and Monetary Union was initiated and subsequently completed, giving rise to the euro zone, which is a group of 19 European countries that have adopted the euro as their common currency, sole legal tender and constitute the largest monetary union in the world. This economic integration formed a unified market for goods and services, labor and capital. These countries began to enjoy seamless trade integration in addition to “import” credibility and stability, often leading to increased investment flows and lower inflation (Hegerty, 2020). *E-mail: okaforsam@gmail.com

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Consequent upon this event, several studies have been examining the economic structures and macroeconomic outcomes of this union to determine how the economies of member countries have fared over time. However, one important question, which remains unanswered, is how real consumption responds to exchange rate uncertainty in the euro zone. Studies have been carried out on the impact of exchange rate volatility on domestic consumption in individual countries (Kumar, Bhutto, Mangrio, & Kalhoro, 2019; Njindan Iyke & Ho, 2018; Bahmani-Oskooee & Xi, 2012, Mumtaz & Ali, 2020; Ang (2011); Rangvid et al., 2016; Aye & Harris, 2019). Studies in the empirical macroeconomics literature have also employed panel-based techniques to determine volatility impact of real exchange rate on real domestic consumption for panels of countries that cut across different regions, continents, or a combination of both (Oseni, 2016; Bahmani-Oskooee, & Xi, 2011; Bahmani-Oskooee, & Hajilee, 2012; Mohsen Bahmani-Oskooee & Baek, 2020; Kodama, 2013; Bahmani-Oskooee, Halicioglu & Neumann, 2018), but not much is known about the impact that real exchange rate uncertainty has on real domestic consumption in a large monetary union such as the euro area.

In this paper, we present an empirical analysis of the macroeconomic impact on real domestic consumption of uncertainty in real exchange rate in the euro zone. This enables us to fill the identified existing gap in the literature. The euro zone experienced the effects of the global financial crisis in addition to the euro zone crisis, so, both crises are taken into account to prevent bias by including them in the model specification as extra predictor variables where 1 and 0 represent the occurrence and absence of crisis throughout the quarters respectively. The results suggest that the negative long run effect of uncertainty on real consumption in the euro zone remains unchanged even after controlling for these two crisis periods.

There are several reasons to have focused on the euro zone in this paper. Firstand most importantly, the euro zone is the largest and most economically prosperous monetary union in the world. In a monetary union, one of the key advantages is risk and uncertainty sharing and an improved adjustment mechanism that acts as a shock absorber within the currency area. Some of these mechanisms include higher mobility of production factors such as labor, capital (Šovran & Hadžić, 2016) and technological transfers, together with centralized monetary policy and fiscal transfers between different parts of the union. Since risks and uncertainty are shared in a monetary union, then a plausible hypothesis is that any harmful effect of uncertainty on real consumption could be muted, limited or at worst equally shared. This potentially explains the mixed result obtained for short run in the euro area where, although there is evidence of a negative consumption effect of uncertainty, the impact can also be positive.

Second, an optimal monetary union which functions for optimal currency areas often have integrated capital markets. These integrated capital markets ensure the ease of capital flows among member countries, diminished transaction costs and lower valuation effects, given the adoption of a single currency. Therefore, in the event of negative labor market outcomes in some member countries that lead to reduced income, real consumption may not necessarily collapse as the better-performing parts of the union might generate adequate returns on invested capital that agents can use to support consumption. This outcome, which is plausible for countries in an optimal monetary union, is largely not existent for panels of countries that do not constitute a monetary union. Hence, it is possible for real consumption in a monetary union to respond differently to shocks compared to their non-monetary union counterpart. Despite this insight, most studies examining the response of real consumption to macroeconomic shocks have concentrated on countries that are not in a monetary union and nothing is known about the responses of real consumption to macroeconomic outcomes in a monetary union.

This paper addresses this gap in the literature.

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Third, compared to countries which have been studied in the literature, the euro zone, as a centralized monetary policy union, is generally not subject to concentration risk. Therefore, an economic slowdown that could dampen lending by financial institutions to the private sectors in the non-monetary unions, due to a fear of default, may in fact go hand-in-hand with more lending as the integrated financial markets continue to ensure that cross border financial institutions operating in better performing parts of the union can extend credit to worthy and more productive borrowers in the parts experiencing a slowdown,.This can possibly lead to a positiverather than a negative convergence in real consumption in the region, resulting in a different outcome for real consumption in the euro zone, at least in the short run. Itis consistent with the mixed outcomes on the short run effects of uncertainty on consumption obtained in this paper. This, again highlights why real consumption of countries in a major monetary union like the euro zone deserves a special focus as their economic outcomes may not necessarily coincide with countries that do not form a monetary union.

These three reasons motivate the resolve to address the novel question of determining how real consumption responds to macroeconomic uncertainty in the euro-zone. Hence, this research aims to investigate the impact of macroeconomic uncertainty on real consumption both on short- and long-term basis in the euro-zone. In line with this, the null research hypothesis (H0) infers that there is no significant impact of macroeconomic uncertainty on real consumption both in the short and long run for the euro-zone; while the alternative hypothesis (H1) submits that there is significant impact of macroeconomic uncertainty on real consumption both in the short and long run for the eurozone. To the researchers’ best knowledge, the specific research question and focus on the largest monetary union, the euro-zone, is new in the context of the euro area and thus, constitutes an important contribution to the literature that examines the response of real consumption to real exchange rate uncertainty and the evolution of other macroeconomic variables in the euro-zone.

A brief review of the literature is now provided below.

REVIEW OF LITERATURE Scholars have identified consumption as the most important component of demand based on several studies that analyze income, inflation and interest rate as determinants of consumption. In support of interest rate as a determinant of consumption, Bloom (2014) argued that high interest rates have an indirect relationship with investment which slows down economic activities and reduces aggregate consumption in the economy. Bahmani- Oskooee et al. (2015) also added that, the rate of exchange and its volatility are also significant determinants of consumption. Tang (2020) considered exports composition in relation to economic growth in Central and Eastern Europe. Meanwhile, Pham and Nasir (2016) investigated consumption in terms of luxury products and counterfeit markets in UK. Boguth and Lars (2013) through a cross-sectional test revealed that consumption volatility exposure is an adverse key source of risk. In the existing literature, Bernard and Ho (2019) is among the few papers that incorporated both short and long run effect into the analysis. They employed quarterly data from 1993Q1 to 2017Q4 using pooled mean group, mean group, dynamic fixed-effect, Hausman test, GARCH, Dynamic Ordinary Least square and error-corrections. The authors found that exchange rate uncertainty exerts significant detrimental influence on long-run domestic consumption; however, the short-run impact is insignificant. Among the Asian countries considered in Bahmani-Oskooee, Akhtar, Ullah and Majeed (2020), only the Philippines was exempted from short run volatility effects on import, whereas, Pakistan, Malaysia and India continued to experience the effects in the long term.

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Furthermore, they also found a mixed effect among some countries in the long term, however, countries like Pakistan, Malaysia and India showed a more significant coefficient. Arize et al. (2017) investigated the impact of real effective exchange rate on the trade balance of eight countries using different nonlinear techniques with an emphasis on the family of nonlinear auto-regressive distributed lag (NARDL) estimators. Through their approach, they showed evidence that depreciation when separated from appreciation has strong influence on trade balance by asymmetric model. Köse & Aslan (2020) analyzed the connection of exchange rate uncertainty and foreign trade performance in Turkey using structural vector autoregressive models (SVAR) with the usual restrictions. They found that domestic income and import strongly influenced export above exchange rate volatility.

Similarly, Aliyu (2010) investigated the impact of exchange rate volatility on export trade in Nigeria using monthly data for the period 1997 – 2016. The study established an indirect relationship between exchange rate volatility and export trade in Nigeria. Using the nonlinear ARDL approach, Okwu, Akpa, Oseni and Obiakor (2020) investigated the short and long run effect of oil export revenue and exchange rate on households’ consumption expenditure in Nigeria from 1981 to 2016. In their study, they found that exchange rate exerted significant positive effect on consumption both in the short and long run.

Adewuyi and Akpokodje (2013) also documented that exchange rate volatility on consumption in Africa from 1986 to2011, was significant positive and significant negative for anticipated and unanticipated depreciation, respectively. Pavlidis, Paya and Peel (2015) examined 14 OECD economies in a nonlinear framework, and rejected the null of no Granger causality from real exchange rate to real consumption. Johannes et al (2016) analyzed the effect of parameter by modeling uncertainty upon beliefs of U.S postwar aggregate consumption dynamics, and documents that confounded learning due to realistic high-dimensional learning problems, resulting in huge uncertainty over consumption dynamics that recedes gradually. Asteriou, Masatci and Pilbeam (2016) studied the short- and long-run impact of nominal and real exchange rate uncertainty on the trade volumes of MINT countries, using GARCH and ARDL techniques. They found that volatility affects import and export demands for all countries considered, apart from Mexico (in the short run) and Turkey (in the long run).

Latief & Lefen (2018) analyzed the impact of exchange rate volatility on trade and FDI in seven developing nations along “One Belt and One Road” initiative. The authors discovered that exchange rate volatility significantly hurts trade and FDI inflows. Blagov (2019) while primarily analyzing the effects of exchange rate uncertainty on import firms’ pricing behavior in the euro area, noted that exchange rate uncertainty is a key influencer of import prices in the euro area, thus, showing that increased level of exchange rate uncertainty dampens import prices on average, and that the fall is basically orchestrated by the decreased prices of intermediate goods. Kim (2017) while studying the possible impact of exchange rate volatility on Seaborne Import Volume, discovered that exchange rate created a unidirectional causality on both import volume and real income. Besides a long run causality effect on import volume, there also exists a bidirectional causality between exchange rate and exchange rate volatility.

In Olomola and Dada (2017), effect of real exchange rate and exchange rate volatility on trade balance in Sub-Saharan African countries were analyzed. The authors observed that volatility of real exchange rate encouraged trade balance in Sub-Saharan Africa and suggested that the region should utilize the positive relationship in enhancing their trade balance. Similarly, Dada (2017) examined the causal relationship between exchange rate volatility and trade balance in 13 sub-Saharan African countries from 2000-2015. The study emphasized the importance of exchange rate volatility in determining trade balance in sub-Saharan African countries. In the same vein, Nadir (2017) estimates the effect of exchange rate volatility on the international trade in Uzbekistan from 1999 to 2009.

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Aydin (2010) investigates panel data for 182 countries from 1973 to 2008 and finds different dynamics in the impact of macroeconomic fundamentals on the equilibrium real exchange rate of subSaharan African economies compared with less advanced economies. The study documented that the real exchange rate volatility has a substantial impact on the exports and imports of Uzbekistan during the period. Herve, Yao and Amzath (2010) examined the effect of real exchange rate on the balance of trade of Cote d’Ivoire from 1975 to 2007 using multivariate cointegration tests. Their results showed that real exchange rate has a significant positive influence on Cote d’Ivoire’s trade balance in both short and long run under fixed real exchange rate management policies for the observed period.

Al-Abri and Baghestani (2015) studied the impact of greater foreign investment on real exchange rate volatility, and found that greater stocks of foreign liabilities discouraged real exchange rate volatility in China, India, Malaysia, Singapore, and South Korea but encouraged real exchange rate volatility for Indonesia, Philippines, and Thailand. Yaya and Lu (2012) examined the causality between effective exchange rate and balance of trade in China with Granger causality test using monthly data from January 1994 to August 2009. The result revealed that in the short run, balance of trade causes a change in effective exchange rate but not vice versa. Bachmann, Elstner, and Sims (2013) examined the behavior of monthly uncertainty proxies and their relationships with other proxies for uncertainty adopted in the literature while cross examining the popular “wait and see” effect of uncertainty on economic activities of Germany and the US. In Soleymani and Chua (2014), the effect of exchange rate volatility was investigated on industry trade flows between Malaysia and China. The authors found that exchange rate uncertainty had positive effect on most of the industries. Bondt, Giesek, and Tujula (2020), assert that wealth increase bolsters households’ confidence and shore up consumption. Stating further that the level of household wealth is a strong determinant of choices and growth in the long term. The works of Fernandez-Villaverde et al (2011) showed that volatility of real interest rate has significant effect on consumption and other economic variables such as output, investment and hours in emerging economies.

Palumbo, Rudd and Whelan (2002) showed that using their preferred way in accounting for durable goods, their research indicated a considerable strong and stable correlation between consumption and financial wealth, whereas the traditional method produced a weaker relationship. Fostel and Geanakoplos (2012) and Bachmann and Moscarini (2012) proffered theoretical explanations for aggravating higher firm-level uncertainty due to risky behavior that results from bad times. While D’Erasmo and Moscoso Boedo (2012), and Tian (2012) suggested other possible influencers of endogenously countercyclical uncertainty.

A few studies have focused their attention on components influencing consumption and savings decisions since they in turn influence decisions on fiscal and monetary policies. The expanse of data on consumption is attributed primarily to the importance of consumption and saving decisions which are key components in economic analysis both in the short and long run. For instance, monetary policy decisions in the short run are subject to the consumption level affecting the business climate.

Furthermore, Tedongap (2015) discovered that changes in consumption volatility are mainly responsible for describing the anomalies associated with asset pricing across risk horizons. Several studies have identified consumption as the most important components of demand based on several studies which highlighted income, inflation and interest rate as consumption determinants. In Obstfeld and Rogoff (1998), exchange rates were found to inhibit real consumption in different forms, directly and indirectly.

Their perspective on direct form is that agents such as households and firms generally respond negatively to uncertainty, and this response shapes their consumption patterns. Moreover, consumption is also affected by production, trade and income uncertainties. For the indirect form, they posited that real consumption may be hindered, as firms tend to lessen exposure to risks associated with uncertainty in exchange rates through upward reviews of prices.

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In Kugler (1985), inflation rate, interest rate and income rate were used as explanatory variables for consumption of nondurables in four developed countries. Bahmani-Oskooee, Kutan and Xi (2015) ascertained whether uncertainty of exchange rate impedes consumption in selected emerging economies.

The selected countries included Armenia, Bolivia, Bulgaria, Chile, Colombia, the Czech Republic, Hungary, Malaysia, the Philippines, Poland, Russia and South Africa. The estimation technique utilized were GARCH and error correction model, using quarterly data which covered periods from 1991Q1 to 2014Q4. The study postulated that domestic consumption in most of the economies sampled was influenced by exchange rate uncertainty in the short-run but sustained long run effect was noticeable in half of the economies.

In recent times, exchange rates have begun to gain prominence in the literature as one of the key drivers of consumption due to increase in trade openness among countries (Iyke & Ho, 2018). Not many researchers analyzed exchange rate’s volatility effect in relation to real consumption. Furthermore, by focusing on only the long-run effects, they tend to undermine the short-run persistence and adjustment to equilibrium. Our paper fills this by assessing the impact of exchange rate uncertainty over real consumption of twelve countries in the euro area and accounting for both short- and long-run effects.

These countries are Austria, Belgium, Finland, France, Germany, Greece, Ireland, Italy, the Netherlands, Portugal, Slovakia, and Spain. While this paper examines the effect of exchange rate volatility on real consumption as in the previous studies, it differs from these studies in several important ways. First, it provides answers to the question on minimal and extended run impact which the volatility of exchange rate bears consumption in euro zone, a large and important monetary union which, surprisingly, previous studies have overlooked. Second, it performs robustness analysis that accounts not only for the Global Financial Crisis as in Iyke and Ho (2019), but also accounts, more importantly, for the euro zone crisis that occurred from Q3 2009 to Q2 2013 and affected the economic fortunes of euro area member countries. Hence, the study implements systematically an additional robustness check to see if the benchmark analysis remains true even after controlling for the euro zone crisis.

METHODOLOGY This study employed both the Mean Group and Pooled Mean Group approach for a panel of twelve countries to study impacts of exchange rate volatility on the Euro-zone while considering the short and long run effects. These countries include Austria, Belgium, Finland, France, Germany, Greece, Ireland, Italy, the Netherlands, Portugal, Slovakia, and Spain. Data for the twelve countries were sourced from the Federal Reserve Economic Data (FRED) and are based on data availability as these are the euro zone countries for which complete data for the variables in this analysis are available in the periods from 1995Q1 to 2019Q4.

In the literature, studies such as Mankiw (1991), Campbell and Palumbo et al. (2002), Kandil and Mirzaie (2011) and Iyke and Ho (2019) have shown that real income and interest rate are important determinants of real consumption. Therefore, these variables are included in the modelling framework.

In the benchmark model, Iyke and Ho (2019) was adopted to measure the uncertainty of exchange rate using (GARCH (1,1)) model. Accordingly, the benchmark empirical specification for the linear consumption model that will be used to examine the impact of real exchange rate uncertainty (VOL) on real consumption can be expressed explicitly as:

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(1) In the above equation, , , and represent the real consumption, real income, real interest rate, and exchange rate uncertainty of country i=1,..., N in the euro zone in quarter t =1,....,T, while ln denotes the natural logarithm operator and a1, a2 and a3 are the model coefficients to be estimated and, finally, is the random error term.

In the benchmark model, the series for all macroeconomic variables are directly observable, except for the variable which is not directly observable in itself but represents the estimated series of real exchange rate volatility obtained from a GARCH (1,1) specification in the spirit of Iyke and Ho (2019).

For each of the i=1 12 euro zone countries considered, the real exchange rates (expressed in natural logarithms) are used to generate the error terms which are i.i.d with mean of zero and variance 1. The time varying conditional variance of , which is of a known form , then represents the measure of real exchange rate uncertainty. The square of the lagged estimates of the error terms (i.e. their lagged square residuals) together with the conditional variance and their lagged values, both enter as inputs into the GARCH (1,1) specification and give rise to the uncertainty variable, , which represents the GARCH based measure of real exchange rate uncertainty.

More formally, the common logarithm of the real exchange rate for each country adheres to AR (1) process as below: (2) where represents natural logarithm for real exchange rate, c is a constant, is the autoregressive coefficient on the lagged natural logarithm of the real exchange rate and represent the error terms or i. i. d innovations with mean zero and variance 1. The conditional variance of equation (2) measures uncertainty of real exchange rate, given by , where is the -algebra generated by . Thus, . With this information, the GARCH (1, 1) model is written as: (3) where and are parameter coefficients to be estimated, is a constant deterministic term representing the lowest value that the conditional variance can achieve in any time period, is the non-time-varying, unconditional long run variance, and is some scaling factor such that .

Given , and the long-run variance can be obtained from (3) as . .

Note that one function of reach a positive level (as

, as a constant deterministic term, is to allow the conditional variance ) provided the condition is satisfied.

to

Two components of volatility were also generated using component GARCH (1,1), which is a variant of the GARCH (1,1) model. The two components resulting from the volatility decomposition of exchange rate volatility are the temporary and permanent components. The temporary component makes it feasible to examine the patterns of short run volatility and how this influences consumption; the permanent component allows for the investigation of the evolution of long run volatility and its impact on consumption. Providing this distinction between the temporary and permanent components of volatility allows one to strategically differentiate the effect of short-term uncertainty on consumption (both in the short and long run) from the effect of long-run uncertainty on consumption (both in the short and long run).

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Following Gutiérrez, Calisto and Salgado (2017), the component GARCH (1,1) which gives rise to the temporary and permanent components of volatility can be written in the form: (4) and (5) where in (4) captures the temporary exchange rate uncertainty and indicates the short-term volatility level (temporary component) that reflects near term events such as sentiment-induced innovations, driven by transitory exogenous events, and which undergo cyclical fluctuations. In (5), is the long-term volatility level (permanent component) which captures the permanent exchange rate uncertainty and is non-transitory in nature and thus much less influenced by transitory events.

Gutiérrez et al. (2017) note that the long-term volatility converges to the unconditional volatility at a level of velocity of while the term drives the dynamic movement of the longterm volatility. In the temporary component, the difference between the conditional volatility and its long-term volatility converges to zero at a velocity of .

Finally, we utilize EGARCH (1,1) model as in Nelson (1991) to obtain the volatility required to examine the potentially asymmetric effects of exchange rate uncertainty on consumption. Asymmetric information can be captured in the GARCH (1,1) based measure of exchange rate uncertainty, that is . Using an EGARCH (1,1), the uncertainty variable can be modelled thus: (6) where , , and are estimable parameters. The model depicts the links between previous shocks and the log of . For a effect we have a positive shock whereas effect gives a negative shock. The asymmetry comes into the model as, where , , and represent the coefficients of the variables. In the exponential GARCH (1,1,) model in (6), a positive shock would have an effect of ( ) on conditional volatility or exchange rate uncertainty whereas negative shocks have a corresponding impact of ( ) on conditional volatility. The potential asymmetry in how uncertainty impacts domestic consumption is captured by the coefficient on the last term, i.e. .

An asymmetric effect is said to exist in the model if . If, in addition, there is evidence that , then leverage effect is said to exist. Under leverage effect, negative shocks from bad news generate a higher volatility than positive shocks from good news.

The EGARCH (1, 1) model thus allows good news (positive shocks) and bad shocks (negative shocks) to have a distinct effect on volatility that in turn could have a distinct effect on consumption.

Having presented the benchmark model specific in equation (1) and described various measures of real exchange rate uncertainty (VOL) based on GARCH in equations (2) through (6), it is now time to formulate the pooled mean group (PMG) specification of the benchmark specification in equation (1) which would help to recover the short/long run effects of variables of real consumption, together with the corresponding error correction coefficient. Accordingly, equation (1) can be rewritten for panel using a distributed lag model (p, q, k) form as follows: 158

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(7) As Pesaran and Shin (1999) argued, it is more convenient to work with a suitable reparameterization of equation (7) above. This is because equation (7), together with the reparameterization, will enable not only the recovery of the long-run effects but also the short-run dynamics. In view of this, equation (7) is reparametrized and reformulated, as an error-correction model for consumption, as follows: (8) where is is,

a vector of the explanatory variables in the benchmark specification in equation (1); that , is the standard country-specific fixed effects and is the i.i.d error term.

Furthermore, the other parameters of the specification in equations (7) and (8) are defined as

where represents the error correction coefficient which depicts the tendency to revert to long run stable equilibrium following a system-wide shock that induces deviations from long run equilibrium to short run. For such convergence or reversion to long run equilibrium to occur, the error correction coefficient should be negative and statistically significant when estimated. Meanwhile, represents the long run coefficients which show the impact of the variables in the benchmark model of consumption (in the long run).

The adopted specification for this paper has several advantages. On the econometric front, the PMG model is suitable in several scenarios, even in instances where one seeks to study relationships between variables that are I(0) and variables that are I(1). Moreover, it is well suited for modelling panels of long dimensions where the time dimension is large or higher than the cross-section, i.e. T>N, which is the dimension of this data. On the policy front, it captures both the long run impact of exchange rate uncertainty on consumption as well as the short run dynamics. This would help to disentangle long run effect of exchange rate volatility over consumption from the short run effects. One immediate advantage is represented in the additional insights which would help to uncover the type of impact uncertainty has on consumption, - long run versus short run in euro zone. Also, it would allow to formally determine whether there is a reversion to the estimated long run equation after a shock that causes deviations from the estimated long run and to estimate the speed of adjustment to the estimated long run equilibrium in instances where such reversion is plausible.

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EMPIRICAL RESULTS

Domestic Consumption and Uncertainty of Exchange Rate The table presentsthe mean of GARCH based estimate of exchange rate volatility and their corresponding mean consumption for each nation. The parentheses consist of their rank.

Table 1: Summary statistics for exchange rate uncertainty and domestic consumption Nation

VOL(Mean)

STDEV(Mean)

Austria

0.008190(12)

0.015792(12)

Consumption(Mean) 576,000,000 (7)

Obs. 100

Belgium

0.012781(9)

0.025872(9)

6,970,000 (6)

100

Finland

0.029127(6)

0.030629(8)

355,000,000 (10)

100

France

0.009619(10)

0.034777(6)

4,140,000,000 (2)

100

Germany

0.015343(8)

0.044135(4)

5,160,000,000 (1)

100

Greece

0.038357(5)

0.035726(5)

443,000,000 (8)

100

Ireland

0.039579(4)

0.077612(2)

267,000,000 (11)

100

Italy

6.566426(1)

0.024937(11)

3,320,000,000 (3)

100

the Netherlands

0.071728(2)

0.034720(7)

1,140,000,000 (5)

100

Portugal

0.008322(11)

0.025218(10)

371,000,000 (9)

100

Slovakia

0.052800(3)

0.173715(1)

126,000,000 (12)

100

Spain

0.020797(7)

0.044359(3)

1,980,000,000 (4)

100

0.572756

0.047291

1,550,000,000

1200

All

Note: Table 1 shows the summary statistics: the positions or country ranks are in the parentheses. Mean VOL denote the mean of the exchange rate uncertainty measure computed with GARCH and STEV represent estimated standard deviation of exchange rate. Obs is the number of observations. The period covered include 1995Q1 to 2019Q4.

Table 1 revealed that Italy experienced most uncertainty followed by the Netherlandswhile the least uncertainty is found in Austria (using volatility as proxy for uncertainty). Whereas using standard deviation as proxy for uncertainty, Slovakia experiences the highest uncertainty followed by Ireland while the least was Austria. Highest real consumption (Mean) is noticed for Germany, then France, Italy, Spain, the Netherlands respectively. While the least real consumption (Mean) was found with Slovakia, followed by Ireland and Finland.

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How does Uncertainty of Exchange Rate Influence Domestic Consumption?

In estimating the benchmark model, GARCH volatility (VOL) has been used as the main baseline to assess exchange rate uncertainty, and common logarithm of consumption as the dependent variable.

The benchmark results from the PMG estimation is presented below.

Table 2: Consumption and exchange rate uncertainty (proxy with volatility) PMG Variables

Coefficients

P-value

Long run estimates In Y

0.8570***

0.0000

NIR

-0.0232***

0.0002

VOL

-0.6117***

0.0058

ECT

-0.0441**

0.0102

D(In Y)

0.437679***

0.0000

D(ln Y(-1))

0.178158**

0.0318

D(ln Y(-2))

0.110022

0.1947

D(ln Y(-3))

0.119668*

0.0934

D(NIR)

-0.003631***

0.0029

D(NIR(-1))

0.000882

0.3306

D(NIR(-2))

-0.001441

0.1945

D(NIR(-3))

-0.001692

0.2479

D(VOL)

1039.379***

0.0094

D(VOL(-1))

-0.4273

0.5579

D(VOL(-2))

-0.9712***

0.0069

D(VOL(-3))

-0.3745

0.6737

CONSTANT

0.159315

0.1826

Short-run estimates

Note: Table shows results of PMG estimation to assess the influence of different measures of exchange rate uncertainty on real consumption. The VOL coefficients are reported as per 1,000 units and *, **, *** represent statistical significance, at the 10%, 5% and 1% level. ECT represents error correction term and D () represents first difference at the different lags.

Three lags are chosen.

In Table 2, the error correction parameter estimate [ect] is both significant and acceptable at 5 percent because its value is negative and lies between 0 and 1. This indicates the presence of steady long run association between the model’s variables. Thus, errors generated over the previous period are corrected in the current period, meaning that they come together in the long run if they drift apart in the short run. Both short and long run parameter estimates indicate that uncertainty of exchange rate slows real consumption. It is important to note that the influence of estimated exchange rate uncertainty could be regarded as average effect across the sampled countries. Also, real income significantly enhances consumption in both long and short run. Meaning, a raise in real income would induce an increase in real consumption in both periods. Furthermore, natural interest rates are adversely associated with real consumption (in the long run).

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Table 3: Domestic consumption and exchange rate uncertainty (proxy with standard deviation) PMG Variables

Coefficients

P-value

Long run estimates In Y

1.154981***

0.0000

NIR

-0.004841***

0.0000

STDEV

-0.467229***

0.0002

ECT

-0.086719***

0.0134

D(In Y)

0.428757***

0.0000

D(NIR)

-0.002825***

0.0012

D(STDEV)

-0.002725***

0.0481

CONSTANT

0.635683

0.1757

Short-run estimates

Note: Table presents estimated results with standard deviation (STDEV) as alternative measure of uncertainty. *, **, *** represent statistical significance, at the 10%, 5% and 1% level. ECT represents error correction term and D () represents first difference.

Does the Effect of Exchange Rate Uncertainty Depend on the Measure of Uncertainty Employed?

The error-correction term is acceptable and significant, clearly suggesting that there is convergence.

Therefore, there is stable long-run relationship among the variables employed. Clearly, uncertainty reduces real consumption in both short and long run. Comparing baseline (volatility) and alternative measure of uncertainty (standard deviation), exchange rate uncertainty impedes long-run real consumption.

Specifically, baseline measure of uncertainty impedes real consumption more than the alternative measure. Thus, the measure of uncertainty adopted is essential in formulating policy on exchange rate uncertainty in the euro zone.

Further evaluation of the effect of exchange rate on uncertainty was estimated using DOLS estimator (dynamic ordinary least squares estimator). But estimation of short run is not permitted in this approach.

Table 4: Dynamic Ordinary Least square (DOL) estimates Model 1 Variables

Model 2

Coefficients

In Y

0.8728*** (0.0000)

0.82167*** (0.0000)

NIR

0.0003*** (0.0019)

0.0020* (0.0863)

VOL

-0.4376*** (0.0000) -0.3089*** (0.0000)

STDEV R-Square

0.998702

0.999018

Adj.

0.998686

0.998899

Note: The long-run adverse effect of uncertainty on real consumption is the same in both PMG and DOS techniques irrespective of uncertainty measure adopted. The VOL coefficients are reported as per 1,000 units and *, **, *** represent statistical significance, at the 10%, 5% and 1% level.

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Conclusively, the long run effect of exchange rate uncertainty over real consumption is negative across all countries in the euro zone irrespective of its short run effect in specific country. In the long run, the market conditions are expected to converge. The different market conditions in these countries are a major reason for the inconsistent precarious effect over consumption on the short run. In fact, consumption can be restrained by uncertainty in a country while the effect may be insignificant or cause a rise in consumption for another country within theeuro zone. Bahmani-Oskooee et al. (2015) supported these findings in their study.

Asymmetric Impact of Exchange Rate Uncertainty The reactions of economic agents towards unexpected exchange rate alterations are usually different.

This denotes that the entire economy may respond in different form to unexpected fluctuations in exchange rate. In fact, one of the spillover effects of negative exchange rate shock is higher uncertainty due to heighten anticipation of increase in speculative risk (Byrne and Davis, 2005).

Hence, GARCH (1, 1) model may be inadequate to clearly indicate asymmetric uncertainty. Thus, exponential GARCH (EGARCH) improved on this shortcoming of GARCH (1, 1). One major benefit of this technique is that it accounts for positive and negative shocks in conditional variance relating to asymmetries (Nelson, 1991).

Table 5: Effect of asymmetric uncertainty on consumption PMG Variables

Benchmark

Model with EVOL

In Y

0.8570*** (0.0000)

1.2099*** (0.0000)

NIR

0.0232*** (0.0002)

0.0061*** (0.0000)

VOL

-0.61172*** (0.0058)

-0.65915*** (0.0033)

ECT

-0.0441** (0.0102)

-0.0720 (0.1650)

D(In Y)

0.4376*** (0.0000)

0.2686*** (0.0465)

D(ln Y(-1))

0.1781*** (0.0318)

0.0093 (0.9416)

D(ln Y(-2))

0.1100 (0.1947)

0.0042 (0.9634)

D(ln Y(-3))

0.1196* (0.0934)

-

D(NIR)

-0.0036*** (0.0029)

-0.0034*** (0.0018)

D(NIR(-1))

0.0008 (0.3306)

-4.7075 (0.8745)

Long run estimates

Short-run estimates

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Variables

Benchmark

Model with EVOL

D(NIR(-2))

-0.0014 (0.1945)

-0.0033** (0.0150)

D(NIR(-3))

-0.0016 (0.2479)

-

D(VOLT)

1.03937*** (0.0094)

-0.02218 (0.4645)

D(VOLT(-1))

-0.42738 (0.5579)

-0.03284 (0.1242)

D(VOLT(-2))

0.971177*** (0.0069)

-0.025127 (0.3346)

D(VOLT(-3))

0.37454 (0.6737)

-

CONSTANT

0.1593 (0.1826)

0.9703 (0.1675)

Short-run estimates

Note: Estimated results use the asymmetric measure of uncertainty, EVOL. Table 5 displays the coefficients’ estimates. The log Consumption, lnC is the dependent variable. While log of income (lnY), log of natural interest rate (lnNIR), and log of volatility (lnVOL) are the independent variables. In parentheses are the p-values; D and ECT represent the first difference estimator and the error-correction term respectively. The VOLT (that is, VOL and EVOL) coefficients are reported as per 1,000 units and *, **, *** represent statistical significance, at the 10%, 5% and 1% level and D() represents first difference at the different lags. Three lags are chosen.

The result shows that the influences of uncertainty on real consumption increase after incorporating the asymmetries effect into the exchange rate uncertainty measure. Other variables exert similar effect as before.

Does Controlling for the Effect of Euro and Global Financial Crisis Influence the Results?

So far, the study has ignored the occurrence of two major crises – the global financial crisis (2007-2009) and the Euro zoneeuro zone crisis (2009-2013) in the analysis. This is because in the last decade, the euro zone experienced not only the global financial crisis which emanated from the US but also the euro zone crisis which sprang from within. Hence, failing to factor these crises into the empirical analysis might generate spurious and/or biased outcomes. Accordingly, to ensure that the result is not being driven by any of these crises, especially the euro zone crisis, which emanated from the euro area and had notable influence on member countries, these crises were controlled for by sequentially including them in the empirical specification as additional predictor variables. In this section, these crises periods are incorporated into the analysis in order to perform robustness checks to determine whether controlling for these crises periods alters the main results. To do this, a dummy variable was created which takes a value of 1 in any quarter that falls into the crisis period and zero otherwise. The results are presented in Table 6 below.

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Table 6: Consumption and exchange rate uncertainty (controlling for the global financial crisis and Euro financial crisis) PMG Variables

Benchmark

Model with GFCDUM

Model with EURODUM

Long run estimates In Y

0.8570*** (0.0000)

0.94024*** (0.000)

1.2351*** (0.0000)

NIR

0.0232*** (0.0002)

0.033545*** (0.000)

0.0180*** (0.0001)

VOL

-0.61172*** (0.0058)

-0.24348** (0.0158)

-14.5145*** (0.0021)

ECT

-0.0441** (0.0102)

-0.0123** (0.0456)

-0.0021** (0.0457)

D(In Y)

0.4376*** (0.0000)

0.4643*** (0.0005)

0.4148*** (0.0000)

D(ln Y(-1))

0.1781** (0.0318)

0.2099** (0.0357)

0.1612** (0.0255)

D(ln Y(-2))

0.1100 (0.1947)

0.1076 (0.1919)

0.1592** (0.0378)

D(ln Y(-3))

0.1196* (0.0934)

0.1045 (0.956)

0.1209** (0.0437)

D(NIR)

-0.0036*** (0.0029)

-0.0036*** (0.0019)

-0.0031*** (0.0084)

D(NIR(-1))

0.0008 (0.3306)

0.0007 (0.4207)

0.0006 (0.3750)

D(NIR(-2))

-0.0014 (0.1945)

-0.001394 (0.2437)

-0.0011 (0.2772)

D(NIR(-3))

-0.0016 (0.2479)

-0.0018 (0.1942)

-0.0033 (0.0282)

D(VOL)

1.03937*** (0.0094)

1.01206*** (0.0143)

0.651893 (0.1165)

D(VOL(-1))

-0.427308 (0.5579)

-0.507967 (0.4471)

-0.543261 (0.4519)

D(VOL(-2))

0.971178*** (0.0069)

1.01497*** (0.0065)

0.790107 (0.6654)

D(VOL(-3))

0.374540 (0.6737)

0.37002 (0.6628)

0.389401 (0.6654)

Short-run estimates

GFCDUM

-

0.00307 (0.2977)

EURODUM

-

-

CONSTANT

0.1593 (0.1826)

0.121489 (0.2474)

-0.010655 0.052225

Note: The table presents PMG estimation results controlling for global financial crisis (GCF) and euro crisis (EURO) in the baseline model. The VOL coefficients are reported as per 1,000 units and *, **, *** represent statistical significance, at the 10%, 5% and 1% level. ECT represents error correction term and D () represents first difference at the different lags. Three lags are chosen.

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The error-correction coefficient is significant and acceptable in both GCF and EURO models.

Comparatively, the uncertainty effect of exchange rate on consumption over the long-run is similar directionally to that of baseline after controlling for the global financial crisis and euro zone crisis.

Specifically, the spillover effect of euro crisis on exchange rate uncertainty is significant and higher than the effect of global financial crisis (GFC). In fact, GFC dummy variable is statistically insignificant.

Incorporating the effect of euro crisis increase the influence of real income on consumption above unity and reduce the impact of natural interest rate below the baseline effect.

What Component of Uncertainty Matter?

Clearly, exchange rate uncertainty exerts adverse long-run influence on consumption. However, policy maker would be more interested in which component of uncertainty influences consumption.

Literature revealed that there are different spillover effects of temporary and permanent component on the economy. Table 7a&b shows the results using both components.

Table 7a: Permanent component Variable

Coefficient

t-Statistic

Prob.*

Long run estimates In Y

0.803125***

12.11153

0.0000

NIR

0.017363***

4.528307

0.0000

VOL

-1.3553***

-3.154974

0.0017

ECT

-0.02206*

-1.679548

0.0934

D(LCSM(-1))

-0.260389**

-3.736182

0.0002

D(LCSM(-2))

-0.085996

-0.945545

0.3446

D(LCSM(-3))

-0.059102

-0.717874

0.4730

D(LY)

0.448984***

4.735304

0.0000

D(LY(-1))

0.202969**

2.200436

0.0280

D(LY(-2))

0.124116

1.319478

0.1873

D(LY(-3))

0.104852

1.298388

0.1945

D(NIR)

-0.003288**

-2.422490

0.0156

D(NIR(-1))

0.000692

0.726370

0.4678

D(NIR(-2))

-0.000954

-0.890154

0.3736

D(NIR(-3))

-0.001929

-1.331765

0.1833

D(VOLT)

0.015158

0.102918

0.9180

D(VOLT(-1))

-0.028507***

-2.966296

0.0031

D(VOLT(-2))

-0.024891**

-1.998081

0.0460

D(VOLT(-3))

-0.017876

-1.185565

0.2361

CONSTANT

0.298017*

1.670917

0.0951

Short-run estimates

Note: Summary of results of permanent component. VOLT (i.e., permanent or temporary component VOL: here it is permanent). Table 7a displays the coefficients’ estimates. The log Consumption, lnC is the dependent variable. While log of income (lnY), log of natural interest rate (lnNIR), and VOLT are the independent variables. In parentheses are the p-values; D and ECT represent the first difference estimator and the error-correction term respectively. The VOLT coefficients are reported as per 1,000 units and *, **, *** represent statistical significance, at the 10%, 5% and 1% level and D () represents first difference at the different lags. Three lags are chosen.

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Permanent and temporary uncertainties negatively influence long-run real consumption. However, in terms of magnitude, permanent component hurts consumption more than temporary component.

Table 7b: Temporary component Variable

Coefficient

t-Statistic

Prob.*

Long run estimates LY

0.467694***

7.360383

0.0000

NIR

0.006481***

3.370931

0.0008

VOLT

-0.703627***

-2.916604

0.0036

COINTEQ01

-0.030772**

-2.043435

0.0413

D(LCSM(-1))

-0.270206***

-3.704317

0.0002

D(LCSM(-2))

-0.101201

-1.110614

0.2670

D(LCSM(-3))

-0.071257

-0.859314

0.3904

D(LY)

0.460068***

4.585638

0.0000

D(LY(-1))

0.212006**

2.102951

0.0357

D(LY(-2))

0.150340

1.550975

0.1212

D(LY(-3))

0.137247

1.518404

0.1292

D(NIR)

-0.003606***

-2.822587

0.0049

D(NIR(-1))

0.000663

0.749701

0.4536

D(NIR(-2))

-0.000930

-0.912645

0.3617

D(NIR(-3))

-0.001745

-1.288553

0.1979

D(VOLT)

0.02648**

1.991256

0.0467

D(VOLT(-1))

-0.00277

-0.358272

0.7202

D(VOLT(-2))

-0.00446

-0.362929

0.7167

D(VOLT(-3))

-0.00540437

-0.322389

0.7472

CONSTANT

-0.361152

-1.129494

0.2590

Short-run estimates

Note: Summary of results of permanent component. VOLT (i.e., permanent or temporary component VOL: here it is temporary) Table 7a displays the coefficients’ estimates. The log Consumption, lnC is the dependent variable. While log of income (lnY), log of natural interest rate (lnNIR), and VOLT are the independent variables. In parentheses are the p-values; D represent the first difference estimator. The VOLT coefficients are reported as per 1,000 units and *, **, *** represent statistical significance, at the 10%, 5% and 1% level and D() represents first difference at the different lags. Three lags are chosen.

CONCLUSION Consumption is a crucial element of aggregate demand which influences other macroeconomic variables that affects growth and policy decisions. This paper has examined the impact of exchange rate volatility on real consumption in the euro zone from1995Q1 to 2019Q4. GARCH (1, 1) was used to assess for uncertainty while EGARCH was used to assess uncertainty asymmetry, and component GARCH was used to obtain the permanent and temporary components of exchange rate volatility.

In the benchmark model, pooled mean group (PMG) was employed to show the presence of a stable long run impact of unstable exchange rate over consumption in euro zone. However, a long run parameter estimates indicate that exchange rate uncertainty is an impediment for real consumption in the long run, while a mixed effect in the short run.

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This provides evidence to support that uncertainty impact on consumption in the euro zone is not the same in direction in the short and long run. Also, permanent uncertainty hurts consumption almost two times more than temporary uncertainty, which lends support to recent findings, for example Iyke and Ho (2019), that permanent uncertainty hurts more than temporary uncertainty. Also, real income significantly enhances consumption in both long and short run implying that, a raise in real income would induce an increase in real consumption in both periods.

By adopting the PMG estimator, the result shows that, in the euro zone, exchange rate uncertainty dampens real consumption in both long and short runs. It can be observed in the benchmark model that the error-correction coefficient is negative and highly significant, at 1% conventional statistical level, lending support to the reality of a long run impact of uncertainty of exchange rate on real consumption.

The error correction coefficient value of -0.044 suggests that convergence/return to the long-run equilibrium path for consumption following an economic-wide shock (short run deviations) appears to be slow, as the proportion of disequilibrium corrected in one quarter is about 4%, which implies it would take close to 17 quarters for one half of the disequilibrium (or deviations from the long run real consumption) in the eEuro zone, to become corrected.

To ensure that the results in the benchmark model are not fully reliant on the measure of uncertainty adopted, quarterly standard deviations is used as an alternative measure of uncertainty. Even with this measure, a negative long run link between uncertainty and consumption, continues to emerge, while a negative effect now dominates in the short run. Thus, while the long run effect of uncertainty on real consumption is adverse in the eEuro zone, irrespective of the measure of uncertainty adopted, there is evidence that the short run effect can be mixed. Hence, it can be conclusively said that the alternative hypothesis (H1): that there is significant impact of macroeconomic uncertainty on real consumption both in the short and long run for eurozone is accepted while null hypothesis (H0): that there is no significant impact of macroeconomic uncertainty on real consumption both in the short and long run for eurozone is rejected. That is irrespective of the mixed direction for short run as well as the negative direction for long run, macroeconomic uncertainty significantly impacts real consumption both in the short and long run for the eurozone.

Although both components require policy attention, policymakers should focus more on permanent uncertainty as evidenced from our results. As the results suggest, the dampening exchange rate uncertainty impact on consumption in the eEuro zone is mostly a long run issue, one important policy implication is that eEuro zone policy makers would do well to focus relatively less on uncertainty in the short run, but instead place much more emphasis on minimizing the dampening long-run impact of exchange rate uncertainty. One way to do this is to concentrate attention and resources on minimizing the permanent uncertainty component of uncertainty given that it has a considerably larger dampening effect on long-run consumption.

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DOMAĆA POTROŠNJA I NEIZVESNOST DEVIZNOG KURSA U MONETARNOJ UNIJI: DOKAZI IZ EVROZONE Rezime: Istraživanje pokušava da otkrije kako realna potrošnja kratkoročno i dugoročno reaguje na neizvesnost deviznog kursa za najveću svetsku monetarnu uniju - evrozonu. Dvanaest zemalja evrozone je uzorkovano za period 1995Q1-2019Q4. Koristeći generalizovanu autoregresivnu uslovnu heteroskedastičnost (GARCH) i objedinjenu srednju grupu, rezultati pokazuju da nesigurnost deviznog kursa značajno umanjuje dugoročnu potrošnju dok je kratkoročni efekat pomešan. U referentnom modelu dobijen je negativan i značajan koeficijent korekcije greške, što omogućava argument da i) postoje dokazi o povratku na dugoročni ravnotežni put potrošnje nakon kratkoročnih odstupanja i ii) brzina prilagođavanja ravnoteži je niska, sa koeficijentom od ~ 4%. To sugeriše da je u evrozoni konvergencija ka dugoročnoj ravnoteži spora, jer je procenat neravnoteže korigovane u jednom kvartalu, nakon šoka, oko 4%, što znači da bi za polovinu neravnoteža ili odstupanja od dugoročne putanje potrošnje koja treba da se isprave trebalo ~17 kvartala.

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Ključne reči: Monetarna unija, evrozona, devizni kurs, potrošnja, nesigurnost, GARCH, PMG. Klasifikacija jela: E20, E21

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ТНЕ EUROPEAN Vol.18 N° 1 APRIL2021 journal.singidunum.ac.rs

OF APPLIED ECONOMICS Measuring DistriЬution of lntellectual Capital Components Contribution: French Context рр. 1-14 Monetary Polic y and Bank Risk-Taking in Sub-Sahara Africa рр. 15-38

Econometric Examination of the Impact of lncome on Household Expenditures for Package Holidays in SerЬia рр. 39-54

The Impact of the Metacognitive and Behavioral Factors of Cultural Intelligence on Foreign Brand Acceptance рр. 73-88

Market Reactions to Football Мatch Results: The Effect ofVennes and Competition Types рр. 55-72

The VulneraЫe Financial lssue: Capital Flight in Indonesia рр. 89-105

1s There any Govemment Debt Threshold in Four Selected Central European Countries! рр. 126-136

Domestic Consumption and Uncerta Domestic Consumption and Uncertainty of Exchange Rate in а Monetary Union: Evidence from the Euro Area рр. 151-172

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The Enterprise Potential, Individual Entrepreneurial Orientation and Entrepreneurial Intentions of Students in SerЬia рр. 106-125

The Effect of Country of Origin Image Trough Quality, Design and Attractiveness Related to Product on Consumer Loyalty рр. 137- 150