European Journal of Applied Science - Vol 17 No 1

Vol. 17 Nº 1

journal.singidunum.ac.rs

Vol. 17 Nº 1 APRIL 2020 journal.singidunum.ac.rs

2020

Determinants of Profitability of the Agricultural Sector of Vojvodina: The Role of Corporate Income Tax pp. 1-19

Herd Behaviour in the Cryptocurrency Market: Fundamental vs. Spurious Herding pp. 20-36

Potential Effects of Cryptocurrencies on Monetary Policy pp. 37-48

Impact of the Business Sector on Children’s Rights in Serbia pp. 49-66

Have Export Compositions Influenced Economic Growth of the European Union Countries in Central and Eastern Europe? pp. 80-103

An Analysis of North Korean Trade Amid Warming Global Relations Utilizing RCA, RSCA and TBI pp. 113-127

Does Unemployment Lead to Criminal Activities?

An Empirical Analysis of CEE Economies pp. 104-112

Te Influence of Human Resources on the Development of Leading Tourism Destinations in Serbia pp. 128-145

Is there Market Power in the U.S. Brewing Industry? pp. 67-79

Vol. 17 No. 1

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Vol. 17 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 Emeritus Slobodan Unković, Singidunum University, Serbia sunkovic@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 Verka Jovanović, Singidunum University, Serbia vjovanovic@singidunum.ac.rs 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 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 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 Nemanja Stanišić, Singidunum University Gordana Dobrijević, Associate Professor, Singidunum University Jovana Maričić, Singidunum University Marijana Prodanović, Assistant Professor, Singidunum University

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

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CONTENTS

1 - 19

20 - 36 37- 48

49 - 66 67 -79 80 - 103 104 - 112 113 - 127 128 - 145

Determinants of Profitability of the Agricultural Sector of Vojvodina: The Role of Corporate Income Tax Stefan Vržina, Miloš Dimitrijević

Herd Behaviour in the Cryptocurrency Market: Fundamental VS. Spurious Herding Chamil W. Senarathne, Wei Jianguo

The Potential Effects of Cryptocurrencies on Monetary Policy Nenad Tomić, Violeta Todorović, Božidar Čakajac

The Impact of the Business Sector on Children's Rights in Serbia

Nataša Krstić, Sandra Nešić

Is There Market Power in the U.S. Brewing Industry?

Sanjib Bhuyan

Have Export Compositions Influenced Economic Growth of the European Union Countries in Central and Eastern Europe?

Donny Tang

Does Unemployment Lead to Criminal Activities? An Empirical Analysis of CEE Economies Nemanja Lojanica, Saša Obradović

An Analysis of North Korean Trade amid Trade Warming Global Relations Utilizing RCA, RSCA, and TBI Aaron Rae Stephens, Ramin Kasamanli

The Influence of Human Resources on the Development of Leading Tourism Destinations in Serbia Nikolina Kordić, Snežana Milićević

III

EJAE 2020, 17(1): 1 - 19 ISSN 2406-2588 UDK: 338.432:330.322.5(497.113) 336.226.14 DOI: 10.5937/EJAE17-21368 Original paper/Originalni naučni rad

DETERMINANTS OF PROFITABILITY OF THE AGRICULTURAL SECTOR OF VOJVODINA: THE ROLE OF CORPORATE INCOME TAX Stefan Vržina*, Miloš Dimitrijević Faculty of Economics, University of Kragujevac, Serbia

Abstract: Due to considerable share in total employment and foreign trade exchange, agriculture represents an important sector of the Serbian economy. It is, therefore, necessary to continuously analyze the financial performance of agricultural companies and key determinants of financial performance. The objectives of this paper are to analyze the corporate income tax burden of agricultural companies in Vojvodina, as well as its impact on company profitability. Statistical tests showed that effective corporate income tax rates (ETRs) in agricultural companies are significantly lower than the statutory corporate income tax rate.

Furthermore, nearly 69% of observations have both a current ETR and cash ETR of 0%, which indicates that agriculture is an industry with an exceptionally low corporate income tax burden. Panel regression showed that agricultural companies with lower ETRs are more profitable than companies with higher ETRs. Results of the analysis are not sensitive to changes in corporate income tax burden and profitability proxies.

Article info: Received: April 16, 2019 Correction: December 2, 2019 Accepted: December 4, 2019

Keywords: agriculture, profitability, corporate income tax, Vojvodina.

INTRODUCTION Despite greater focus on the sector of information and communications technology, the agricultural sector still represents an important part of Serbian economy. Atanasijević and Danon (2014) stress that Serbia has a large agricultural sector, with high-quality arable land and a favorable continental climate.

Mitrović, Mitrović, and Cogoljević (2017) argue that the share of agriculture among total employment in Serbia is still significant and that agriculture is a very important factor in the country’s foreign trade exchange. On the other hand, agriculture is one the most criticized industries within the context of failed privatization processes (Radulović, & Dragutinović, 2015, p. 227). Stanojević, Krstić, and Đekić (2015) emphasize the need for a further increase in agricultural production and productivity, primarily through improvement of technological equipment of agricultural companies. Therefore, it is necessary to continuously assess the profitability of agricultural companies in Serbia. *E-mail: stefan.vrzina@kg.ac.rs

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EJAE 2020  17 (1)  1-19

VRŽINA, S., DIMITRIJEVIĆ, M.  DETERMINANTS OF PROFITABILITY OF THE AGRICULTURAL SECTOR OF VOJVODINA: THE ROLE OF CORPORATE INCOME TAX

Companies naturally strive to avoid corporate income tax liabilities in order to become more profitable.

Andreoni, Erard, and Feinstein (1998) point out that tax avoidance practices are as old as taxes themselves.

However, in practice it is not easy to distinguish between legal tax avoidance and illegal tax evasion. As a result, in theory and practice, a new term is defined – tax avoision – to describe situations in which tax law does not clearly declare the legality of certain tax practices (Prinz, Muehlbacher, & Kirchler, 2014).

The research subject of this paper is corporate income tax as a determinant of profitability of agricultural companies in Vojvodina. This geographic region was chosen for analysis since (as of December 31st , 2016) nearly 53% of the agriculture, forestry, and fishing sector companies in Serbia are headquartered in Vojvodina (according to the Statistical Office of the Republic of Serbia, 2017). This research area could be significant, since an imposed moderate flat statutory corporate income tax rate (STR) is 15% in Serbia, with many options available for the reduction of effective corporate income tax rates (ETR).

There are two main objectives to this paper. The first objective is to measure the corporate income tax burden of agricultural companies in Vojvodina, while the second is to examine whether the profitability of agricultural companies in Vojvodina is significantly influenced by corporate income tax, i.e., whether agricultural companies with lower corporate income tax burdens are more profitable than companies with higher corporate income tax burdens.

Corporate income tax burden and profitability are proxied with many indicators in order to test the sensitivity of results to change of the proxies – corporate income tax burden is proxied with current ETR and cash ETR, while profitability is proxied with ROA (Return on Assets), ROE (Return on Equity), and ROS (Return on Sales).

The impact of corporate income tax on company profitability may seem trivial, since tax expense reduces the income available for reinvestment or distribution to the owners. However, when using current and cash ETR, such impact depends on company tax avoidance strategy. If a company uses temporary book-tax differences to avoid taxes, employed ETRs have no impact on profitability, as deferred corporate income tax expenses offsets reduction in current corporate income tax expense. On the other hand, if a company uses permanent book-tax differences, employed ETRs impact profitability, since deferred corporate income tax expense does not offset permanent differences.

An empirical analysis on the sample of 50 agricultural companies across a four year period was conducted. Considering empirical orientation of the research, inductive approach is dominant in this paper. Furthermore, the following statistical methods were employed: descriptive statistics, tests of equality, correlation analysis, and panel regression analysis.

This study contributes to the relevant literature in several ways. It contributes to the existing literature on the profitability of Serbian agricultural companies, which has been studied from various aspects but not from the tax aspect. In addition, it contributes to the existing foreign literature on the influence of corporate income tax on company profitability.

The originality of the research is reflected in the fact that this is, to authors’ knowledge, the first research that includes tax aspects in the analysis of profitability of agricultural companies in Vojvodina.

Excepting the introduction, conclusion, and appendix, the paper consists of three parts. The literature review and hypotheses development are given in part 1. Sample description and research model are shown in part 2. Results of the empirical research are given in part 3.

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EJAE 2020  17(1)  1-19

VRŽINA, S., DIMITRIJEVIĆ, M.  DETERMINANTS OF PROFITABILITY OF THE AGRICULTURAL SECTOR OF VOJVODINA: THE ROLE OF CORPORATE INCOME TAX

LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT The profitability of agricultural companies in Serbia and other transitional economies has been studied from various aspects in the past decade. Birovljev, Đokić, Matkovski, and Kleut (2017) argue that the performances of the agricultural sector in CEFTA (Central European Free Trade Agreement) countries are far from the performances in the European Union (EU). Jakšić, Zekić, Ristić, and Mijić (2016) find that the profitability of agricultural companies in the EU is considerably higher than the profitability of agricultural companies in non-EU Southeastern European countries, though countryspecific characteristics can be more important determinants of profitability than EU membership.

On the examples of the Slovakian and Hungarian agricultural sector, Chrastinova and Burianova (2009) and Miklos (2014), respectively, argue that EU membership can improve the productivity and profitability of agriculture through Common Agricultural Policy subsidies, although agriculture performances in these countries are still far from its main competitors in Western Europe. Kocsis and Major (2017) argue that Polish, Hungarian, and Czech agricultural sectors suffer from a lack of capital and unfavorable loan conditions, despite EU membership.

Previous research find that the global economic crisis significantly hit the agricultural sector of Serbia and Vojvodina. Zekić, Gajić and Kresoja (2012) find that first decade of the 21st century in Serbia was marked not only by growth in agricultural production and productivity, but by the global crisis as well. Vukoje and Zekić (2010) add that positive trends in the agricultural sector of Vojvodina disappeared in 2008 and 2009, partially due to the global crisis. After that, between 2010 and 2015, agricultural companies from Vojvodina recovered from losses and achieved modest profit rates (Vukoje, & Dulić, 2017). Bubić and Hajnrih (2012) conclude that the global crisis has only deepened existing problems of bad privatizations of agricultural companies in Vojvodina and its failure to restrict the level of companies’ debt.

Vučković (2016) stresses that agricultural companies in Vojvodina, despite having similar arable land and operating in the same same geographic area, might have varying profitability due to differences in their financial structures, asset structures, activity ratios, and liquidity. Vučković, Veselinović, and Vučković (2017) add that higher level of owners’ equity in total assets and higher liquidity ratio positively influence the profitability of agricultural companies in Vojvodina.

Vukoje and Vukelić (2010) argue that agricultural companies in Vojvodina are increasingly engaged in trade activities in pursuit of profits. They find that the costs of sold merchandise increase while the costs of production materials decrease in agricultural companies over the years. On the other hand, Mijić and Jakšić (2016) find that trade activities are among the most profitable economic activities in Serbia.

Agricultural companies in Serbia are influenced by several specific factors. Đuričin and Bodroža (2013) point out that drought and other meteorological extremes worsened the profitability of agricultural companies in Serbia between 2007 and 2010. In terms of profitability, Vukoje, Miljatović, and Zoranović (2017) argue that, in Vojvodina, most problems are found among micro and small-sized agricultural companies, while medium and large-sized companies are far more successful.

On the other side, previous research that studied the impact of corporate income tax on the financial performance of the agricultural sector is scarce. Crocker and Slemrod (2005) show abundant evidence that the focus of companies has changed from passive compliance with tax laws to active and often aggressive tax avoidance and tax planning. Furthermore, contemporary companies implement advanced tax strategies that capture their tax expense minimization, compliance requirements, and public demonstrations of “fair” share of taxes paid (Hogsden, 2018).

Agricultural companies studied in this paper follow International Accounting Standards (IAS), International Financial Reporting Standards (IFRS), or IFRS for small and medium-sized enterprises.

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EJAE 2020  17 (1)  1-19

VRŽINA, S., DIMITRIJEVIĆ, M.  DETERMINANTS OF PROFITABILITY OF THE AGRICULTURAL SECTOR OF VOJVODINA: THE ROLE OF CORPORATE INCOME TAX

According to these standards, companies report current and deferred corporate income tax in their income statement. Dhaliwal, Gleason and Mills (2004) argue that management of companies can adjust their current and deferred corporate income tax expense in order to maximize profitability of their companies.

Empirical research on the relation between corporate income tax and profitability is relatively scarce since researchers from the most developed countries are primarily interested in the influence of corporate income tax on company value, usually proxied with Tobin’s Q. Therefore, the majority of the research on the influence of corporate income tax on profitability have been conducted in developing countries with lower efficiency and liquidity of stock markets.

Previous research predominantly find significant a positive impact of legal minimization of corporate income tax expense on company profitability. Gatsi, Gadzo, and Kportorgbi (2013), Assidi, Aliani, and Omri (2016) and Pitulice, Stefanescu, Minzu, Popa, and Niculescu (2016) studied Ghanaian, Tunisian, and Romanian companies, respectively. They found that corporate income tax is statistically significant determinant of company profitability – a reduction of ETR leads to a significant increase of company profitability. In the context of Serbia, Vržina (2018) studied companies listed on the Belgrade Stock Exchange and found a significant negative impact of ETR on company profitability.

Al-Jafari and Al Samman (2015) studied companies in Oman and found no statistically significant influence of corporate income tax on company profitability. Contrary to economic logic, Ezugwu and Akubo (2014) found that profitability of Nigerian companies increases with an increase in ETR. It is important to notice that STRs in most of the previously mentioned research (Gatsi et al., 2013; Ezugwo, & Akubo, 2014; Assidi et al., 2016; Pitulice et al., 2016) were higher than current STR in Serbia (15%). Moreover, in the research of Al-Jafari and Al Samman (2015) and Assidi et al. (2016) ETR was, on average, lower than STR. Considering previous research results, the following research hypotheses are formulated: H1: Effective corporate income tax rate (ETR) in agricultural companies in Vojvodina is significantly lower than statutory corporate income tax rate (STR).

H2: Agricultural companies in Vojvodina with lower effective corporate income tax rate (ETR) are more profitable than agricultural companies with higher ETR.

SAMPLE AND RESEARCH MODEL The research sample comprises agricultural companies (under Eurostat activity codes 011 – Growing of non-perennial crops, and 012 – Growing of perennial crops) headquartered in Vojvodina, active in the period between 2013 and 2016. The sample comprises only stock companies and limited liability companies as the two most frequent legal forms in agriculture, forestry, and fishing sectors of Serbia (as of December 31st , 2016, Statistical Office of the RS, 2017, p. 213). In addition, micro-sized agricultural companies,1 according to Accounting Law (The Official Gazette of the RS, 30/2018), are not included in the sample.

Companies without audited 2016 statutory financial statements, as well as companies with adverse audit opinion or disclaimers of audit opinion, are not considered in order to ensure reliability of financial data. Finally, only companies with a pre-tax profit in minimum of two years between 2013 and 2016 are sampled in order to avoid negative ETRs as much as possible.

1 Micro-sized companies are dominant in the agriculture, forestry, and fishing sectors of Vojvodina (Statistical Office of the Republic of Serbia, 2017). However, financial statements of these companies are rarely audited, and often contain only basic data from balance sheet and income statement, without any additional data (for example, from cash flow statements)

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EJAE 2020  17(1)  1-19

VRŽINA, S., DIMITRIJEVIĆ, M.  DETERMINANTS OF PROFITABILITY OF THE AGRICULTURAL SECTOR OF VOJVODINA: THE ROLE OF CORPORATE INCOME TAX

There are 50 companies found to meet the mentioned criteria. The sample initially comprises 200 observations (50 companies across four years). However, four observations were withdrawn due to over-indebtedness, i.e., losses that exceed the owners’ equity (in order to calculate ROE, and to avoid abnormal debt ratios), while eight observations were withdrawn due to pre-tax losses in order to avoid negative ETRs. Therefore, the final sample comprises 188 observations which is, according to Tabachnick and Fidell (2007, p. 123), an appropriate sample size.2 Table 1 presents the structure of sampled companies. The list of sampled companies is given in Appendix 1.

Table 1 - Structure of Sampled Companies Administrative district Central Banat

8

South Banat

9

North Bačka

5

Srem

3

North Banat

4

West Bačka

8

South Bačka

13

Total

50

Legal form (as of December 31 , 2016) st

Public stock company

22

Limited liability company

26

Private stock company

2

Total

50

Size (according to 2016 statutory financial statements) Small

14

Large

4

Middle

32

Total

50

Audit opinion (from 2016 statutory financial statements) Unqualified

26

Qualified

4

Unqualified with emphasis of matter

12

Qualified with emphasis of matter

8

Total

50

The ownership of sampled companies is highly concentrated. As of January 1st, 2018, one company from Belgrade is the direct majority shareholder in five companies, one company from Novi Sad is, through related-party entities, the majority shareholder in another five companies, while one company from Sombor is the direct majority shareholder in four additional sampled companies. Furthermore, only 11 companies have 2016 statutory financial statements audited by Big Four companies.

In this research, there is examined influence of corporate income tax on the profitability of agricultural companies. Current ETR (CuETR) and cash ETR (CaETR) are used as corporate income tax burden proxies, and ROA, ROE and ROS as profitability proxies. According to Hanlon and Heitzman (2010), current ETR and cash ETR are among the most widely used corporate income tax burden proxies. Figure 1 presents the research model.

Figure 1 - Research Model

{

CuETR

CaETR

Corporate income tax burden

-

Profitability

{

ROA ROE ROS

2 Tabachnick and Fidell (2007, p. 123) suggest a minimum number of observations to be calculated as follows: N > 50 + 8m, whereas N refers to the minimum number of observations and m refers to the number of independent variables. Since there are nine independent variables employed in the research model, the minimum sample size is 122 observations.

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EJAE 2020  17 (1)  1-19

VRŽINA, S., DIMITRIJEVIĆ, M.  DETERMINANTS OF PROFITABILITY OF THE AGRICULTURAL SECTOR OF VOJVODINA: THE ROLE OF CORPORATE INCOME TAX

Following the conclusion of Vučković (2016) about the influence of financial structure, asset structure, activity ratios, and liquidity on the profitability of agricultural companies, the following control variables are used: debt ratio (DEBTR), share of fixed assets in total assets (FIXED), asset turnover ratio (ATR) and liquidity ratio (LIQ). In accordance with Assidi et al. (2016), company size (SIZE) is used as control variable. Following Gatsi et al. (2013), the age of the company (AGE) is used as a control variable. In accordance with Vržina (2018), gross domestic product growth rate (GDP) and inflation rate measured by consumer prices index (INFL) are used as macroeconomic control variables. Table 2 presents the definition of the employed variables.

Table 2 - Definition of Variables Variable

Definition

Dependent variables ROA (%)

(Net profit / Total assets) x 100

ROE (%)

(Net profit / Equity) x 100

ROS (%)

(Net profit / Sales revenue) x 100

Independent variables – ETRs CuETR (%)

(Current corporate income tax expense / Pre-tax profit) x 100

CaETR (%)

(Corporate income tax paid / Pre-tax profit) x 100

Independent variables – firm-specific control variables SIZE

Natural logarithm of total assets (in 000 Serbian dinars)

DEBTR

Total liabilities (including deferred tax liabilities) / Total assets

FIXED

Fixed assets / Total assets

ATR

Sales revenue / Total assets

LIQ

Current assets / Short-term liabilities

AGE

Natural logarithm of age of the company in years; age of the company is calculated as the difference between balance date and incorporation date, retrieved from The Serbian Business Registers Agency (2018)

Independent variables – macroeconomic control variables GDP (%)

As reported by the World Bank (2019)

INFL (%)

As reported by the World Bank (2019)

The impact of corporate income tax on company profitability is examined on the basis of regression analysis. This method has been widely used in the similar previous research (Gatsi et al., 2013; Ezugwu, & Akubo, 2014; Al-Jafari, & Al Samman, 2015; Assidi et al., 2016; Pitulice et al., 2016; Vržina, 2018).3 In this regard, it is possible to formulate the following general panel regression research model: PROFit = α + β1ETRit + β2SIZEit + β3DEBTRit + β4FIXEDit + β5ATRit + β6LIQit + β7AGEit + β8GDPt + β9INFLt + εit

(1)

where PROF stands for ROA, ROE, and ROS, while ETR stands for CuETR and CaETR.

3 Most of the previous research (e.g. Gatsi et al., 2013; Al-Jafari, & Al Samman, 2015; Vržina, 2018) used OLS regression.

One exception is the research of Assidi et al. (2016) that used fixed-effects panel regression.

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EJAE 2020  17(1)  1-19

VRŽINA, S., DIMITRIJEVIĆ, M.  DETERMINANTS OF PROFITABILITY OF THE AGRICULTURAL SECTOR OF VOJVODINA: THE ROLE OF CORPORATE INCOME TAX

Statistical data processing has been conducted in econometric software EViews, version 9, while statistical significance of results has been estimated at 10%, 5%, and 1% confidence level. The necessary financial data has been retrieved from statutory financial statements published on the website of The Serbian Business Registers Agency (2019).4

RESEARCH RESULTS Descriptive Statistics The presentation of research results begins with descriptive statistics. Table 3 presents descriptive statistics for all variables used in this analysis.

Table 3 - Descriptive Statistics Variable

Mean

Minimum

Median

Standard deviation

Maximum

Coefficient of variation

Dependent variables ROA

5.043%

0.060%

3.686%

25.632%

4.750%

0.942

ROE

9.035%

0.088%

6.986%

91.341%

10.126%

1.121

ROS

10.546%

0.121%

6.015%

70.712%

11.581%

1.098

Independent variables – ETRs CuETR

3.561%

0.000%

0.000%

42.037%

7.391%

2.076

CaETR

4.163%

0.000%

0.000%

108.632%

12.435%

2.987

Independent variables – firm-specific control variables SIZE

14.109

12.176

13.983

16.467

0.856

0.061

DEBTR

0.372

0.006

0.385

0.979

0.255

0.687

FIXED

0.550

0.127

0.533

0.938

0.169

0.308

ATR

0.655

0.105

0.516

2.252

0.438

0.669

LIQ

5.238

0.099

1.733

85.137

11.594

2.213

AGE

2.991

1.109

3.117

4.261

0.518

0.173

Independent variables – macroeconomic control variables Year

2013

2014

2015

2016

GDP

2.572%

-1.831%

0.758%

2.797%

INFL

7.694%

2.082%

1.392%

1.122%

Mean profitability values are considerably higher than median ones due to high maximum values.

However, many observations were able to record only modest profitability rates: 39 observations have ROA between 0% and 1%, 17 observations have ROE between 0% and 1%, while 23 observations have ROS between 0% and 1%. Vučković, Veselinović, and Drobnjaković (2016) argue that modest profitability of agricultural companies is primarily the consequence of high input prices and expensive bank loans.

ETR in mean and median agriculture company is well below STR of 15%. Furthermore, 139 (139) observations have current (cash) ETR of 0%, while only 22 (13) observations have current (cash) ETR higher than 15%. It is interesting to point out that 129 observations (68.62%) have both current ETR and cash ETR of 0%. In addition, 23 (20) sampled companies had current (cash) ETR of 0% continuously between 2013 and 2016 despite recording pre-tax profit in every observed year.

4 Raw data used in empirical analysis is available at: https://data.mendeley.com/datasets/47f9wr2r7k/2.

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There can be many explanations for the relatively low ETRs in agricultural companies. Companies may carry forward tax losses incurred until December 31st, 2009 to the following ten years and tax losses incurred after January 1st, 2010 to the following five years, to offset the taxable profit in the following years. There is also an investment tax incentive, as a company that invests at least one billion Serbian dinars in fixed assets and employs at least a hundred new workers can reduce corporate income tax liabilities over the following ten years. It is interesting to point out that 22 of sampled 50 companies have continuously increased the value of fixed assets between 2013 and 2016, which can be an indicator of significant investment in fixed assets.

Highly concentrated ownership of sampled agricultural companies enables a reduction of corporate income tax liabilities through related-party transactions and transfer pricing system. Furthermore, related companies can benefit from tax consolidation. If one company owns more than 75% of one or more companies, it can apply for tax consolidation and offset profits of one company with losses of another related company. Related companies can, in this way, minimize the corporate income tax liabilities that are allocated on individual companies according to the share of their profits in consolidated profit.

Agricultural companies have a relatively low debt ratio since their share of debt in total assets in mean and median company is lower than 50%. This is in line with the conclusion of Popović, Janković, and Stojanović (2018) about agricultural loans as an unutilized bank credit market segment in Serbia which can be serious deficient as it that slows down the development of agricultural companies. On the other hand, the share of fixed assets in total assets in mean and median agricultural company is higher than 50%.

The asset turnover ratio is higher than one in only 31 observations. In the context of liquidity ratio, a very low minimum and very high maximum value, as well as high mean value, indicate that many agricultural companies have problem with disparity between assets maturity and debt maturity, as pointed out by Jakšić, Vuković, and Mijić (2011).

The statistical significance of the difference between current (cash) ETR and STR is tested in order to examine the validity of the first research hypothesis. Since current ETR and cash ETR are not normally distributed, median tests of equality of samples (Wilcoxon/Mann-Whitney tie-adjusted method) have been conducted, and their results are presented in the Table 4.

Table 4 - Tests of Equality Between Current (Cash) Effective Corporate Income Tax Rate (ETR) and Statutory Corporate Income Tax Rate (STR) Tax rate 1

Tax rate 2

Test Value

p-value

Current ETR

STR

14.146

***0.000

Cash ETR

STR

15.915

***0.000

Note: Statistically significant at 10% (*), 5% (**), and 1% (***) confidence level.

The results of statistical tests indicate that statistically significant difference exists between current (cash) ETR and STR. These results are quite understandable, since median values of current and cash ETRs are 0%. On the other hand, there is no significant difference between current ETR and cash ETR (test value = 0.189; p-value = 0.850). Therefore, the first research hypothesis cannot be rejected.

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Univariate Analysis Table 5 presents Pearson’s correlation coefficients and their statistical significance. There is significant correlation between profitability proxies: ROA and ROE as well as ROA and ROS are highly positively correlated, while ROE and ROS are moderately positively correlated. Current ETR is significantly negatively correlated with all three profitability proxies, while cash ETR is significantly negatively correlated with ROA and ROS. Among the independent variables, the highest correlation appears between debt ratio and asset turnover ratio. On the other hand, gross domestic product is the only variable that is not significantly correlated to any other variable from research model.

Table 5 - Pearson’s Correlation Matrix Variable

ROA

ROE

ROS

CuETR

CaETR

SIZE

DEBTR

ROA

1

ROE

***0.636

1

ROS

***0.715

***0.300

1

CuETR

***-0.241

*-0.126

***-0.196

1

CaETR

***-0.213

-0.115

**-0.178

***0.522

1

0.087

-0.082

***0.308

*-0.142

**-0.144

1

DEBTR

***-0.336

***0.247

***-0.470

***0.210

**0.144

*-0.129

1

FIXED

*-0.126

**-0.186

0.049

***-0.253

***-0.207

***0.224

***-0.214

ATR

*0.135

***0.487

***-0.370

0.096

0.056

***-0.362

***0.518

SIZE

LIQ

**0.175

-0.048

***0.348

-0.106

-0.070

0.081

***-0.453

AGE

*-0.133

***-0.265

-0.111

-0.057

-0.086

-0.117

**-0.172

GDP

0.029

0.031

0.039

-0.052

0.039

0.004

0.005

INFL

***0.200

**0.179

0.047

-0.116

**-0.152

-0.106

-0.004

Variable

FIXED

ATR

LIQ

AGE

GDP

INFL

ROA ROE ROS CuETR CaETR SIZE DEBTR FIXED

1

ATR

***-0.383

1

LIQ

-0.051

***-0.228

1

AGE

0.056

**-0.152

0.069

1

GDP

0.024

0.005

0.047

0.015

1

INFL

0.035

0.092

-0.044

*-0.121

***0.332

1

Note: Statistically significant at 10% (*), 5% (**), and 1% (***) confidence level.

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Regression Analysis Since the influence of two corporate income tax burden proxies (CuETR and CaETR) on three profitability proxies (ROA, ROE and ROS) has been studied, there are six regression models to be reported. Regarding multicollinearity statistics, Variance Inflation Factor and Tolerance statistics are considerably lower than 10 and higher than 0.10, respectively, for each independent variable in all six regression models, so problems with multicollinearity are not expected. Relying on Ratner (2009) correlation criteria, it can be concluded there is no high correlation between independent variables, confirming the absence of multicollinearity.

Table 6 presents the results of the Breusch-Pagan LM test and Hausman test, used to determine which regression method is the most appropriate. According to Breusch-Pagan LM tests, random-effects regression outperform Ordinary Least Squares (OLS) regression in each regression model. Hausman tests indicate that the random-effects regression is more appropriate in the first four models, while fixed-effects regression should be employed in the last two models.

Table 6 – Results of Statistical Tests

BreuschPagan LM test

Hausman test

Dependent: ROA

Dependent: ROE

Dependent: ROS

Corporate income tax burden proxy:

Corporate income tax burden proxy:

Corporate income tax burden proxy:

CuETR

CaETR

CuETR

CaETR

CuETR

CaETR

Cross-section

47.091 (0.000)

45.848 (0.000

67.913 (0.000)

64.831 (0.000)

57.471 (0.000)

55.572 (0.000)

Test Hypothesis Time

0.549 (0.459)

0.716 (0.398)

1.073 (0.300)

1.269 (0.260)

1.491 (0.222)

1.573 (0.210)

Both

47.640 (0.000)

46.564 (0.000)

68.987 (0.000)

66.100 (0.000)

58.962 (0.000)

57.145 (0.000)

Chi-Sq. Statistic

14.069

13.623

11.829

12.300

22.763

20.911

Chi-Sq. d.f. p-value

Note: p-values in parentheses.

10

9

9

9

9

9

9

0.120

0.136

0.223

0.197

0.007

0.013

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Table 7 - Panel Regression Estimates Variable

Dependent: ROA

Dependent: ROE

Dependent: ROS

Effects

Random

Random

Random

Random

Fixed

Fixed

Intercept

-2.160 (-0.243)

-2.136 (-0.240)

20.303 (0.971)

19.711 (0.938)

**-130.770 (-2.053)

*-111.316 (-1.743)

CuETR

**-0.092 (-2.420)

***-0.244 (-3.063) *-0.036 (-1.685)

CaETR

**-0.240 (-2.461) *-0.084 (-1.899)

*-0.091 (-1.770)

*0.926 (1.722)

*0.924 (1.719)

-0.560 (-0.442)

-0.529 (-0.416)

2.834 (0.739)

2.112 (0.546)

DEBTR

***-11.235 (-6.388)

***-11.253 (-6.374)

-1.465 (-0.373)

-1.415 (-0.357)

**-19.424 (-2.591)

**-18.208 (-2.405)

FIXED

**-5.069 (-2.129)

**-4.793 (-2.008)

1.565 (0.295)

2.203 (0.411)

-13.194 (-1.552)

-13.022 (-1.512)

ATR

***5.133 (4.790)

***4.968 (4.585)

***11.552 (4.812)

***11.105 (4.548)

-2.100 (-0.457)

-3.689 (-0.786)

LIQ

-0.009 (-0.288)

-0.010 (-0.318)

0.027 (0.433)

0.024 (0.380)

-0.011 (-0.138)

-0.023 (-0.284)

AGE

-0.868 (-1.003)

-0.944 (-1.093)

*-3.823 (-1.849)

*-3.995 (-1.932)

***38.523 (3.742)

***35.504 (3.454)

GDP

-0.071 (-0.591)

-0.042 (-0.340)

-0.048 (-0.195)

0.020 (0.081)

*-0.612 (-1.846)

-0.478 (-1.434)

***0.257 (2.790)

***0.254 (2.691)

*0.324 (1.688)

*0.327 (1.660)

***1.165 (3.371)

***1.071 (3.071)

Adj. R2

0.275

0.264

0.224

0.201

0.690

0.683

F-Value

***8.866

***8.449

***7.010

***6.234

***8.300

***8.073

SIZE

INFL

Note: Beta coefficients in front of the parentheses, t-statistics in the parentheses; statistically significant at 10% (*), 5% (**,) and 1% (***) confidence level.

Panel regression estimates are reported in Table 7,5 showing that the reduction of ETRs increases the profitability of agricultural companies in Vojvodina, though this impact is relatively weak. In this context, has current ETR has the strongest impact on ROE, whereas a reduction in the current ETR of 1% increases ROE for only 0.244%. Furthermore, the impact of the current ETR on profitability is stronger than the impact of cash ETR. The weak influence of corporate income tax burden on the profitability of agricultural companies should not be surprising given that most of the observations have ETRs considerably lower than STR. Current ETR and cash ETR, as well as the inflation rate, are the only independent variables that significantly influence every profitability proxy, so results are robust to corporate income tax burden and profitability proxies changes.

There are many reasons that can explain why agricultural companies with lower ETRs have a higher profitability. Firstly, several agricultural companies that overcame difficulties during the global crisis (in 2008 and 2009) became more profitable. On the other hand, these companies use tax loss carryforward to reduce ETRs. 5 OLS regression estimates and other regressions estimates are given in Appendix 2 and Appendix 3, respectively.

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Tax losses from 2008 and 2009 can be carried forward into the following ten years. Secondly, it is possible that high profitability of agricultural companies is a consequence of the high efficiency of investment in fixed assets. On the other hand, these companies use investment tax incentive to reduce ETRs. Therefore, the second research hypothesis cannot be rejected.

Larger companies have significantly higher ROA. Debt ratio significantly influences ROA and ROS – due to the existence of interest expenses, companies with higher debt ratio have lower ROA and ROS. Companies with a higher share of fixed assets in total assets, due to fixed depreciation and amortization costs, have lower ROA. Companies with a higher asset turnover ratio have higher ROA and ROE. Older agricultural companies have significantly lower ROE, yet higher ROS. Liquidity and the gross domestic product growth rate are independent variables that are not a significant determinant of profitability in any regression model. Finally, the inflation rate significantly influences each profitability proxy – agricultural companies had higher ROA, ROE and ROS in years with higher inflation rates.

CONCLUSION This paper analyzed the determinants of profitability of 50 agricultural companies in Vojvodina in the period between 2013 and 2016, with a focus on the impact of corporate income tax on company profitability. Corporate income tax burden has been proxied with current ETR and cash ETR, while profitability has been proxied with ROA, ROE and ROS. Statistical analysis showed that ETRs in agricultural companies are significantly lower than STR of 15%. In addition, nearly 69% of observations have both current ETR and cash ETR of 0%. Reasons for relatively low ETRs can be found in tax loss carryforward and investment tax incentive permitted by law, as well as transfer pricing and tax consolidation opportunities, since ownership of the sampled companies is highly concentrated. These results indicate that agriculture is an industry with exceptionally low corporate income tax burden. Findings about ETR lower than STR are consistent with Al-Jafari and Al Samman (2015) and Assidi et al. (2016), though statistical significance of the difference between ETR and STR has not been tested in these papers.

It was found that a reduction of ETRs leads to a significant increase of profitability of agricultural companies when analyzing panel regression. These results indicate that companies that use tax loss carryforward have recovered from losses, and that companies that use investment tax incentive have high efficiency of their investments. However, this impact is relatively weak, due to the low corporate income tax burden of agricultural companies. This finding is in line with Gatsi et al. (2013), Assidi et al. (2016), and Pitulice et al. (2016).

Considering the obtained research results, neither research hypotheses can be rejected. Research results are not sensitive to change of corporate income tax burden or profitability proxies. Managers of agricultural companies can benefit from the research, which recognizes the important variables of their companies’ success. Managers should particularly realize that relying on minimizing ETRs is a significant cost-efficient strategy leading to increase of management efficiency.

However, users of these results should bear in mind that a possible limitation of the research results lies in the fact that the analysis covered only 50 agricultural companies. It is possible that research results would be different had the sample size and/or sampling period been different. Furthermore, removing some observations (with pre-tax loss, with losses that exceed owners’ equity or with adverse audit opinion or disclaimer of audit opinion) could impact research results. Henry and Sansing (2018) 12

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argue that removing pre-tax loss observations could be a particularly important limitation. Since many sampled companies had qualified audit opinions, which could indicate meaningful misstatements in financial statements, research results should be interpreted carefully.

The presented results emphasize the importance of corporate income tax in microeconomic analysis and the need for further inclusion of tax aspects in the analysis of company financial performance.

Research results additionally indicate that agricultural companies should continue to minimize their corporate income tax burden in order to be more profitable. Future research should include the period before 2013, during which STR was 10%, and compare the obtained results in the period before and after the increase of STR. Moreover, future research should compare the corporate income tax burden of agricultural companies with the burden of companies from other industries. It would be also interesting to research the determinants of ETRs in agricultural companies assuming, that bigger and more profitable companies might have more power and resources to lower the tax burden.

ACKNOWLEDGEMENT This paper is a part of the Project III 47005, financed by the Ministry of Education, Science and Technological Development of the Republic of Serbia.

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APPENDIX 1 List of Sampled Companies No.

16

Registration number

Company name

No.

Registration number

Company name

1

08750718

Agrogrnja

26

08028419

Miletić

2

08276188

Agrohemika

27

08011079

Napredak

3

20084740

Agroprom Com

28

08237930

Nova Budućnost

4

08188190

Agro-Promet

29

08044376

Nova Peščara

5

08120544

Agros

30

08047723

Omoljica

6

08003572

Agrounija

31

08056811

Panonija

7

08219699

Agrovet

32

20655526

Per-Agro

8

08325316

Almex

33

08053324

Petefi

9

08035512

Banatski Despotovac

34

08607753

PIK Bečej

10

08246670

Bezdan

35

08154848

PIK Južni Banat

11

08057729

Borac

36

08055157

Pionir

12

08144532

Budućnost

37

08142599

Pobeda

13

20698764

Ćirić Agro MĐŽ

38

08684936

Podunavlje

14

08056757

Doža Đerđ

39

08064911

Poljoprivredna proizvodnja

15

08065616

Feketić

40

08305480

Potkozarje

16

08470634

Fotos

41

08069042

Ratkovo

17

08021848

Galad

42

08049335

Ravnica

18

08057664

Graničar

43

08065888

Sava Kovačević

19

08057621

Grmeč

44

08021899

Sloga

20

08121893

Hajdučica

45

08047731

Stari Tamiš

21

08129525

Jedinstvo Apatin

46

08021856

Topola

22

08021937

Jedinstvo Kikinda

47

08671613

Uljarice-Bačka

23

08047715

Kačarevo

48

08043787

Vojvodina

24

08021902

Kozara

49

20419482

Vrebalov Agrar

25

08115842

Maglić

50

08035466

Zlatica

EJAE 2020  17(1)  1-19

VRŽINA, S., DIMITRIJEVIĆ, M.  DETERMINANTS OF PROFITABILITY OF THE AGRICULTURAL SECTOR OF VOJVODINA: THE ROLE OF CORPORATE INCOME TAX

APPENDIX 2 OLS Regression Estimates Variable

Dependent: ROA

Intercept

-2.118 (-0.344)

CuETR

***-0.112 (-2.725)

Dependent: ROE

-1.910 (-0.306)

2.163 (0.160) ***-0.238 (-2.636)

**-0.058 (-2.381)

CaETR

Dependent: ROS

1.946 (0.142)

-2.161 (-0.144)

-0.156 (-0.010)

*-0.166 (-1.659) **-0.113 (-2.092)

*-0.115 (-1.941)

***0.980 (2.640)

**0.964 (2.570)

0.744 (0.915)

0.737 (0.895)

***2.643 (2.922)

***2.547 (2.810)

DEBTR

***-10.071 (-6.882)

***-10.346 (-7.085)

***-10.346 (-7.085)

0.974 (0.304)

***-15.050 (-4.223)

***-15.305 (-4.337)

FIXED

**-4.848 (-2.531)

**-4.526 (-2.370)

-2.789 (-0.664)

-1.949 (-0.465)

**-9.898 (-2.122)

**-9.830 (-2.130)

ATR

***4.292 (4.994)

***4.322 (5.004)

***10.782 (5.726)

***10.894 (5.744)

**-4.551 (-2.175)

**-4.628 (-2.217)

LIQ

-0.002 (-0.065)

-0.001 (-0.039)

0.061 (0.981)

0.063 (1.011)

**0.137 (1.995)

**0.136 (1.992)

AGE

**-1.154 (-2.004)

**-1.228 (-2.110)

***-3.480 (-2.758)

***-3.604 (-2.821)

***-3.848 (-2.744)

***-4.042 (-2.874)

GDP

-0.067 (-0.415)

-0.021 (-0.127)

-0.091 (-0.258)

-0.001 (-0.004)

0.102 (0.261)

0.193 (0.490)

INFL

**0.285 (2.433)

**0.266 (2.226)

0.424 (1.649)

0.393 (1.499)

0.236 (0.826

0.179 (0.622)

Adj. R2

0.329

0.323

0.292

0.282

0.331

0.335

F-Value

***11.202

***10.908

***9.556

***9.148

***11.282

***11.456

SIZE

Note: Beta coefficients in front of the parentheses, t-statistics in the parentheses; statistically significant at 10% (*), 5% (**), and 1% (***) confidence level.

17

EJAE 2020  17 (1)  1-19

VRŽINA, S., DIMITRIJEVIĆ, M.  DETERMINANTS OF PROFITABILITY OF THE AGRICULTURAL SECTOR OF VOJVODINA: THE ROLE OF CORPORATE INCOME TAX

APPENDIX 3 Other regressions estimates Variable Effects

Dependent: ROA

Dependent: ROE

Dependent: ROS

Fixed

Fixed

Fixed

Fixed

Random

Random

Intercept

-2.987 (-0.106)

5.235 (0.184)

**142.474 (2.477)

***162.413 (2.792)

-9.364 (-0.421)

-7.468 (-0.337)

CuETR

**-0.101 (-2.331)

***-0.244 (-2.780) -0.026 (-1.160)

CaETR

*-0.160 (-1.830) -0.073 (-1.567)

*-0.082 (-1.696)

SIZE

-0.982 (-0.578)

-1.268 (-0.737)

***-9.625 (-2.781)

***-10.335 (-2.934)

***-17.826 (-4.201)

***-17.646 (-4.154)

DEBTR

*-7.338 (-1.949)

*-7.055 (-1.841)

4.519 (0.589)

5.046 (0.643)

*-10.919 (-1.903)

*-10.819 (-1.885)

FIXED

*-7.338 (-1.949)

*-7.055 (-1.841)

4.519 (0.589)

5.046 (0.643)

*-10.919 (-1.903)

*-10.819 (-1.885)

ATR

***5.589 (2.743)

**5.073 (2.429)

5.822 (1.402)

4.453 (1.042)

-3.449 (-1.332)

-3.946 (-1.510)

LIQ

-0.015 (-0.428)

-0.019 (-0.516)

0.006 (0.082)

-0.004 (-0.050)

0.046 (0.672)

0.043 (0.622)

AGE

*8.695 (1.907)

7.242 (1.584)

-0.546 (-0.059)

-3.936 (-0.421)

-2.249 (-1.028)

-2.498 (-1.149)

GDP

-0.197 (-1.343)

-0.146 (-0.982)

0.049 (0.164)

0.178 (0.588)

0.093 (0.343)

0.160 (0.578)

INFL

***0.404 (2.638)

**0.368 (2.372)

0.106 (0.341)

0.016 (0.052)

0.198 (0.940)

0.174 (0.807)

Adj. R2

0.638

0.627

0.669

0.656

0.151

0.153

F-Value

***6.794

***6.520

***7.643

***7.263

***4.709

***4.762

Note: Beta coefficients in front of the parentheses, t-statistics in the parentheses; statistically significant at 10% (*), 5% (**), and 1% (***) confidence level.

18

EJAE 2020  17(1)  1-19

VRŽINA, S., DIMITRIJEVIĆ, M.  DETERMINANTS OF PROFITABILITY OF THE AGRICULTURAL SECTOR OF VOJVODINA: THE ROLE OF CORPORATE INCOME TAX

DETERMINANTE PROFITABILNOSTI POLJOPRIVREDNOG SEKTORA U VOJVODINI: ULOGA POREZA NA DOBITAK

Rezime: Usled značajnog učešća u ukupnoj zaposlenosti i spoljnotrgovinskoj razmeni, poljoprivreda predstavlja važan sektor ekonomije Srbije.

Stoga, neophodno je kontinuirano analizirati finansijske performanse poljoprivrednih preduzeća i ključne determinante finansijskih performansi. Ciljevi rada jesu analiza opterećenja porezom na dobitak poljoprivrednih preduzeća u Vojvodini i njegovog uticaja na profitabilnost preduzeća. Statistički testovi su pokazali da su efektivne stope poreza na dobitak (ETR) u poljoprivrednim preduzećima značajno niže u odnosu na propisanu stopu poreza na dobitak. Dodatno, oko 69% opservacija ima i tekuću ETR i gotovinsku ETR od 0%, što ukazuje na to da je poljoprivreda delatnost sa izuzetno niskim opterećenjem porezom na dobitak. Panel regresija je pokazala da su poljoprivredna preduzeća sa nižim ETR profitabilnija u odnosu na preduzeća sa višim ETR. Rezultati analize nisu senzitivni na promene merila opterećenja porezom na dobitak i profitabilnosti.

Ključne reči: poljoprivreda, profitabilnost, porez na dobitak, Vojvodina.

19

EJAE 2020, 17(1): 20 - 36 ISSN 2406-2588 UDK: 336.744 004.738.5:339.133.5(100)"2015/2019" DOI: 10.5937/EJAE17-22053 Original paper/Originalni nauÄ?ni rad

HERD BEHAVIOUR IN THE CRYPTOCURRENCY MARKET: FUNDAMENTAL VS. SPURIOUS HERDING Chamil W. Senarathne*, Wei Jianguo School of Economic, Wuhan University of Technology, Hubei, China

20

Abstract:

Article info:

This paper sets out to explore whether the investor herding in the cryptocurrency market induces correlations in cryptocurrency returns using the methodology of Chang et al. (2000) and Galariotis et al. (2015) from a daily data sampling period of 3/30/2015 to 5/24/2019. The initial regression results show that the cross-sectional absolute deviation of return can only be explained by GSCI oil and gold index return, but no relationship exists between cross-sectional absolute deviation of return and other regression variables, such as return on CCi30, US equity risk premium and US/Euro exchange rate return. The herding regression results under normal market condition show that a strong tendency exists to herd on non-fundamental information that explains cross-sectional absolute deviation of returns. As such, cryptocurrency returns cannot be predicted on the basis of fundamental economic information (e.g., major macroeconomic announcements). Herding on non-fundamental information is found to be more pronounced during an upward-trending period of the market and other than upward-trending period. No signs of herding on fundamental information could be observed under other market conditions. Although the theory suggests that herding on non-fundamental information results in more efficient outcomes, the above findings do not encourage the diversification of traditional assets with cryptocurrency on the basis of low correlation.

Since cryptocurrency lacks intrinsic value, the exchange is shown to provide a pseudo-efficient trading platform for speculative investors.

Implications for future research are discussed.

Received: June 7, 2019 Correction: August 20, 2019 Accepted: August 21, 2019

*E-mail: chamil@whut.edu.cn

Keywords: herd behavior, cryptocurrency, fundamental information, CASD, portfolio diversification, pseudo efficient, intrinsic value.

JEL Classification: G15, G14

EJAE 2020  17 (1)  20 - 36

CHAMIL W. SENARATHNE, K, W., JIANGUO, W.  HERD BEHAVIOUR IN THE CRYPTOCURRENCY MARKET: FUNDAMENTAL VS. SPURIOUS HERDING

INTRODUCTION Herding in financial markets has been a heated debate in the scholarly world over the past two decades. Investor herding is a market phenomenon in which a group of investors simply imitate the actions of others or base their decisions upon the actions of other investors in the market. It can also be a situation in which the trades of a group of investors move in one direction (Nofsinger and Sias, 1999), or investors follow trends in the previous trades, ignoring their initial assessments (Avery and Zemsky, 1998). When herding is present, investor behaviour converges to the average price change pattern of the market (Hirshleifer et al., 2003) which may eventually lead to mutual imitation. Not all investors have the same set of information about security prices at any given point in time, while prices adjust to information as and when the information becomes available to investors. If investors trade without information about the trading securities, price movements will reflect the information variables related to common premiums in the market, while price changes may co-move closely with this premium on average because investors sometimes trade for the sake of trading with speculative profit motives (Senarathne and Jayasinghe, 2017). In such a case, the benchmark for expected payoffs would be the common market expectation (or premium), because there is no security (i.e., firm) specific information 1.

The term ‘herding’ is closely associated with ‘efficiency’. Herding is one of the critical factors that contribute to market inefficiency. Froot et al. (1992) argue that speculators may choose to study information that is completely unrelated to firms, microeconomic and macroeconomic fundamentals. On the other hand, scholars show that herding on fundamental information (i.e., mimicking fundamental factors) results in inefficient outcomes, whereas herding on non-fundamental information (i.e., mimicking firm-specific factors) leads to efficient market conditions (See Bikhchandani and Sharma 2000).

These findings can be effectively observed in markets with instruments (i.e., assets) that have underlying assets. Cryptocurrencies do not have underlying assets to justify whether the trading occurs due to firm-specific (or underlying asset-specific) factors. Although scholars have established efficiency in the cryptocurrency market through their findings (e.g. Urquhart, 2016; Wei 2018), no scholar has attempted to understand the type of efficiency in cryptocurrency trading with reference to any critical factor affecting market efficiency. Moreover, the papers dealing with detecting herding in the cryptocurrency market have so far not attempted to distinguish herding between the fundamental and the spurious (i.e., non-fundamental) in order to understand its impact on market efficiency.

The objective of this paper is to examine the herd behaviour in the cryptocurrency market using the framework of Chang et al. (2000). This paper also attempts to distinguish the herding phenomenon between fundamental and spurious (i.e., non-fundamental) by applying the methodological approach followed by Galariotis et al. (2015). The findings show that there is a strong tendency to herd on nonfundamental information that explains cross-sectional absolute deviation of returns under normal and up-trending market conditions. The paper is organized as follows: Section two provides the econometric framework. Section three provides the statistical properties of the sample data. Section four outlines and discusses the findings. Section five concludes the paper.

LITERATURE REVIEW The famous work of Chang et al. (2000) examines the herd behaviour in the stock markets of five countries namely, the USA, Japan, South Korea, Hong Kong and Taiwan, and finds no evidence for herding in the U.S. and Hong Kong markets. However, they find partial evidence for herding in Japanese markets, and significant evidence for herding in the stock markets of South Korea and Taiwan.

1 This is known as common market premium, which is the difference between overall market expectation (i.e., market risk) and the aggregate expectations formed on the basis of firm-specific information.

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EJAE 2020  17 (1)  20 - 36

CHAMIL W. SENARATHNE, K, W., JIANGUO, W.  HERD BEHAVIOUR IN THE CRYPTOCURRENCY MARKET: FUNDAMENTAL VS. SPURIOUS HERDING

Herding has now become a common phenomenon in equity markets, and a number of scholars have observed herd behaviour in equity markets around the world (See, e.g., Peiyuan and Donghui, 2002; Demirer and Kutan, 2006; Tan et al., 2008; Chiang and Zheng, 2010; Demirer et al., 2010; Balcilar et al., 2013; Chen, 2013).

Since the introduction of cryptocurrency (i.e., Bitcoin) in the year 2009, there have been several large literature attempts to understand the nature and characteristics of cryptocurrency. Among them, several papers identify Bitcoin (i.e., the largest cryptocurrency according to market capitalization) as a speculative instrument, although the main purpose of its introduction was to facilitate the settlement of commercial transactions. Along these lines, Yermack (2015), Baek and Elbeck, (2015), Cheah and Fry (2015), Bouoiyour and Selmi (2015), Dyhrberg, (2016), Baur et al. (2017), and Senarathne (2019) establish cryptocurrency (or Bitcoin) as a speculative asset rather than a medium of exchange. The main feature of cryptocurrency is that it does not have an underlying asset and, as such, no reliable method could be used to assess the value of the currency.

Herding phenomenon could be effectively interpreted for financial markets with instruments (i.e., securities) having underlying assets. However, Vidal-Tomás et al. (2018) find evidence for herding during a down-trending market, and observe that the smallest cryptocurrencies tend to herd towards the price change dispersion of the largest ones. Using a rolling window analysis, Bouri et al. (2018a) find that the herd behaviour varies over time in the cryptocurrency market as uncertainty increases. Caporale et al. (2018) examine the degree of predictability of cryptocurrencies, and find that the current value of cryptocurrency is highly positively correlated with its past and future values. They attribute these findings to market inefficiency and the ability of investors to earn arbitrage profits by analyzing the trends in price movements.

Poyser (2018) examines the herd behaviour using Markov-Switching approach, and finds evidence for herding. Derived from a single-factor capital asset pricing model, the framework introduced by Chang et al. (2000) can be meaningfully applied when the asset prices are determined by the information available to investors on the underlying trading assets. In the absence of an underlying asset of a trading currency, investors are either trading for speculative reasons or gambling (Senarathne 2018). Van Wijk (2013) shows that the Bitcoin price is determined mainly by the macroeconomic and financial developments of major economies. However, Cheah et al. (2015) unearth an important fact about Bitcoin in that the fundamental value of Bitcoin prices is zero, and demonstrate that it exhibits speculative bubbles.

The papers reviewed so far have made no attempts to understand what drives herding in the cryptocurrency market. Understanding that would give us an idea about market efficiency (See Bikhchandani and Sharma 2000), because spurious herding may lead to an efficient outcome, while intentional herding may not. In the absence of underlying assets in the cryptocurrency market, it is difficult to establish market efficiency without carrying out an adequate analysis of the market behaviour. Efficiency in the cryptocurrency market cannot simply be assessed by observing the level of correlation between cryptocurrencies and other speculative assets or economic variables, as shown by a number of scholars (See, e.g., Chuen et al., 2017; Guesmi et al., 2019). Instead, additional test procedures must be employed to understand what factually drives efficiency of cryptocurrency. The framework used by Galariotis et al. (2015) provides a meaningful way to analyze the type of herding that can impact cryptocurrency market efficiency.

Econometric Framework Chang et al. (2000) introduce a simple framework to detect herd behaviour in financial markets. They use cross-sectional absolute deviation (CSAD) as a proxy for the unobservable expected CSAD. In their work, CSAD is estimated using the average absolute value deviation of each stock relative to the return on an equally-weighted market portfolio. The model assumes a non-linear relationship between CSAD and square of the market return.2 Consider the following model in the sense of Chang et al. (2000): 2 Note that this study omits cross-sectional standard deviation (CSSD) method given the inherent limitations associated with its regression (e.g. effect of outliers)

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EJAE 2020  17 (1)  20 - 36

CHAMIL W. SENARATHNE, K, W., JIANGUO, W.  HERD BEHAVIOUR IN THE CRYPTOCURRENCY MARKET: FUNDAMENTAL VS. SPURIOUS HERDING

(1) where Rit is the observed return of cryptocurrency i at time t , and CSADN is the total number of cryptocurrencies in the portfolio. The portfolio is formed with Bitcoin, Litecoin, Tether, Nem, Ripple. CCi30t is the cryptocurrency market return proxied by the CCi30 cryptocurrency index at time t , which was created and has been maintained since 1st January 2017. The base value is set at 100 on 1st January 2015 for the computation of index, and its constituents are maintained by an independent team of mathematicians and fund managers led by a panel of professors and experts (See https://cci30.com/#statistics for a detailed description on constituents and methodology). 30 cryptocurrencies were chosen based on five main characteristics: 1. diversified; 2. replicable; 3. transparent; 4. provides in-depth coverage of the entire sector; 5. presents the best risk-adjusted performance profile possible. The following specification can be used to investigate the general herd behaviour in the cryptocurrency market:

(2) (3) (4) where L is the lag operator, and a is the constant of the conditional variance equation, ht . Obviously, a>0 and π and λ are the non-negative ARCH and GARCH coefficients whose auto-regressive structure requires shocks to volatility to persist over time. Henceforth, coefficients of all mean equations are estimated by the above GARCH variance specification (04), which is unnecessary to repeat under each mean regression. A number of authors document that there is no leverage effect in the cryptocurrency market (see: e.g., Dyhrberg, 2016; Urquhart, 2017; Othman et al., 2019; Senarathne 2019) and, as such, the volatility estimation is limited to the GARCH (1, 1) specification see also Glaser et al., 2014 for a well-received work).

In the presence of herding, the coefficient γ3 should be negative and statistically significant. The idea is that, if investors tend to follow the aggregate market behavior during periods of large average price movements or during the periods of extreme market conditions, there should be a less-than-proportional increase or even decrease in the cross-sectional absolute return deviation.

Equations (1) and (2) are based on the idea that the market exhibits herding when investors react to information relating to microeconomic or macroeconomic fundamentals, rather than firm-specific information events. In view of this, Galariotis et al. (2015) examine whether the herd behaviour is caused by fundamental information (e.g., fundamental macroeconomic announcements) attached to securities trading. A more appropriate way to distinguish between intentional (or fundamental) herding and spurious herding is to identify whether the herding outcome results in efficient market conditions. Spurious herding may sometimes present due to spurious correlation between the trading behaviour (pattern) of a group of investors and the average trading pattern of the market. This does not necessarily indicate that the investors move towards the market average on fundamental information because there is a common premium in the market (see Senarathne and Jianguo 2018). If herding is present when similar fundamental information is not available to investors, or when investors do not react to fundamental information, this type of herding can be identified as ‘spurious,’ as opposed to the general idea of herding. This form of herding may lead to an efficient outcome, while intentional or fundamental herding results in a fragile market, excess volatility, and systematic risk.

23

EJAE 2020  17 (1)  20 - 36

CHAMIL W. SENARATHNE, K, W., JIANGUO, W.  HERD BEHAVIOUR IN THE CRYPTOCURRENCY MARKET: FUNDAMENTAL VS. SPURIOUS HERDING

In order to estimate CSAD driven by non-fundamental information or the spurious component of price changes, the following method could be used in the pattern of Galariotis et al. (2015). Regress CSAD on some variables could explain cryptocurrency returns.

(5) where RPUS is the US equity risk premium, which is the value-weighted return of all CRSP firms incorporated in the US and listed on the NYSE, AMEX, or NASDAQ exchanges. GSCIGOLD and GSCIOIL are the S&P Dow Jones major investable GSCI commodity indices for gold and oil. CCi30 is the cryptocurrency index mentioned above, and EXR is the U.S/Euro exchange rate.

Emulating Galariotis et al. (2015), let the CSAD driven by non-fundamental information be estimated by the following equation:

(6) (7) Herding under fundamental and non-fundamental information could be estimated by the following equations:

(8) (9) In the presence of herding, coefficient γ3 should be negative and statistically significant. It is clear that the cryptocurrencies do not have underlying assets. However, a number of scholars have shown that it is driven by macroeconomic fundamentals (see especially van Wijk 2013). Another section of scholars show that the cryptocurrency price changes are random and, as such, the prices are driven by random information events (see section 1 literature). In the absence of an underlying asset, this random information may include cryptocurrency-specific events, for example, ICO project, trading restrictions, frauds, hacking, trading suspension, or restrictions on cash withdrawal, etc.

The literature shows that herding is more intensive when the markets are on the uptrend (see, e.g., Ouarda et al., 2013; Litimi et al., 2016; BenSaïda 2017). A dummy variable Dtup is introduced at time t , which takes the value 1 for all positive observations during the sampling period, or zero otherwise. The following herding regression specification captures the magnitude of investor herding under upwardtrending market condition:

(10) (11)

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EJAE 2020  17 (1)  20 - 36

CHAMIL W. SENARATHNE, K, W., JIANGUO, W.  HERD BEHAVIOUR IN THE CRYPTOCURRENCY MARKET: FUNDAMENTAL VS. SPURIOUS HERDING

Coefficient γ4 should be negative and statistically significant if the investor herding is more pronounced when the market is up, rather than down or neutral. If the herding is more intensive during periods other than up-trending market, the coefficient γ3 should be statistically significant and negative.

Scholars have also shown that herding is more pronounced in crisis and bullish periods (see Bikhchandani and Sharma 2000; Bowe and Domuta 2004; Hwang and Salmon 2004; Philippas et al., 2013; Yao et al., 2014; Galariotis et al., 2015; Bekiros et al., 2017). As such, dummy variables Dbullish and Dcrisis are introduced, which take value 1 during the period of the bullish market (the largest bullish cluster) from 3/15/2017 to 1/05/2018, and during the period of crisis (the largest crisis cluster) from 1/07/2018 to 12/05/2018, respectively, or otherwise zero. The following two separate regressions detect the herd behavior during bullish and crisis periods of the market:

(12)

(13)

(14) (15) Similarly, if the herding is more pronounced under bullish or crisis market conditions, the coefficient γ4 should be negative and statistically significant. If the herding is more intensive during periods other than bullish or crisis market periods, coefficient γ3 should be negative and statistically significant.

Market volatility and trading activities are highly correlated, and this induces market participation and active trading (Darrat and Rahman 1995; Gallo and Pacini, 2000). A number of scholars demonstrate that investors tend to herd more when the market is highly volatile (see, e.g., Dennis and Strickland 2002; Gleason et al., 2004; Gabaix et al., 2006; Holmes et al., 2013). A dummy variable Dvolatility is introduced, which takes the value 1 when the volatility of CCi30 index (i.e., market volatility) return exceeds the average market volatility of the sampling period. The average market volatility can easily be computed from the following GARCH (1, 1) volatility estimates by Eviews:

(16) recall (03) recall (04) (17) (18) 25

EJAE 2020  17 (1)  20 - 36

CHAMIL W. SENARATHNE, K, W., JIANGUO, W.  HERD BEHAVIOUR IN THE CRYPTOCURRENCY MARKET: FUNDAMENTAL VS. SPURIOUS HERDING

If herding is more pronounced during high market volatility periods, the coefficient γ4 should be statistically significant and negative. The coefficient γ3 should be negative and statistically significant, if herding is more intensive in periods other than high volatility periods.

Data and Sample The S&P GSCI commodity indices data are obtained from S&P Dow Jones Indices LLC (available at https://us.spindices.com) and the US/Euro exchange rate data are available on the Federal Reserve Bank of St. Louis webpage (available at https://fred.stlouisfed.org). US equity risk premium data are obtained from the data library (MBA portal) of Kenneth R. French (http://mba.tuck.dartmouth.edu/ pages/faculty/ken.french/index.html). The cryptocurrency index CCi30 data were downloaded from the webpage www.cci30.com (available at ttps://cci30.com/). Readers are advised to visit the webpage above for a detailed description about the computation and its constituents. The cryptocurrency price data are obtained from the webpage https://coinmarketcap.com. The sample covers 1,048 daily observations from the period 3/30/2015 to 5/24/2019. The following steps were followed in processing the data and generating the final outcome. The relevant data are first downloaded from the respective websites and double-checked for accuracy before the return series is generated for each selected cryptocurrency, Bitcoin, Litecoin, Tether, Nem, Ripple (https://coinmarketcap.com), and other assets. The sample is drawn from the top twenty cryptocurrencies ranked by market capitalization. Since most of the cryptocurrencies were launched very recently, priority was given to the date of launch, in addition to the market capitalization, in order to cover a sufficient amount of observations. When portfolio returns are computed, equally-weighted average returns were considered. Once the returns are generated, regressions are run on Eviews statistical software. The raw data used for the research can be reached at http://dx.doi.org/10.17632/k32dph9hjc.1.

The correlations among regression variables and the descriptive statistics of the sample data are as follows: Table 1 – Correlation Matrix of Regressors Variable PRUS

PRUS

GSCIOIL

GSCGOLD

Rcci30t

| Rcci30t |

EXR

Dup

Dbullish

Dcrises

Dvolatility

1

0.334

-0.114

0.066

-0.072

-0.077

0.017

0.020

-0.025

-0.014

GSCIOIL

0.334

1

0.033

0.026

-0.057

0.029

-0.003

0.021

-0.019

0.028

GSCGOLD

-0.114

0.033

1

0.050

0.065

0.425

0.035

0.023

-0.022

0.015

Rcci30t

0.066

0.026

0.050

1

-0.033

0.013

0.656

0.140

-0.144

0.029

| Rcci30t |

-0.072

-0.057

0.065

-0.033

1

0.040

-0.003

0.188

0.151

0.303

EXR

-0.077

0.029

0.425

0.013

0.040

1

0.033

0.054

-0.029

0.001

Dup

0.017

-0.003

0.035

0.656

-0.003

0.033

1

0.123

-0.123

-0.002

Dbullish

0.020

0.021

0.023

0.140

0.188

0.054

0.123

1

-0.261

0.242

D

-0.025

-0.019

-0.022

-0.144

0.151

-0.029

-0.123

-0.261

1

0.308

D

-0.014

0.028

0.015

0.029

0.303

0.001

-0.002

0.242

0.308

1

crises volatility

As Table 1 reports, the correlations among regression variables are very low. The highest correlation is recorded at 0.656 and the lowest is -0.001. Crisis dummy, absolute market return, and up-dummy are negatively correlated with most of the other variables. In addition to the abovementioned variables, all other variables are mostly positively correlated with each other. Since the reported correlations are considerably low, the cryptocurrency pricing and herding regressions are free from multicollinearity problem.

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CHAMIL W. SENARATHNE, K, W., JIANGUO, W.  HERD BEHAVIOUR IN THE CRYPTOCURRENCY MARKET: FUNDAMENTAL VS. SPURIOUS HERDING

Table 2 – Descriptive Statistics of Sample Data Variable

Mean

Med.

Max.

Min.

JB

ADF

Q (20)

CSADpt

0.033

0.021

0.218

1.7E-05

1410.72

-12.37

792.64

CSAD FUND pt

0.025

0.024

0.045

0.013

1124.6

-34.79

27.31

CSAD NONFUND pt

0.008

-0.004

0.195

-0.032

1441.62

-12.33

786.96

3.9E-04

0.001

0.051

-0.040

702.55

-32.24

34.96

GSCI

1.8E-04

0.001

0.101

-0.080

168.68

-35.59

26.49

GSCIGOLD

PRUS OIL

8.0E-05

-1.2E-04

0.046

-0.034

422.21

-33.61

25.08

Rcci30t

0.004

0.004

0.196

-0.292

1073.6

-31.55

58.76

| Rcci30t |

0.032

0.019

0.292

2.1E-05

3364.4

-10.69

456.1

EXR

3.0E-05

0.000

0.031

-0.027

282.37

-33.07

26.84

up

D

0.563

1.000

1.000

0.000

174.68

-31.10

49.81

Dbullish

0.195

0.000

1.000

0.000

421.80

-1.79

18512

Dcrises

0.219

0.000

1.000

0.000

322.13

-1.70

18709

D

0.383

0.000

1.000

0.000

176.84

-6.73

4887

volatility

Notes:

1. JB is the Jarque–Bera test statistic for normality. Under null hypothesis for normality, critical value of χ2 (2) distribution at 5% significance level is 5.99.

2. ADF is the Augmented Dickey–Fuller test statistic for stationarity of data for maximum 21 lags.

Under null hypothesis for data having unit root, the critical value at 5% significance level is -2.87.

3. Q (20) is the Ljung-Box Q statistic for serial correlation upto20 lags. Under the null hypothesis for no serial correlation, the critical value of χ2 (20) distribution at 5% significance level is 31.41.

As Table 1 reports, all regression variables including the distribution of CSAD are highly nonnormal, as JB test statistic substantially exceeds the critical value of 5.99 at 5 percent significance level. Certain variables such as absolute market return and dummy variables are usually nonnormal, as their values are generated from specific computations. The descriptive statistics of these sample data are reported for readers who understand the nature of their distributions. Except for bullish and crisis dummy variables, all other variables are highly stationary. The ADF test statistic substantially exceeds the critical value of -2.87 for these variables. However, variables such as CASDpFUND , returns on GSCI gold, and oil indices and exchange rate return data are serially uncorrelated, as null hypothesis for no serial correlation up to 20 lags is accepted under the Ljung-Box Q test. Other variables are highly serially correlated.

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EJAE 2020  17 (1)  20 - 36

CHAMIL W. SENARATHNE, K, W., JIANGUO, W.  HERD BEHAVIOUR IN THE CRYPTOCURRENCY MARKET: FUNDAMENTAL VS. SPURIOUS HERDING

Figure 1 – Distributions of CSAD and return on CCi30 Index

As Figure 1 exhibits, CCi30 index return is highly volatile during 2017 and 2018. This period is characterized by the largest cluster of price changes in the history of cryptocurrency trading. CSAD driven by fundamental information seems to fluctuate somewhat during 2015 and 2016. No significant variation in CSAD driven by fundamental information is observed from 2017 to 2018, although a significant variation in market return (i.e., CCi30 index return) can be observed during this period.

This provides an indication as to whether the herding during bullish and crisis periods (i.e., 2017-2018) was not driven by fundamental information pertaining to cryptocurrency price changes. Furthermore, non-fundamental CSAD is highly variable from 2017 to 2018, and seems to correlate somewhat with CCi30 index return. However, a conclusion on the above can only be reached based on the results of herding regression specifications outlined above.

Empirical Findings As Table 3 outlines, US equity risk premium, cryptocurrency market return, and US/Euro exchange rate return are not significant for explaining CSAD. However, GSCI index for oil and gold are significant in the regression equation (5) at 10 percent significance level.

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CHAMIL W. SENARATHNE, K, W., JIANGUO, W.  HERD BEHAVIOUR IN THE CRYPTOCURRENCY MARKET: FUNDAMENTAL VS. SPURIOUS HERDING

Numerous papers show that the cryptocurrency returns are not correlated with macro/microeconomic variables; thus, exhibiting a speculative nature of price changes. The scholars have argued that it provides significant diversification benefits (see, e.g., Briere et al., 2015; Baur et al., 2018) based on the assessment of correlation between cryptocurrency and other speculative assets. However, CSAD is shown to provide some predictive power of GSCI oil and gold commodity index returns. ARCH and GARCH coefficients are highly statistically significant at 5% significance level. The sum of π and λ is less than unity, indicating a good fit for the underlying return series volatility process. Since the leverage effect is not observed in the cryptocurrency market for the considered period, the GARCH (1, 1) model more preciously captures the time-varying volatility.

Table 3 – CSAD Regression Results

Coefficient

Value

t-statistic

P-value

ϕ ψ1 ψ2 ψ3 ψ4 ψ5 a π λ

0.024**

27.85

0.000

-0.104

-1.051

0.293

-0.071*

-1.874

0.061

0.200*

1.800

0.072

0.013

0.455

0.649

-0.171

-0.859

0.391

5.6E-05**

2.835

0.005

0.176**

3.573

0.000

0.792**

17.124

0.000

1. **Statistically significant at 5 percent, assuming returns are conditionally normally distributed. *Statistically significant at 10 percent.

2. The coefficients are estimated using the methods described by Bollerslev and Wooldridge (1992) for obtaining quasi-maximum likelihood (QML) covariances and robust standard errors.

3. Log likelihood is 2154.78 and Durbin-Watson statistic for autocorrelation in the residuals from the regression above is 1.106 and Akaike info criterion for the model is -4.098.

Table 4 outlines the test results of general herding regressions. The results show that the coefficient γ3 , which captures the herd behaviour of investors (on fundamental information) in the cryptocurrency market, is positive and statistically insignificant at 5 percent significance level. This confirms that there is no evidence for herding on fundamental information (e.g., information regarding economic policy uncertainty, financial and economic crisis, economic innovation, regularity formwork, economic growth, IT sector innovations, etc.) in the cryptocurrency market during the sampling period. Herding on fundamental information generally results in inefficient market conditions (Bikhchandani and Sharma 2000). However, there is a tendency to herd on non-fundamental information as coefficient γ3 is negative and statistically significant at 1 percent significance level. This non-fundamental information may include information concerning cryptocurrency trading itself, for example, ICO project, trading restrictions, system failures such as hacking, frauds, trading suspension, or restrictions on cash withdrawal, etc. Concerning stock markets, Kremer and Nautz, (2013, p. 1) argue that unintentional herding can sometimes be inefficient if the trading is not driven by fundamental values.

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EJAE 2020  17 (1)  20 - 36

CHAMIL W. SENARATHNE, K, W., JIANGUO, W.  HERD BEHAVIOUR IN THE CRYPTOCURRENCY MARKET: FUNDAMENTAL VS. SPURIOUS HERDING

Unlike assets trading in other markets, there are no underlying assets for cryptocurrency trading.

Therefore, there is no meaningful information flow that reflects the true value of underlying cryptocurrencies. As such, the cryptocurrency price changes can be pseudo-efficient based on the findings above.

These findings do not suggest that traditional assets could be diversified with cryptocurrencies simply on the basis of correlation (e.g., low correlation) between cryptocurrencies and traditional assets. If diversified, care must be taken in periodically evaluating the investment portfolio for possible underestimation of true risk associated with cryptocurrency investments (See Šoja and Senarathne 2019).

Table 4 – General Herding Regression Results Dep. Variable

γ1

γ2

γ3

π

λ

AIK

DW4

LogL

CSAD FUND pt

0.012* (9.355)

0.004 (0.800)

0.003 (0.114)

0.152* (3.371)

0.809* (20.25)

-9.26

2.15

4854

CSAD NONFUND pt

0.013 (0.510)

0.490* (8.182)

-1.217* (-3.232)

0.193* (4.170)

0.765* (13.79)

-4.30

1.23

2256

Notes: 1. Asymptotic t-statistic appears in parenthesis.

2. *Statistically significant at 1 percent, assuming returns are conditionally normally distributed.

3. The coefficients are estimated using the methods described by Bollerslev and Wooldridge (1992) for obtaining quasi-maximum likelihood (QML) covariances and robust standard errors.

4. DW is the Durbin-Watson statistic for autocorrelation in the residuals from the respective regression. Field (2000)’s rule of thumb suggests that values under 1 or more than 3 are a definite cause for concern and any value within the rage is acceptable.

5. LogL is the Log likelihood value and AIK is the Akaike info criterion.

Table 5 reports the herding regression test results under different market conditions. Equations 10 and 11 estimate the impact of herding under up-trending market and other than upward-trending market conditions during the sampling period. Using logistic regression techniques, Bouri et al. (2018b) find evidence for significant herding when uncertainty increases. Therefore, it is likely that herding may be more intensive under extreme market conditions. More importantly, Vidal-Tomás et al. (2018) find evidence for herding only during down-trending markets. However, the findings of this study show that the cryptocurrency market is characterised by herding during down-trending and upward-trending mark conditions on non-fundamental information, as coefficients γ3 and γ4 are negative and highly statistically significant at 1 percent significance level. Herding on non-fundamental information does not indicate inefficient conditions in the cryptocurrency market, although herding on fundamental information does. Given the lack of intrinsic value of cryptocurrency, this form of herding can lead to pseudo-efficient conditions as opposed to the market-efficiency concept put forward by Fama (1965) and many of his successors (see, e.g., Tobin 1984; Roll 1988). There is no evidence for the presence of herding during other market conditions (bullish, crisis, and high market volatility) under fundamental and non-fundamental CSAD regressions, as coefficients γ3 and γ4 are not negative, although some coefficients are significant.

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EJAE 2020  17 (1)  20 - 36

CHAMIL W. SENARATHNE, K, W., JIANGUO, W.  HERD BEHAVIOUR IN THE CRYPTOCURRENCY MARKET: FUNDAMENTAL VS. SPURIOUS HERDING

Table 5 Herding under Market Conditions Equ.

10& 11

12& 13

14& 15

17& 18

γ1

γ2

γ3

γ4

π

λ

AIK

DW4

LogL

CSAD FUND pt

0.009* (1.687)

0.017** (3.225)

0.006 (0.189)

-0.012 (-0.325)

0.153** (3.375)

0.808** (20.15)

-9.28

2.14

4855

CSADNONFUND pt

-0.463** (-6.203)

0.563** (6.717)

-1.096** (-2.492)

-1.792** (-2.503)

0.197** (4.217)

0.762** (13.77)

-4.29

1.23

2257

CSAD FUND pt

0.014** (7.495)

0.012** (6.962)

0.033** (2.002)

0.008 (0.740)

0.153** (3.312)

0.807** (19.63)

-9.27

2.15

4855

CSADNONFUND pt

0.048* (1.752)

-0.006 (-0.113)

1.240** (4.450)

2.258** (5.907)

0.160** (4.359)

0.819** (22.17)

-4.22

1.21

2217

CSAD FUND pt

0.013** (8.548)

0.012** (4.306)

0.019* (1.910)

0.020 (0.983)

0.152** (3.354)

0.810** (20.27)

-9.26

2.15

4854

CSADNONFUND pt

0.030 (1.081)

-0.010 (-0.248)

2.266** (9.067)

0.771** (2.681)

0.160** (4.397)

0.822** (22.33)

-4.24

1.21

2227

CSAD FUND pt

0.013** (5.904)

0.012** (7.053)

0.041** (1.999)

0.017 (1.354)

0.153** (3.368)

0.809** (20.37)

-9.26

2.15

4856

CSADNONFUND pt

0.021 (0.481)

0.047* (1.840)

3.032** (6.998)

0.982** (4.538)

0.167** (4.655)

0.814** (22.32)

-4.26

1.15

2236

Dep. Var.

Note: 1. Asymptotic t-statistic appears in parenthesis.

2. **Statistically significant at 5 percent assuming returns are conditionally normally distributed. *Statistically significant at 10 percent 3. The coefficients are estimated using the methods described by Bollerslev and Wooldridge (1992) for obtaining quasi-maximum likelihood (QML) covariances and robust standard errors.

4. DW is the Durbin-Watson statistic for autocorrelation in the residuals from the respective regression. Field (2000)’s rule of thumb suggests that values under 1 or more than 3 are a definite cause for concern and any value within the rage is acceptable.

5. LogL is the Log likelihood value.

CONCLUDING REMARKS The notion of herding was first rooted in zoology before it had been widely used in sociology, psychology, economics, and finance. It is the act of bringing individual animals together into a group and maintaining or moving the group from place to another; where the herder directs the animals (the animals need not worry about where they go). However, there is no-one to direct investors in speculative markets (e.g., cotton futures, stock markets, oil and gold, etc.); instead, the investors are directed by the information they have received. As such, investor herding in speculative markets can only be identified with reference to the information pertaining to underlying assets (e.g., firms).

The initial regression results show that the CSAD can only be explained by GSCI oil and gold index return. No relationship exists between CSAD and other variables such as return on CCi30, US equity risk premium, and US/Euro exchange rate return. These findings are in line with the observations of Baur and Lucey (2010), Briere et al. (2015) and Baur et al. (2018), in that for return on stocks, exchanges rates are uncorrelated with cryptocurrency returns.3 3 These studies examine the relationship between cryptocurrency return and return on other speculative variables such stocks, exchange rates, oil and gold. However this study finds that CSAD can be explained by the GSCI oil and gold index returns.

31

EJAE 2020  17 (1)  20 - 36

CHAMIL W. SENARATHNE, K, W., JIANGUO, W.  HERD BEHAVIOUR IN THE CRYPTOCURRENCY MARKET: FUNDAMENTAL VS. SPURIOUS HERDING

The herding regression under normal market (i.e., general market) conditions shows that a strong tendency exists to herd on non-fundamental information that explains CSAD of returns. This clearly indicates the speculative nature of cryptocurrency price changes, and supports the argument that cryptocurrency returns cannot be predicted on the basis of fundamental economic information (e.g., major macroeconomic announcements), as documented by a number of scholars. Furthermore, herding regression under different market conditions reveals that herding on non-fundamental information in the cryptocurrency market is more pronounced during an upward-trending market and other than upward-trending market periods. No evidence for herding on fundamental information could be observed under normal or other market conditions (e.g., bullish, crisis, high volatility).

The findings of this research have several implications for future research. Although the cryptocurrency market may be efficient at the pricing stage, as shown by numerous papers, herding on non-fundamental information suggests that the trading is functionally efficient. However, this form of efficiency does not reflect the true underlying security-specific information (e.g., firm-specific information in the case of stock market) in the absence of underlying assets (i.e., lack of intrinsic value) in the cryptocurrency trading platform. As such, cryptocurrency can be regarded as a pseudo-efficient instrument for speculative exchange traders. Previous research on market efficiency must be revisited to research the behavioural factors affecting cryptocurrency price changes. These findings do not suggest that traditional assets could be diversified with cryptocurrency simply on the basis of correlation (i.e., low correlation) between cryptocurrencies and traditional assets. Furthermore, the findings are important for investors and portfolio managers to understand the market dynamics and the role of cryptocurrency in portfolio diversification.

ACKNOWLEDGMENT The author would like to thank the Editor-in-chief, Nemanja Stanišić, the Managing Editor, Gordana Dobrijević and the editorial board. Useful comments from three anonymous reviewers shall be gratefully acknowledged. Usual disclaimer applies.

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Philippas, N., Economou, F., Babalos, V., & Kostakis, A. (2013). Herding behavior in REITs: Novel tests and the role of financial crisis. International Review of Financial Analysis, 29, 166-174.

DOI: https://doi.org/10.1016/j.irfa.2013.01.004.

Poyser, O. (2018). Herding behavior in cryptocurrency markets. arXiv preprint arXiv:1806.11348. Available at https://arxiv.org/pdf/1806.11348.pdf.

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Senarathne, C. W. (2019). The leverage effect and information flow interpretation for speculative Bitcoin prices: Bitcoin volume vs ARCH effect. European Journal of Economic Studies, 8(1), 77-84. DOI: https://doi.org/10.13187/es.2019.1.77.

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Vidal-Tomás, D., Ibáñez, A. M., & Farinós, J. E. 2018 (In Press). Herding in the cryptocurrency market: CSSD and CSAD approaches. Finance Research Letters (forthcoming). DOI: https://doi.org/10.1016/j.frl.2018.09.008.

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CHAMIL W. SENARATHNE, K, W., JIANGUO, W.  HERD BEHAVIOUR IN THE CRYPTOCURRENCY MARKET: FUNDAMENTAL VS. SPURIOUS HERDING

PONAŠANJE GOMILE NA TRŽIŠTU KRIPTOVALUTE: STVARNO VS LAŽNO GOMILANJE

Rezime: Ovaj rad ima za cilj da istraži da li gomilanje investitora, na tržištu kriptovalute, dovodi do korelacije u prinosu kriptovalute – uz upotrebu metodologije Čenga i dr, Galariotisa i dr, a na primeru uzorka podataka u vremenskom okviru 30.03.2015-24.05.2019. Početni rezultati regresije ukazuju na to da apsolutna, standardna devijacija širokog spektra u prinosu može biti objašnjena samo indeksom prinosa GSCI ulja i zlata, kao i da ne postoji spona između apsolutne standardne devijacije širokog spektra i drugih varijabli regresije, poput prinosa u vezi sa Cci30, premije za tržišni rizik SAD-a, kao i prinosa koji se odnosi na kurs američkog dolara ili Evra. Rezultati regresije gomilanja – u standardnim uslovima tržišta, pokazuju da postoji izražena sklonost gomilanja na ne-fundamentalnim informacijama, što objašnjava apsolutnu standardnu devijaciju prinosa širokog sprektra. Kao takvi, prinosi kriptovalute ne mogu da se predvide, a na osnovu fundamentalnih, stvarnih informacija, relevantnih za ekonomiju (npr. značajne objave na makroekonomskom planu). Gomilanje zasnovano na informacijama koje nisu fundamentalne učestalije je, kako je primećeno, tokom perioda rasta na tržištu, dok ovakve vrste gomilanja nema u drugačijim okolnostima na tržištu. Premda teorija sugeriše da gomilanje zasnovano na infomacijama koje nisu od fundamentalne vrednosti dovodi do efikasnijih ishoda, prethodno izloženo ne govori u prilog diversifikacije tradicionalnih sredstava kriptovalutom, na osnovu slabe korelacije. Budući da kriptovaluti nedostaje intrinzična vrednost, takva razmena dovodi do nastanka pseudo-efikasne platforme za trgovinu spekulativnih investitora. Na kraju rada, navode se implikacije u vezi sa budućim istraživanjem.

36

Ključne reči: ponašanje gomile, kriptovaluta, fundamentalne informacije, CASD, portfolio diversfikacija, pseudo-efikasnost, intrinzična vrednost.

JEL Classification: G15, G14

EJAE 2020, 17(1): 37 - 48 ISSN 2406-2588 UDK: 336.744 004.738.5:339.13 338.23:336.74 DOI: 10.5937/EJAE17-21873 Original paper/Originalni naučni rad

THE POTENTIAL EFFECTS OF CRYPTOCURRENCIES ON MONETARY POLICY Nenad Tomić*, Violeta Todorović, Božidar Čakajac Faculty of Economics, University of Kragujevac, Serbia

Abstract: All current cryptocurrencies are controlled by private entities, so that the issue of impact on monetary system becomes very important. Autonomous decisions by private entities concerning the money supply could diminish the ability of central banks to implement monetary policy effectively. The subject of this paper is the influence of alternative cryptocurrency forms on the monetary system. The aim of this paper is to determine the ability of central banks to conduct monetary policy successfully in conditions of widespread use of cryptocurrencies in payment transactions. The situation in the cryptocurrency market is compared with the phases of Internet development and the current situation on other markets of electronic payment systems. It is concluded that cryptocurrencies do not have the capacity to endanger the traditional monetary system at the current level. Bearing in mind the early maturity of this market, certain predictions of possible future trends have been made. In the case of private cryptocurrency usage growth, central banks could partially or completely lose influence over monetary policy. The proposed solution is the development of national cryptocurrencies that would ensure the retention of seigniorage to central banks and the prevention of further use of private cryptocurrencies in criminal activities.

Article info: Received: May 24, 2019 Correction: February 24, 2020 Accepted: February 25, 2020

Keywords: monetary policy, cryptocurrencies, stablecoins, private money.

INTRODUCTION The application of information and communication technologies (ICT) changed the business model of many activities. The potential impact of intensive ICT application to central banks and the potential for monetary policy management has become a particularly sensitive issue (King, 1999). *E-mail: ntomic@kg.ac.rs

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TOMIĆ, N., TODOROVIĆ, V., ČAKAJAC, B.  THE POTENTIAL EFFECTS OF CRYPTOCURRENCIES ON MONETARY POLICY

Internet commercialization created infrastructural prerequisites for development and implementation of software electronic money. According to the legal regulation, the licence for issuing electronic money was granted to private entities that met the specified conditions. The possibility to manage the electronic money emission created a real threat that traditional measures of central banks would lose its effectiveness (Goodhart, 2000).

Early e-money operational solutions were of limited duration and had a modest impact. Their influence on the payment system in developed countries was negligible. Therefore, fear of the impact of electronic money seemed to be unjustified during the first decade of the 21st century. However, the Bitcoin emergence and the expansion of alternative forms of cryptocurrencies brought about this issue. It was proved that cryptocurrencies were far more resistant than early electronic money solutions.

Due to the great usage of social networks and virality of modern communication, their usage was soon exponentially increased. It should be kept in mind that there was not only an increase in the number of users’, but also in the functional diversification of the system.

The subject of this paper is the influence of alternative cryptocurrency forms on the monetary system.

Therefore, the effects of private and state cryptocurrencies could be separated. The goal of this paper is to examine the capacity of central banks to conduct monetary policy successfully under conditions of widespread usage of cryptocurrencies. Bearing in mind that the process of monetary regulation is complex even under the existing conditions, the question of whether the inclusion of cryptocurrencies into the monetary system would be a mitigating or aggravating circumstance for monetary policy makers arises.

In the first part of this paper, absolute and relative representation of cryptocurrencies on the global market will be examined. In the second part, the degree to which cryptocurrencies fulfil the money functions will be determined. Based on the first two sections, a conclusion will be drawn concerning to what extent cryptocurrencies affect the monetary system. Their value on the global market is too low, while the number of performed transactions is also at the level that cannot provoke a global shock.

Having in mind that this is quite a dynamic category, the possible effects of potential cryptocurrencies’ wider usage, or the creation of national cryptocurrencies, will be analysed.

THE CURRENT SHAPE OF THE CRYPTOCURRENCIES’ MARKET The economic model of cryptocurrencies has its own theoretical foundation in the concept of synthetic commodity money. This concept was formulated by Coase (1972, pp. 143-144) and represents a hybrid form of commodity money and Fiat money. Commodity money is characterized by a limitation in absolute quantity, because its availability depends on the physical limitations of goods which is its basis. The goods used as a base have non-monetary and monetary value. Fiat money is exclusively used for monetary purposes. Furthermore, there is no absolute limitation on the amount available. Since the marginal costs of making new cash are close to zero, central banks could produce it limitlessly, if that had no consequences on monetary system. The possibility of a continual increase in cash offer means that its availability does not have absolute restraint. Therefore, it could be concluded that cash availability is artificially restricted or, so to say, restricted by the will of responsible individuals who manage its emission.

Synthetic commodity money is characterized by an absolute limitation, thus making it more similar to commodity money. At the same time, there is no non-monetary use, which is a characteristic inherent to fiat money. Synthetic commodity money represents the medium of exchange without nonmonetary value, for which the creation of new units has been stopped, or has a growing marginal cost (Selgin, 2014, pp. 93-94). In this sense, an additional issue is economically unjustified, or impossible.

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The abovementioned characteristics will have great importance when considering the impact to the conduct of monetary policy.

Since all cryptocurrencies have been controlled by private entities so far, the issue of influence on the monetary system becomes very important. Theoretically, the autonomous decisions of private entities on money supply would prevent central banks from implementing monetary policy efficiently. However, low market capitalization of cryptocurrencies limits their impact to global monetary movements.

In February 2020, the website coinmarketcap.com registered a total number of 5,134 cryptocurrencies, with only 19 of them having a market capitalization higher than USD 1 billion, and only 65 having a market capitalization higher than 100 million US dollars. The total capitalization of the top 100 cryptocurrencies is lower than 120 billion US dollars, and more than a half of this amount belongs to Bitcoin. Previously stated data indicate that the total capitalization of cryptocurrencies does not change significantly with the increase in their total number.

According to the analysis made by Desjardins (2017), the market capitalization of Bitcoin as a representative cryptocurrency was at about 100 billion US dollars at its peak. This value, although very high in absolute amount, is slightly more than 1.3% of the world's total cash in circulation (7.6 trillion US dollars), less than 1.3% of global gold reserves (7.7 trillion US dollars), only 0.13% of the world stocks value (73 trillion US dollars), or 0.11% of the global money supply (90.4 trillion US dollars).

Bitcoin was used as a representative example because of its higher capitalization in comparison to all other cryptocurrencies together. It should be kept in mind that due to the global fall in the value of all cryptocurrencies in 2018, the share in all observed aggregates would have been much lower. It could be concluded that cryptocurrencies at the current level of usage do not have the capacity to endanger the traditional monetary system.

McCann (2018) made graphical representations of the trend in the total number of Internet users and the total number of all cryptocurrency users. The total number of Internet users in the interval 1990-1995 and total number of cryptocurrency users in the interval 2013-2018 follow almost identical patterns. Not only are the shapes of diagrams are similar, but the absolute numbers are also comparable, since they are twice higher for the Internet users on average. The author concludes that the cryptocurrency market may be dynamic, but is still far from being mature. A similar observation was made by Paul (2017), who states that the cryptocurrencies were in 2017 in the place in which the Internet was in 1994, not only because of the number of users, but also in the sense of content growth. Therefore, in further predictions related to the cryptocurrencies influence, one must keep in mind that the current state is probably just one stage in the evolution process.

FULFILLMENT OF BASIC MONEY FUNCTIONS Having achieved unexpectedly high values, certain cryptocurrencies have directed public attention towards the investment aspect. Although many economists treat them as hybrid financial instruments, cryptocurrencies are by their definition a class of electronic money. It is, therefore, necessary to examine to what extent cryptocurrencies fulfill the basic functions of money.

The basic money functions are: medium of exchange, unit of account, and store of value (Mankiw, 2015, 82). Once again, Bitcoin could be taken as an example. According to the website www.blockchain. com, slightly less than 81.4 million Bitcoin transactions were performed in the year 2018. The website www.bitcoinexchangeguide.com states that their total value was 2.2 trillion US dollars.

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These are very high amounts in absolute terms, but in order to understand their relative importance, it is necessary to make a comparison with some established electronic payment system.

The Visa annual report states that in the period from September 30, 2017 to September 30, 2018, Visa processed 124 billion transactions, with total of about 8.2 trillion US dollars. While there is an obvious difference in transaction numbers, it could be concluded that the values of the total amount are much closer. It should be noted that the total value of Bitcoin transactions is more dependent on the exchange rate to USD than on the transaction number. Thus, the total transaction value in 2017 was 0.87 trillion US dollars while in 2016 it was 0.03 trillion US dollars. Bearing in mind that in 2019, after only a month and a half, the price of Bitcoin was halved, a sharp drop in the annual transaction value could be expected.

In order to completely understand the impact of Bitcoin transactions on the economy, a participant analysis should be made. Most often, participants are individuals who appear in numerous transactions, both as payers and payees. They are traders who frequently purchase and sell in order to make a profit. This increases both the total number and the total amount of transactions, without spreading the scope of the participants.

In order to satisfy the unit of account function, money value should be stable (Marković & Furtula, 2012, p. 18). Sudden and frequent changes of value expressed in convertible currencies diminish the possibility of its usage as a unit of account. In that case, goods and services change their values too frequently and suddenly, which equally complicates merchants’ business and choices of customers. All cryptocurrencies are characterized by unstable values expressed in convertible currencies (US dollar is most commonly used). Bitcoin shows the highest volatility, since it is most often used in speculative transactions. Table 1 displays the annual rate of return for Bitcoin in the period 2011-2018. The table clearly displays the extremely high annual return (both positive and negative) that makes Bitcoin unusable as a long-term unit of account for goods and services.

Table 1: Bitcoin annual rate of return for period 2011-2018 (in US dollars) Year

Opening price

Closing price

Annual return

2011

0.30

4.72

1473%

2012

4.72

13.51

186%

2013

13.51

758

5507%

2014

758

320

-58%

2015

320

430

35%

2016

430

968

125%

2017

968

13860

1331%

2018

13860

6321

-54%

Source: https://bitcoinexchangeguide.com/bitcoin-btc-proves-to-be-profitable-when-measuring-yearlyreturns-for-investors-2011-2018/

The cryptocurrency value instability could be considered the key factor of their failure to perform the store of value function. The previous table illustrates the annual rates of return, but it does not show monthly changes in value. For example, for the period October 2017 - March 2018, the monthly rates of return were, respectively, 127.81%, then 39.77%, then -26.65%, then 1.41%, and finally -32.82%.

Individuals or companies that want their wealth to be stable and predictable would not choose to store value in a currency that manifests expressed unpredictability in such a short period of time. In the example of Bitcoin, it could be concluded that cryptocurrencies do not fulfill any of the three basic money functions.

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TOMIĆ, N., TODOROVIĆ, V., ČAKAJAC, B.  THE POTENTIAL EFFECTS OF CRYPTOCURRENCIES ON MONETARY POLICY

In the report made for the Committee on Economic and Monetary Affairs of the European Parliament, Claeys, Demertzis & Efstathiou (2018) state that a medium of exchange needs to be widely used and to have a relatively stable value in order to be considered as money. The cryptocurrencies at this point do not possess any of these characteristics. Lielacher (2019) provides certain projections of Bitcoin user numbers for the end of 2018. A total of 32 million accounts have been created through various digital wallets. This does not mean that these are unique users, because a single user can create multiple personal accounts. In addition, numerous accounts are inactive for a long period of time. It is estimated that 7.1 million of users are active globally. (One should bear in mind that the methodology for determining the level of user activity is debatable; the question arises: is a long-term investor, who bought a certain amount of a cryptocurrency more than a year ago and who has been maintaining that amount, considered active?) Although it is not an insignificant number, it is far below the number of active PayPal users (235 million), MasterCard users (604 million) or Visa users (736 million). The report’s authors believe that money should be secured by real assets (e.g., gold) and/or that there is the state coercion so that money could be widely used. In the case of private cryptocurrencies, both conditions are lacking, so not even an increase in user numbers would lead to widespread use of it.

The algorithm creators are aware of the high volatility problem. One of the potential solutions is the cryptocurrency class named "stablecoins". The idea is that the coin supply is adapted to demand in order to achieve stable value. In this way, it is possible to achieve a stable exchange rate against convertible currencies (for example, against the US dollar). An example of stablecoins is Basis. At first, the concept of stable cryptocurrencies may seem a logical solution, but it is economically unsustainable. Namely, all existing cryptocurrencies, including Basis, have been designed with the goal to be globally used. In order to fulfill the money functions, the optimal currency area law must be applicable to all cryptocurrencies, as well as for all other currencies. It is an area in which economic efficiency is maximized if a single currency is used (Mundell, 1961). Since the entire world (or a developed part of the world) could not be an optimal currency area, this means that even the concept of "stablecoins" cannot provide global value stability. In other words, such cryptocurrencies will be fully linked to one convertible currency, but in time their value in other currencies will become more and more unstable. That's why the report’s authors consider that it is more appropriate to design a "national cryptocurrency", rather than a global one that adapts only to one national currency.

THE FUTURE OF MONETARY POLICY Bitcoin turned 10 on the 1st of January of 2019. Cryptocurrencies at this level of development could not diminish the central banks’ ability to conduct monetary policy. Nevertheless, their 10-year existence has changed the approach to electronic money management. The first change relates to the knowledge that private electronic money systems could become long-termed. The Fifth European Union AntiMoney Laundering Directive (Directive 2018/843) refers to cryptocurrencies (which are referred to as "virtual currencies" in the text of the directive, though provisions clearly state what the legislators refer to) and stored value cards. This is an indicator of the seriousness with which regulators will approach these categories in the future. The second change relates to the blockchain as the technological base of cryptocurrencies. It is a technology that has proved to be excellent in preserving large databases so far, because it simultaneously enables mass access while preventing malicious attacks. There is no doubt that, regardless of the fate of first cryptocurrencies which used it, blockchain will become part of the future payment system.

Although existing cryptocurrencies do not have the capacity to influence monetary policy, it could be expected that their presence in the future would increase. Two scenarios are possible: private publishers could create one or more cryptocurrencies that will start to be widely accepted as a means of payment; or some countries could create their own electronic money based on blockchain or related technology.

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Potential Effects of Private Cryptocurrencies In 1976, Hayek proposed the creation of competitive supranational currencies that would be used in accordance with the market’s principles. He considered that it was necessary to allow the money issuance to the private sector, because national money would always be political money. If certain private currencies proved to be stable, consumers would start using them, and abandon national currencies completely. The emergence of private cryptocurrencies is an attempt to realize this idea. Still, not even one of the multiple cryptocurrencies has succeeded in extruding national currencies, although current events in Venezuela indicate that this moment may not be so far away.

Figure 1 represents so-called "Money Flower". It is a web of sets that define different money forms from the standing of our key aspects: private or public issuer, convertible or non-convertible money, physical or electronic form, indirect transfer with an intermediary, or direct transfer without an intermediary. The space within the sets is the presence of the characteristic listed at the very edge of the set, while all the space outside represents its absence. It is important to pay attention to the theoretical difference between Bitcoin and convertible cryptocurrencies - with the same characteristics when it comes to the mean of transfer and manifestation, the potential convertible cryptocurrencies would be exchangeable with other forms of property by the definition - deposit or cash.

Figure 1: The classification of money forms based on four key aspects, known as the “money flower”

Electronic

Public

Hayek Bank (Inconvertible Deposits)

Convertible

Standard Bank Deposits

Peer-to-Peer Central Bank Deposit (Universal Money/Vollgeld)

Cash

FedCoin

Central Bank Deposits Goldstandard FedCoin Goldstandard Banknote Goldstandard

Convertible Cryptocurrency

Hayek Banknote

Bitcoin

Source: Bech & Garratt (2017)

Cryptocurrencies have properties which make them a preferable means of payment. They offer a certain degree of anonymity, which is often compared to the degree offered by cash (although the actual anonymity differs among the various cryptocurrencies). Payments are not geographically limited; clearing and settlements are done very quickly. Due to the speed and absence of currency conversion costs, they are particularly suitable for cross-border payments.

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TOMIĆ, N., TODOROVIĆ, V., ČAKAJAC, B.  THE POTENTIAL EFFECTS OF CRYPTOCURRENCIES ON MONETARY POLICY

They could be divided into very small parts, which make them suitable for micropayments, so it is expected that they would be more applicable in a modern services-based economic model.

If a cryptocurrency achieved a high rate acceptability in payments, it could easily compete with cash and deposit money for retail payments, as well as traditional electronic payment systems in ecommerce. If the influence on cash is ignored at the moment, it could be concluded that a wider usage of cryptocurrencies could simultaneously reduce the deposit money usage. Cryptocurrency transfers are performed directly between payer and payee through the appropriate infrastructure, but without the participation of intermediaries (He et al., 2017). Settlement of all payments made with deposit money is done through an intermediary (commercial banks), which indicates that users will keep the lower level of deposits in banks. According to the transfer mechanism, cryptocurrencies resemble cash more than deposit money. The final result would be the reduction in bank deposit and erosion of credit potential.

If users use cryptocurrencies to a greater extent, central banks could lose their monopoly over the money supply. In situations where the private sector entity is an issuer of widely accepted electronic money, the central bank partially loses control over reserve requirement system and monetary multipliers, in addition to loss of seigniorage (Sedlarević, Furtula & Tomić, 2015, pp. 1246-1247). The possibility for managing interest rates was not commented in the paper. However, some authors expressed their fear earlier that interest rate management in these situations would become completely inefficient (Friedman, 2000). If the central bank wants to influence the acceleration or slowdown of economic activities through interest rates, the effect will be limited or completely neutralized by consumers’ migration to cryptocurrencies as a means of payment. Further efforts of central banks could be related to the effects on the national currency, which is used less and less, and therefore diminishes the effectiveness of monetary policy. Similar processes were recorded many times in the case of dollarization of monetary systems of small countries. If most of the national financial system is based on foreign currency, monetary policy measures have no impact on economic activity (He, 2018). The same analogy applies to cryptocurrencies, but with the difference that it could also affect developed countries.

Almost all cryptocurrencies that are in use at the moment have a controlled offer. An example is Bitcoin, whose algorithm is programmed to increase the amount of coins at a constant rate, which halves every four years. Others have a completely constant offer, which could be absolutely limited or unlimited. Because of these characteristics, the cryptocurrencies tend to create deflationary effects. In an economic model that relies on cryptocurrencies with such characteristics, the greatest risk would be a deflationary risk. The cryptocurrencies controlled by algorithms do not have the tools to respond to demand shocks, nor to fulfill the lender of last resort function.

The Potential Effects of National Cryptocurrencies In the previous section it was explained that cryptocurrencies could be an extremely powerful economic instrument in private hands. States should not be inert during the permanent ICT evolution.

Declaring all cryptocurrencies completely illegal and banning their usage would be a very bad solution.

States should look at the current trends and consumer preferences. A modern approach is the best defence from innovations that have the potential to reduce the efficiency of the state. Instead of direct battle against cryptocurrencies, states can decide to issue their own operational solutions. The state may have different motives for creating a central bank digital currency (CBDC). One of them is the faster and easier execution of transactions and the almost immediate availability of funds (DeVries, 2016, p. 5). Traditional payment infrastructure is expensive and slow, which is particularly evident in international business. Blockchain-based connectivity has the potential to facilitate business integration by removing the burdens of time-consuming procedures and high currency conversion costs (Team, 2016).

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Motivation may also be the control of the payment system, with the aim of preventing money laundering, terrorist financing, and criminal activities. Countries that depend heavily on remittances from citizens living abroad may be interested in a faster and cheaper inflow of money, unburdened by the high fees paid to remitters. Finally, certain countries may decide to develop a national cryptocurrency due to the desire to overcome economic sanctions or other international barriers.

With regard to the introduction of national cryptocurrencies, a distinction should be made between the situation in which the cryptocurrency completely replaces cash, and the one in which it serves as an addition to the existing payment system. The first situation is unlikely to occur anywhere in the next few years. The closest country to find to such a solution in the future could be Sweden, which announced the complete withdrawal of cash in 2023. One of the elements for such actions should be the introduction of the e-crown, national electronic money that may not necessarily be cryptocurrency (Sveriges Riksbank, 2017). As for the second situation, several countries have either fully implemented certain forms of national cryptocurrencies, or are in the final stages of implementation.

A pioneering venture in this direction is the creation of the national cryptocurrency Petro in Venezuela in late 2018 (Chohan, 2018). It should have served as a mean of safeguarding value (hence, it should have performed one of the basic functions of money) in a situation when the national currency, the Bolivar, lost that function due to excessive inflation as a result of external economic sanctions.

Petro's value was supposed to be backed by Venezuela's oil reserves. Strictly speaking, Petro is not a true cryptocurrency, because it is offered through an initial coin offering (ICO), it cannot be mined and the system is controlled in a fully centralized manner. Therefore, Petro can be classified as a digital token, created on the basis of NEM blockchain (Cavicchioli, 2018). In practice, the experiment did not prove successful; the world’s largest cryptocurrency exchanges have not accepted to list Petro, and its trade within national borders has remained negligible (Brown, 2019).

Back in 2015, it was announced that Tunisia would be the first country to introduce a national cryptocurrency which would be called eDinar or BitDinar (O'Neal, 2019). The whole project was to be carried out by a national postal company, and was meant to target adults who do not have access to traditional banking services in Tunisia, as well as Tunisian citizens living in Europe. The technical basis should have been provided through the Monetas mobile application. However, the company in charge of developing the application went bankrupt, and the Central Bank of Tunisia became involved in the venture. Although there was some news in November 2019 that the eDinar had finally become operational, the Central Bank of Tunisia denied this, claiming that tests were underway to demonstrate the feasibility of the whole concept (Nelson, 2019).

Numerous other countries have announced that they are considering the introduction of national electronic money, including Dubai within the United Arab Emirates, followed by the Marshall Islands, Senegal, China and Russia (Phillips, 2020). In the case of its implementation in traditional payment infrastructure, the role of central banks and other state bodies would not change significantly. Cash would still exist as an anonymous substitute for electronic payments. In the case of transformation into a completely cashless society, the first step in introducing a national cryptocurrency would be a withdrawal of cash. Since the transactions would be managed by the state through an authorized institution, the motives for the elimination of cash would not be purely monetary. If all payment transactions in the future were made cashless, the state could gain an insight into the overall payment flow of every citizen.

Justification of it could be found in the fight against criminal and terrorist activities; however, there is a justifiable fear of civil liberties’ loss (Tomić & Todorović 2018). States could use available information to monitor citizens permanently, while the absolute control of payment transactions could be used to punish disloyal individuals. The impact to monetary policy would depend on the implemented model of cryptocurrency.

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National cryptocurrencies could be stored on a card or in a mobile app (value-based solution), or in a special current account at the centralized institution, which could be an organizational part of the central bank (register-based solution). These two models do not have to be strictly considered as substitutes for cash and deposit money respectively, as deposit money could continue to function simultaneously with one or both of cryptocurrency models. The question arises as to what extent the relative position of the central bank, commercial banks, and end users will change.

There would be no revolutionary changes in the case of replacing cash with value-based cryptocurrency solution. The demand for transactional deposits could fall due to the possibility of remote payments via cryptocurrency applications. A substantial change would occur in the case of implementation of a cryptocurrency register-based solution. Central banks could use interest rates to influence the entire money supply, which would increase monetary policy options. In the case of a general fall in demand and a deep recession, central banks could react with the policy of the negative interest rate (Bofinger, 2018). In the case of cash existence or value-based cryptocurrency, consumers could respond to the fall of interest rates by converting their deposit money to cash or cryptocurrency, in this way remaining beyond the reach of monetary policy. Without cash and value-based cryptocurrency, spending would be the only way to prevent long-term loss of owners’ assets.

If the current account opening is generally available to everyone, depositors would transfer all funds to central banks through accounts of secured deposits. Furthermore, the chances of a "digital run on banks" would increase. If the central bank conducts a different interest rate policy on deposit money and cryptocurrencies, consumers could easily decide to transfer all funds to the central bank. The space for commercial bank activities would be significantly narrowed, so these banks would be forced to obtain the missing liquid funds from non-depository sources.

CONCLUSION Cryptocurrencies currently do not have the capacity to endanger the international monetary system. This does not mean that their emergence is completely irrelevant to the monetary policy creators.

If one considers the market maturity and constant increase in the number of participants, it may be concluded that, at some point in the foreseeable future, cryptocurrencies could affect certain aspects of monetary policy. Central banks in countries with unstable national currencies would experience such problems before others. Something similar is already happening now in Venezuela. Causes for this behavior do not have to be necessarily monetary - so individuals can opt to transfer their assets into private cryptocurrencies for privacy reasons to avoid transaction tracking. In this case, even the central banks in countries with stable currencies could experience problems.

A reduction or complete loss of influence on monetary policy would lead to the deepening of economic problems. The basic approach against monetary control loss is to conduct a principled, responsible, and unwavering monetary policy. Maintaining the neutrality of the central bank and depoliticizing monetary policy are clear institutional assumptions. At the same time, the central bank must be open to new ideas and trends. The growing pressure of accepted private cryptocurrencies will not be solved by ignoring reality and by simply banning their use. After an adequate analysis, it should be determined in which percentage cryptocurrencies are really used for criminal and terrorist activities. Measures to prevent such behavior should be defined after. This would stop the additional mystification of private cryptocurrencies.

At this point, issuing their own electronic money seems to be the ultimate solution for all central banks. It does not necessarily have to be technologically close to current cryptocurrencies. It must be some kind of "stablecoin", that is, it must be based on adaptable protocol. Previous claims that global cryptocurrencies could not be adjusted equally to every national currency is in favor of this idea.

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With the issuance of their own electronic money, states would avoid the possibility of the emergence of a powerful private issuer in the shape of a consortium of IT companies, websites, and banks. They would also ensure the effectuation of "digital seigniorage", which could fund their further activities.

The research limitation originates from the very nature of cryptocurrencies. Due to the instability of its value, the relation of market capitalization and monetary aggregates and total value of transactions could change significantly on the weekly basis. All indicators referring to cryptocurrencies should be tied strictly to the time period in which they were determined, and they should not be considered stable.

In terms of analyzing cryptocurrencies with monetary policy, research should be understood as a set of assumptions that could be modified under the influence of new knowledge. Therefore, in the following research studies, steps of the states which announce the project of cash limitation and cash withdrawal should be taken into consideration. The questions of the introduction of national currencies is inextricably linked to the issue of cash withdrawal, so these two processes should be analyzed simultaneously.

REFERENCES: Bech, M. L. & Garratt, R. (2017). Central bank cryptocurrencies. BIS Quarterly Review – September. 55-70 Bofinger, P. (2018). Digitalisation of money and the future of monetary policy. Vox – CEPR Policy portal.

Brown, A. (2019). Venezuela’s Failed Cryptocurrency Is the Future of Money. Bloomberg. May 10th Cavicchioli, M. (2018). The difference between Token and Cryptocurrency. Medium. August 1st Chohan, U.W. (2018). Cryptocurrencies as Asset-Backed Instruments: The Venezuelan Petro. SSRN electronic journal, February 2018. Available at: https://ssrn.com/abstract=3119606 or http://dx.doi.org/10.2139/ssrn.3119606 Claeys, G., Demertzis, M. & Efstathiou, K. (2018). Cryptocurrencies and monetary policy. Policy Department A at the request of the Economic and Monetary Affairs Committee. June Coase (1972) Durability and monopoly. Journal of Law and Economics, 15(1), 143-149. DOI: 10.1086/466731 Desjardins, Ј. (2017). Comparing the world’s money and markets. The Money Project, available at https://money. visualcapitalist.com/worlds-money-markets-one-visualization-2017/ European Commission (2009) Directive 2018/843 amending Directive (EU) 2015/849 on the prevention of the use of the financial system for the purposes of money laundering or terrorist financing, and amending Directives 2009/138/EC and 2013/36/EU, Official Jounrnal of European Union, L series Friedman, B. M. (2000). Decoupling at the Margin: The Threat to Monetary Policy from the Electronic Revolution in Banking. International Finance 3(2), 261–272. DOI: 10.3386/w7955 Marković, D. & Furtula, S. (2012). Monetarna ekonomija. Kragujevac: Ekonomski fakultet Univerziteta u Kragujevcu Goodhart, C. (2000). Can Central Banking Survive the IT Revolution? International Finance, 3 (2), 189–209.

DOI: 10.1111/1468-2362.00048 Hayek, F. A. (1976). The denationalization of money. London: Institute for Economic Affairs He, D., Ross L., Vikram H., Tommaso M. G., Nigel J., Mikari K., Tanai K., Céline R. & Hervé T. (2017). Fintech and Financial Services: Initial Considerations. IMF Staff Discussion Note 17/05. Washington DC: International Monetary Fund He, D. (2018). Monetary policy in digital age. Finance & Development, 55(2), 14-16.

King, M. (1999). Challenges for Monetary Policy: New and Old. Symposium on New Challenges for Monetary Policy. Federal Reserve Bank of Kansas City, Jackson Hole, Wyoming, August 27 Lielacher, A. (2019). How Many People Use Bitcoin in 2019?. Bitcoin Market Journal, February 11th Mankiw, G. (2015). Macroeconomics (9th edition). New York, NY: MacMillan education Mundell, R. A. (1961). A Theory of Optimum Currency Areas. American Economic Review, 51(4), 657–665.

McCann, C. (2018). 12 Graphs That Show Just How Early The Cryptocurrency Market Is. Medium.com, May 6th 46

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Nelson, D. (2019) Tunisia’s Central Bank Denies Reports Claiming It Issued an E-Dinar. Coindesk, November 12th O’Neal, S. (2019) CBDCs of the World: The benefits and drawbacks of national cryptos, according to different jurisdictions. Cointelegraph, June 19th Paul, A. (2017). It’s 1994 in Cryptocurrency. Forbes, November 27th Phillips, G. (2020) These 6 Countries Want a National Cryptocurrency. Blocksdecoded.com, February 11th Sedlarević, L.., Furtula, S. & Tomić, N. (2015). Potencijalni efekti elektronskog novca na monetarnu politiku.

Teme, 39(4), 1235-1255 Selgin, G. (2014). Syntetic commodity money. Journal of Financial Stability, 17(C), 92-99. DOI: 10.1016/j. jfs.2014.07.002 Sveriges Riksbank (2017). The Riksbank´s e-krona project. Report 1, September Team, B. (2016, January 20). Understanding Bitcoin's Growth in 2015. Retrieved from Bitpay Website: https://blog.bitpay.com/understanding-bitcoins-growth-in-2015/ Tomić, N. & Todorović, V. (2018) Challenges of transition to cashless society, in: Babić, V. (ed.) Contemporary issues in economics, business and management, 313-320 Visa Annual report 2018 https://bitcoinexchangeguide.com/bitcoin-btc-proves-to-be-profitable-when-measuring-yearly-returns-forinvestors-2011-2018/ (accessed on 10 May 2019) https://bitcoinexchangeguide.com/bitcoin-btc-transaction-volume-in-2018-surpasses-paypals-performance-viasatoshi-capital-data/ (accessed on 12 May 2019) https://www.blockchain.com/en/charts/n-transactions-total (accessed on 12 May 2019) https://coinmarketcap.com/ (accessed on 18 Febrary 2020)

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POTENCIJALNI EFEKTI KRIPTOVALUTA NA MONETARNU POLITIKU

Rezime: Sve aktuelne sisteme kriptovaluta kontrolišu privatni entiteti, pa pitanje uticaja na monetarni sistem postaje veoma važno. Autonomne odluke privatnih entiteta o ponudi novca mogle bi umanjiti sposobnost centralnih banaka da efikasno sprovode monetarnu politiku. Predmet istraživanja rada je uticaj alternativnih oblika sistema kriptovaluta na monetarni sistem. Cilj rada je utvrđivanje sposobnosti centralnih banaka da uspešno vode monetarnu politiku u uslovima široke upotrebe kriptovaluta u platnom prometu. U radu je stanje na tržištu kriptovaluta upoređeno sa fazama razvoja interneta i trenutnim stanjem na tržištu drugih elektronskih sistema plaćanja. Zaključeno je da kriptovalute na sadašnjem nivou upotrebe nemaju kapacitet da ugroze tradicionalni monetarni sistem. Imajući u vidu ranu zrelost ovog tržišta, izvršena su i određena predviđanja mogućih tendencija. U slučaju rasta upotrebe privatnih kriptovaluta, može se dogoditi da centralne banke delimično ili potpuno izgube uticaj na monetarnu politiku. Predloženo rešenje je razvoj nacionalnih kriptovaluta, koje bi osigurale zadržavanje sinjoraže centralnim bankama i sprečile mogućnost dalje upotrebe privatnih kriptovaluta u kriminalnim aktivnostima.

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Ključne reči: monetarna politika, kriptovalute, stabilne kriptovalute, privatni novac.

EJAE 2020, 17(1): 49 - 66 ISSN 2406-2588 UDK: 005.332:005.35 341.231.14-053.2(497.11) DOI: 10.5937/EJAE17-22487 Original paper/Originalni naučni rad

THE IMPACT OF THE BUSINESS SECTOR ON CHILDREN’S RIGHTS IN SERBIA Nataša Krstić*, Sandra Nešić Faculty of Media and Communications, Singidunum University, Belgrade, Serbia

Abstract: The business sector has a strong direct and indirect impact on children, at the workplace, on the marketplace, in the community, and through the supply chain, where risks arise in terms of endangering children and their rights. This paper aims to provide new evidence of the impact of the business sector on children’s rights in Serbia. The analysis of the impact of the Serbian business sector on children's rights was carried out through cabinet research of three sectors identified as a priority based on their influence on the economy and children's rights – ICT, food and agriculture, and financial sector. After interviews with managers responsible for corporate social responsibility in the leading companies from these sectors, their potential impact on children's rights has been mapped together with sectoral risks, while opportunities for shared-value engagement between businesses and organizations and institutions supporting children and their rights were extracted. Our research suggests that shared value in the context of the promotion of children's rights goes far beyond traditional corporate philanthropy, and audits how core business operations, assets and practices, advocacy initiatives, skills, and know-how can support children's rights in achieving Sustainable Development Goals.

Article info: Received: July 11, 2019 Correction: December 2, 2019 Accepted: December 4, 2019

Keywords: business principles and children’s rights, corporate social responsibility, shared value, Serbia.

INTRODUCTION Human rights are relevant to the economic, social, and environmental aspects of corporate activity (Sinha, 2013). On the other hand, corporations impact human rights in significant ways, especially since economic and political influence has grown over decades (Paine and Srinivasan, 2019). Legal and economic research has examined the protection and promotion of human rights in the context of business operations in growing numbers over the last decade (Office of the High Commission for Human Rights, 2011; Remmert, Koalick and Mahler, 2013; CSR Europe, 2016). *E-mail: krstic.natasa@gmail.com

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The research has focused on Corporate Social Responsibility (CSR) and its effectiveness; international soft-law mechanisms, such as the United Nations Global Compact (UNGC), and the ethical and moral argumentation of profit-driven companies being assigned social obligations.

Although human rights protection is nowadays an integral part of CSR activities (Obara and Peattie, 2019), children’s rights protection is often neglected. CSR has become an essential component of today's business strategy for most companies (Lahtinen, Kuusela and Yrjölä, 2018), while the business sector's impact on children's rights is still relatively unclear, ungoverned and unresearched, especially in Serbia (Krstić, 2018). When assessing the role and the impact of the business sector on children’s rights, academic research is predominantly focused on topics such as child labour and exploitation, gender inequality, and ethics of advertising toward children (Carlson and Clarke, 2014). The research by authors Crane and Kazmi (2010) offers broader guidance in implementing CSR activities for children that go beyond moral and physical protection, and address economic welfare, parents' employment conditions, quality of family life, involvement in initiatives that support education and culture, and engagement into partnership for change.

Children are among the most sensitive and vulnerable societal groups, affected both directly and indirectly by the business sector (Berlan, 2016), at the same time being unable to access the mechanisms to participate in making business decisions, and having difficulties in achieving remuneration if their rights were violated (Gerber, Kyriakakis and O’Byrnet, 2013). There is a fine line between respecting children’s rights, attracting them as potential consumers, and the point of view where investing in a children-related project is a public relations activity (Berlan, 2016). It is expected from the business sector to take responsibility for its potential impact on children’s welfare, instead of developing relationships that are just contributing to brand reputation (Smaiziene, 2015).

Globally, corporate responsibilities extend to ensure that children’s rights, from the right to education and health to the right to be free from child labour and discrimination, are respected at each stage of the company’s value chain (Collins, 2014).

Nowadays, this is governed under two sets of principles – The United Nations (UN) Guiding Principles on Business and Human Rights, which recommends the steps companies should take to respect human, and especially children’s rights, and the Children’s Rights and Business Principles (CRBP), built on the Guiding Principles, and which provide guidance that companies can follow to respect and support children’s rights in the workplace, marketplace, and community (OHCHR, 2011; UNICEF, UNGC and Save the Children, 2013). In addition, the Children’s Rights in Impact Assessments (UNICEF, Danish Institute for Human Rights, 2013) provides useful guidance for companies aiming to integrate children’s rights into their formal risk assessment.

Furthermore, as the concept of CSR has evolved, the impact of the business sector on children’s rights becomes the focus on exploring and defining opportunities and mechanisms on how to create value for involved stakeholders (Filho, Idowu and Louche, 2010). In that respect, shared value (SV) refers to the approach businesses are taking to create social impact while also enhancing their long-term business value, thus being much broader than CSR (Porter and Kramer, 2011). Until recently, corporate engagement in society has been viewed as a business cost to be traded off against profitability. Traditional approaches to corporate engagement, such as financial contribution and corporate philanthropy, represent a missed opportunity. From a social perspective, they create little incremental value beyond the cash amount donated, and the benefits are mainly in terms of reputation. Increasingly, companies are realizing that, by creating SV, they can benefit society and boost their competitiveness at the same time (Porter and Kramer, 2011). Shared value partnerships (SVPs) between businesses and organizations and institutions supporting children’s rights can be found in advocacy initiatives, business practices, core business operations, and assets and with various models of financing (Figure 1).

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In Serbia, children represent close to one-fifth of the total population (UNICEF, 2016) with high risk-of-poverty rate. Those most exposed to the poverty risk in Serbia are individuals up to 18 years of age, with as much as 30.5% affected (Statistical Office of the Republic of Serbia - SORS, 2018). The Republic of Serbia has ratified all key international treaties concerning the protection of children’s rights, including the Convention on the Rights of the Child - CRC (2001) and two optional protocols.

Based on that, the Constitution of the Republic of Serbia guarantees the enjoyment of human rights by children in line with their age and mental maturity, while leaving the detailed regulation of children's rights to laws. The Deputy Ombudsman for Children’s Rights and Gender Equality is tasked with monitoring the application of the CRC with the Office of the Ombudsman of the Republic of Serbia. Over the past decade, many laws affecting children’s rights have been enacted or amended, and a range of strategic documents in this area have been adopted. Moreover, Chapters 23 and 24 of the European Union (EU) Accession Negotiation Positions and their accompanying action plans are directly related to child rights, while several other chapters may benefit from an increased child-rights focus. The National Plan of Action for Children (NPA), a government strategic document that was to operationalise the commitments that transpired from the CRC and Millennium Development Goals (MDGs) was adopted in 2005, expired in 2015 and has not been revised. The Council for the Rights of the Child as the advisory body of the Serbian Government has been active since 2018, which is expected to be proactive on topics and legislative initiatives supporting children and their rights.

The research aimed at understanding the awareness and implementation of CRBP in Serbia conducted by UNICEF (2018e) concluded that the business sector highly values activities toward children, and frequently engages in them by supporting the areas of education, social protection and childcare services, in promoting healthy lifestyles, and improving living conditions in the community. UNICEF research confirmed that many businesses are interested in supporting child rights within their sustainability strategies, which are however often not explicitly incorporated into their reporting principles, internal strategies and policies.

To foster this integration, a sectoral approach towards CRBP should be established.

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Our research aims to assess the potential business impact of top priority sectors, identified based on their influence on the economy and children's rights, perceived risks and opportunities for SV engagement in partnerships with organisations and institutions that support children and their rights. The paper is structured as follows: The first, the introduction section, discusses the theoretical insights on the topics of children’s rights as integral part of the universal human rights, CSR and their intersection. The second part of the paper focuses on presenting research methods. Finally, research results are presented and discussed in the third part of the paper.

METHODOLOGY For the purpose of this article, a combination of empirical and qualitative research was deployed.

In the first phase, several business sectors in Serbia were empirically analysed, which UNICEF globally selected in view of their direct intersection with children’s rights (e.g. impact of business operations, products, services on children) and/or potential to amplify the reach through core assets (UNICEF, 2018a). According to the UNICEF methodology, the following sectors were selected: Information and Communication Technology (ICT), Food and Agriculture, Pharmaceutical and Healthcare Equipment, Tourism, Textile, Retail Trade, Media, Sports and Toys, and Banking and Insurance. In that respect, the term ‘sector’ is used to address a collection of industries under a shared area of economic activity. The sectoral approach was deployed having in mind that the next era of CSR should look out for a period of experimentation and innovation as organisations advance their core business objectives by addressing existing social and environmental issues, which are dependent on market and industry settings (Jose and Venkitachalam, 2019).

Next, the extracted sectors were evaluated based on their overall performance and potential for the Serbian economy. In order to better understand the sectoral approach to the current state of CSR towards children, in-depth interviews were conducted with a representative from one of the leading companies in the sector from the position CSR Manager. Of the ten requests for interviewing, five companies from the sectors of ICT, Food and Agriculture, Retail Trade, Media, and Banking participated in in-depth interviews conducted during March and April 2019.

Based on the conducted sectoral assessment, in the second phase of the research the mapped sectors were split into two tiers, wherein the first tier consists of three priority sectors that need to be engaged in the promotion of children’s rights in Serbia. The priority sectors were finally evaluated based on: - their direct and indirect impact on children’s rights in the four key impact areas of CRBP; the workplace (key ways businesses can promote children’s rights in the workplace), the marketplace (ensuring healthy, positive and appropriate products and marketing for children), the community (managing community impacts of operations, networks, products, and services) and the supply chain (ensuring children’s rights are protected throughout a company’s supply chain); - the sectoral risk posed to children and communities (UNICEF, 2018b); - the opportunities to engage in creating partnerships that have a SV for both children and business (UNICEF, 2018a).

For reciprocity, the data (DOI: 10.17632/yrx3z66pvr.1) used for the study has been uploaded on the Mendeley data repository, which can be retrieved from https://data.mendeley.com/datasets/yrx3z66pvr/1.

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RESULTS AND DISCUSSIONS The results of the empirical research split the profile of the mapped sectors with strong impact on children and their rights into two tiers, as presented in Table 1. The first tier consists of the ICT, Food and Agriculture, and Banking and Insurance sectors, which are the three priority engagement sectors that need to be engaged in the promotion of children’s rights in Serbia. The other tier, Tier 2, includes Pharmaceutical and Healthcare Equipment, Tourism, Textile, Retail Trade, Media, Sports, and Toys sectors, which can be selectively engaged in certain areas that cause children deprivation or bring about opportunities for enhancing children’s rights.

Table 1 - Sector priority in shared value partnerships with organisations and institutions supporting children (source: empirical research by the authors)

Tier 1 1. ICT 2. Food and Agriculture 3. Banking and Insurance

Tier 2 4. Pharmaceutical and Healthcare Equipment 5. Tourism 6. Textile 7. Retail Trade 8. Media 9. Sports and Toys

ICT Sector With a strong engineering education background and outstanding skills, high fluency in English and attractive labour costs, Serbia is aiming to become an alternative to more traditional ICT markets, with the ICT sector1 becoming one of the key pillars of the Serbian economy (Deloitte, 2018). Given its strong results in attracting investors and employment, ICT is one of the healthiest sectors of the Serbian economy, and the priority sector for the Government in the forthcoming period (Kleibrink, et al., 2018). Cumulative ICT sector export revenues account for 19% of the total export of services (1.1 billion euros), where most revenues (89.2%) were realised from the exports of information technology (IT) services, and 9.4% from the export of telecommunications (Telco) services (Chamber of Commerce and Industry of Serbia, 2018). Serbia is also perceived as one of the most promising ICT outsourcing destinations in the world (Andjelković, Šapić and Skočajić, 2019). The IT sector consists of around 2,000 legal entities, it employs over 21,500 people (1.4% of the total workforce), records revenues of 1.8 billion euros and profits of 467 million euros, with an annual growth of 6% - which is steadily increasing (Vojvodina ICT Cluster 2018; CCIS, 2019b). In the structure of the sector, IT hardware is still considered to be the dominant segment (45% share), followed by IT services (37%), while the remaining 18% comes from software which, however, provided the bulk of the profits (60%), and has the strongest export potential (Vojvodina ICT Cluster 2018; CCIS, 2019b). According to European standards, such a market structure indicates that the market is still not quite mature (Vojvodina ICT Cluster, 2018, p. 37).

1 The ICT sector is divided into two sub-sectors: telecommunications (Telco) and information technology (IT). Furthermore, the IT sub-sector comprises three segments: hardware, software, and IT-related services.

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From the perspective of local companies operating in the IT sector, foreign direct investments (FDIs) are seen more as a threat than a benefit due to the limited human resources available on the market.

Namely, foreign companies tend to attract good IT experts by offering attractive salaries, which is something local companies cannot compete with (Vojvodina ICT Cluster, 2018, p. 26). However, the average salary in the IT sector is amongst the highest in the Serbian economy, especially in the field of computer programming, consultancy, and related activities (twice as high as the average) (Vojvodina ICT Cluster, 2018, p. 54).

Based on size, most IT companies are classified as micro companies (with an average number of employees close to six). Only eight IT companies have more than 250 employees, and small and medium enterprises (SMEs) account for 20% of all the IT companies. A huge number of micro companies (1,629) with low financial capacities, insufficient technological, and management skills are also operational on the market (Vojvodina ICT Cluster, 2018, p. 49). The sector is also characterized by many freelancers, and informal commissioning is also common, with both methods of employment outside of official statistics (Kleibrink, et al., 2018). In 2017, a lot of effort and investments were made to build science, technology, and IT parks in Novi Sad, Niš and Indjija; however, private public partnerships (PPPs) are still an unused avenue for building more parks and IT-supporting infrastructures country-wide (Vojvodina ICT Cluster, 2018).

Based on the conducted in-depth interviews, we could conclude that the IT sector offers a familyfriendly workplace, aiming at branding the employees around corporate culture, and increasing their overall level of satisfaction. Some examples include remote and/or work from home, superficial office space with a cafeteria and a bar, interactive teambuilding with family members, a nursery within the business headquarters, private family healthcare, and the possibility of children spending a day at work with their parent. Most of the IT companies are not deeply rooted in the community, as they are either exporters or outsourcers. Lately, there has been an awakening of interest in employee volunteering and support to non-governmental organisations (NGOs) by deploying digital innovation and company expertise.

The more mature Telecom sector is dominated by three mobile operators and one cable operator.

The sector operates through 250 companies, employing nearly 19,000 people (1.1% of the total workforce), with revenues of 2.2 billion euros (driven from the provision of mobile services, 60%) making a 4.5% contribution to the country’s gross domestic product (GDP) (Vojvodina ICT Cluster, 2018; CCIS, 2019b). The value of investments in the Serbian Telecom sector in 2017 was over 250 million euros, which is close to the European average (300 million euros) (Vojvodina ICT Cluster, 2018, p. 43).

The Telco leaders at the Serbian market are very active and innovative in the CSR field, also dynamically supporting the business associations through which they promote and advocate on CSR. The areas of support include education on internet safety, raising awareness of digital violence, curbing the digital gender gap and the digital divide, enhancing digital competencies of children with disabilities, early childhood development programs, youth competitions in the field of core business (mobile applications), student internships, supporting work-life balance of young mothers, and donations in-kind to schools.

The assessment of the potential sectoral impact on children’s rights in Serbia, perceived risks and opportunities for SVP with organisations, and institutions that support children and their rights are presented in Table 2:

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Table 2 – The potential impact of the ICT sector on children and opportunities for SVP (source: authors, based on Child Rights and the ICT Sector, UNICEF, 2018c)

Potential direct and indirect impact

Marketplace (major impact area) Ensure products and services promote and protect child rights in use: Addressing violence, abuse and exploitation online, protecting the privacy of young users’ personal data, and preserving their right to freedom of expression online.

Community Reinforcing government and community child rights initiatives: Gender equality (uneven representation of women employed in the ICT industry and enrolled at IT faculties); economic digital divide (vulnerable children and poor families); territorial digital divide (discrepancy in internet connection and presence of computers between Belgrade and Central Serbia). Protect children affected by emergencies; e.g., by establishing the internet and other means of electronic communication.

Supply Chain

Perceived risks

Opportunities for SVP

Ensure workplace rights are upheld by suppliers and subcontractors.

Children may be exposed to online exploitation or abuse if ICT companies fail to create a safe and age-appropriate online environment.

Online safety for children has oriented around three primary areas: - inappropriate content (disturbing or potentially harmful content); e.g., images and descriptions associated with war and other atrocities, domestic abuse and violence, cruelty to animals, and harmful material that may promote racial and religious hatred, homophobia, or misogyny. Children may also be exposed to content that discusses suicide, self-harm, and eating disorders. Inappropriate content may also include a reference to child sexual abuse images and videos.

- inappropriate contact (online grooming) - inappropriate conduct (cyberbullying), e.g., spreading rumours; posting false information or nasty messages, embarrassing comments or photos; or excluding someone from online networks or other forms of online communications.

Missed opportunity to design child safety features / protection of child rights into products.

Compromising young users’ personal data.

Electronic waste leading to hazards in collection and processing.

Advocacy Awareness raising: Online violence against children/Safe use of the internet Influencing policy sector and government agenda: Addressing the issue of the Digital divide; fostering enrolment at STEM faculties with increased quotas.

Agenda setting through platforms (Digital Serbia, cluster ICT initiatives): advancement of digital literacy; Gender equality/Women in Tech.

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Business Practices Child digital footprint: Protecting online privacy and reputation of children from internet browsing data, use of biometric data, and online cyberbullying. Children are more susceptible to advertising and marketing techniques, and their preferences and behaviours are more open to influence and manipulation. ICT companies may undermine children’s privacy rights if they sell or disclose their personal information and browsing data to advertisers and third parties, which can be further manipulated to inform behavioural targeting and advertising to children. Thus, if ICT companies allow for online photos to be tagged and shared without the authorisation of the child, that child’s privacy is affected, as personal information is shared without authorisation.

Policies: Integrating child rights’ considerations into all appropriate corporate policies and management processes, e.g., conducting privacy impact assessments, collecting minimal information on children, and applying enhanced security measures to protect any personal data that is collected, providing and communicating child-friendly terms and conditions, acquiring explicit consent, making children’s online profiles private by default, offering simplified reporting and complaints mechanisms, respecting anonymity online. Developing standard processes to handle child sexual abuse and online violence material.

Financing Strategic grants: Curbing online violence (educating children, parents, and teachers about children’s safety and responsible use of ICT), cooperation with STEM universities (reducing the digital gender gap through scholarships and internships for girls); e-classrooms/ schools for enhanced digital literacy.

Engaging employees: fostering IT literacy.

Contribution in kind: digital hardware and software solutions aiming to decrease the digital divide and supporting vulnerable children and families.

Core business and assets Innovation in products and services: Encryption and anonymization technology (e.g., age verification, parental control, service providerdeployed blocking and filtering), creating tailor-made IT solutions to NGOs supporting children.

Expertise: Creating a safe and age-appropriate online environment

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Food and Agriculture Agriculture in Serbia has strong growth potential, and is the main engine behind the development of rural areas (Ministry of Agriculture - MoA, 2012). Serbia is blessed with very favourable natural conditions (land and climate) for agricultural production since it has one of the cleanest soils in Europe with over 6 million hectares of agricultural land, 60% of which is arable, and around 90% is in private ownership (Development Agency of Serbia - RAS, 2017, p. 3). As for the agricultural production structure, crop field production (mostly cereal crop production: wheat, barley, rye, oats, corn, millet and sorghum) makes up two-thirds of the structure, while livestock production makes up one-third (MoA, 2012, p. 2). Serbia also ranks as one of the world’s largest producers of plums and raspberries.

Other important agricultural products are sunflower, sugar beet, barley, soybean, potato, apple, pork and chicken meat, beef, poultry, and dairy (MoA, 2012, p. 2). As an important indicator of Serbia’s efforts to produce good-quality and healthy food, Serbian law prohibits the production and import of any genetically modified foods and seeds (GMO) (Deloitte, 2018). Serbia also plans to allocate 20% of its agricultural land to be converted for organic production over the next ten years (RAS, 2017).

In 2017, the agriculture sector2 accounted for 6% of the Serbian GDP, attracted 11.1% of the country FDIs, with a recorded 2.7 billion euros of exports, thus becoming one of the few industry sectors to record a trade surplus (World Bank, 2018). Serbia is the biggest exporter of food among the Central European Free Trade Agreement (CEFTA) countries, and the only net exporter. The most important export products are fresh and frozen fruits (raspberries, apples, plums, cherries), maize, wheat, beet or cane sugar, vegetables, non-alcoholic beverages and beer made from malt (World Bank, 2018). Employment in agriculture in Serbia (as a percentage of total employment) was reported at 19% in 2017 (World Bank, 2018), whilst the average net salaries in the sector are about 15% lower than the Serbian average, with the informal sector making up a large part of employment (MoA, 2017). Although Serbia is among the top ten European net exporters of agricultural and food products, much of its potential remains untapped. The challenges are that the average yields per hectare are lower than the EU average, the assortment of products is relatively low-value, there is low productivity due to outdated technologies and infrastructure, as well as small economic size and low utilisation of agricultural land per farm (European Commission, 2018, p. 13). Significant challenge comes also from internal migration trends; because of the lower standard of living in rural areas, young people are increasingly leaving the countryside for big cities (European Commission, 2018, p. 13).

From the interview with one of the leading companies from the sector, we could conclude that the sectoral CSR activities are based on target groups (e.g., the youth for soft drinks, and children for dairy producers), as they often make up part of the parent group’s global projects, or are the result of a domestic owner’s philanthropic beliefs. Some of the projects focus on encouraging small farmers to remain on their farms and improve their sustainability through the supply chain. Additionally, a decrease in the operations’ environmental impact, educating children on food labelling, donations in-kind, and youth mentorship and internship projects are also in the focus of CSR activities.

The assessment of the potential impact of the food and agriculture sector on children’s rights in Serbia, perceived risks and opportunities for SVP with organisations and institutions that support children and their rights is presented in Table 3: 2 Under the Food and Agriculture sector, the agricultural production, hunting, and accompanying service activities and production of food products were analysed.

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Table 3 – The potential impact of the food and agriculture sector on children and opportunities for SVP (source: authors, based on UNICEF Children's Rights and Business Atlas)

Potential direct and indirect impact

Workplace Inadequate employment protection for parents and caregivers.

Respecting the rights of young workers and encouraging their education.

Children may be pushed into child labour as a result of poverty.

Marketplace (major impact area) High-sugar and calorie-dense food.

The negative contribution of child-directed marketing and advertising of food with a high content of fat, sugar and salt on overweight and obesity rates in children.

Community Exposing children to harmful chemicals or contaminated local water supplies.

Environmental degradation may disrupt agricultural production and undermine the livelihoods of local communities.

Supply chain

Perceived risks

Opportunities for SVP

Ensure workplace rights are upheld by suppliers and subcontractors.

Ensure suppliers’ products are safe and meet international health and safety standards.

Young workers are increasingly leaving the countryside for big cities.

Although most companies have Codes of Conduct when it comes to child labour, the risk of child labour in the agriculture supply chains still exists, because of root causes, including poverty and limited access to education. Lack of knowledge by suppliers on how to identify / mitigate child rights impacts/risks.

Working conditions: decent wages, maternity/paternity protection, big part of the employment comes from the informal sector.

Lack of adequate nutritional information, limiting children, parents and caregivers from making informed or healthy food choices.

Advertising that promotes an unhealthy diet could increase the risk of overweightness, and obesity among children.

Unregulated agricultural practices, such as cutting down forests, hay burning, partitioning and polluting rivers can have serious impacts on local communities.

Advocacy Awareness raising on healthy food/nutrition and the importance of sports in children.

Agenda setting through platforms (International Advertising Association, IAA, International Advertising Bureau, IAB): Responsible marketing, advertising, labelling.

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Business practice Policies: Build children’s rights into selection, review, and training of suppliers; Code of responsible advertising, International Code of Marketing of Breast-milk Substitutes; Review of current labelling procedures for food and beverage targeting children and adolescents.

Financing Strategic grants: Programs promoting healthy lifestyles; Agricultural education, e.g., introduction of new biotechnologies and modern ways of farming in high-school and university curriculums, support to agricultural and veterinary schools, faculties and institutes, granting scholarships to young farmers.

Engaging customers: donating money in cooperation with customers for child-related social goals.

Contributions in-kind (food): vulnerable and children affected by emergencies.

Core business and assets Innovation in products: enhanced ecological principles in soil usage to decrease the community environmental impact in agri-business.

Expertise: sharing global/regional know-how with the supply chain to enhance their capacities.

Banking and Insurance The Serbian banking sector consists of 29 banks, dominated by foreign-owned banks (71% of the market share) mainly from Italy, Austria, France and Greece. The network is comprised of 1,633 business units and employs over 23,000 persons with a decreasing trend, mainly due to digitalization (National Bank of Serbia, 2018, p. 3-4) and with earnings significantly higher than the average (CCIS, 2019a).

In terms of net profits, 24 banks operate with a surplus, whilst five banks (with a market share of 1%) posted a negative financial result (National Bank of Serbia, 2018, p. 6). The sector is characterised by an acceptable level of competition and low concentration of activities, with the highest values in deposits (chiefly household deposits) and income from fees and commissions, and the lowest figures for total gross loans to corporates (National Bank of Serbia, 2018, p. 4). Thus, the sector is highly liquid and well-capitalised (European Commission, 2018, p. 6), but also highly euroized, even though during the last two years the overall deposit and loan dinarisation increased (Deloitte, 2018). Some holders of mortgage loans indexed in Swiss Francs have had their homes seized because they were unable to meet payments when the exchange rate changed, and the Government has engaged itself in finding a sustainable solution for them. Some progress has been seen in implementing the Non-performing loans (NPL) Resolution Strategy of the Serbian Government (European Commission, 2018, p. 19), as it contributed to lowering the gross NPL, although the major part of NPLs is still in the corporate segment making banks resilient to lend (NBS, 2018, p. 17). The top five banks (in terms of balance-sheet assets, gross loans and deposits) account for more than half of Serbia's banking sector (NBS, 2018, p. 3).

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The insurance market in Serbia shows a moderate level of concentration, comprising 21 insurance undertakings (four life insurers, seven non-life insurers and six both life and non-life insurance), 16 of which are in foreign ownership, whilst employment in the sector accounts for over 10,000 persons, with decreasing trends (NBS, 2018, p. 4). In terms of premium, the share of non-life insurance makes up two-thirds of the market (78%) and life insurance one fifth (22%) (NBS, 2018, p. 4).

From the interview with the leading sectoral market player, it was concluded that of all the sectors, banks were among the first entering the field of CSR, and are very active in business associations and platforms through which corporate responsibility is advocated. In the past, banks mostly supported largescale programs targeting children and the community; e.g., the refurbishment of parks, playgrounds, affinity payment cards supporting a cause, helping vulnerable and children with disabilities, culture, sports, environmental protection and donations to children hospitals. Recently, a shift has been made towards sustainability reporting, measuring the socio-economic impacts of operations, and employee engagement (volunteerism, mentoring). Due to strong digitalisation trends, some banks have embarked on digital innovations, supporting youth and student hackathons, and establishing partnerships with ICT and creative hubs. Improving the financial literacy of children and youth by teaching them how to save money is also in the spotlight, where banks either launch their own program, or collaborate with the Association of Serbian Banks (ASB). Finally, insurance companies are active in promoting healthy lifestyles in children through sports, or are occasional philanthropists for children’s institutions and organisations.

The assessment of the potential impact of the banking and insurance sector on children’s rights in Serbia, as well as the perceived risks and opportunities for SVP with organisations and institutions that support children and their rights is presented in Table 4.

Table 4 – The potential impact of the banking and finance sector on children and opportunities to engage in SVP (source: authors, based on UNICEF and CYFI, 2013)

Potential direct and indirect impact

Workplace Respecting the rights of young workers with decent wages and enabling them to continue their education.

Marketplace (major impact area) Availability and accessibility of financial services to certain categories of young people (e.g., freelancers, start up’s, youth entrepreneurs, students).

Technology is driving a shift in payments from cash to electronic forms, and young people without access can be socially and financially excluded.

Youth may be forced to delay life-cycle milestones (moving out of the parental home, getting a first apartment) due to unfavourable economic conditions, which requires financial institutions to adapt their lending criteria, delinquency aging, and collections practices.

Indirect impacts on children and families in investment decisions through indebtedness and financial exclusion.

Community Financial literacy of children and youth.

Financing of PPP projects in the segment of healthcare, education, and social services.

Supply chain Respecting children's rights in outsourced security arrangements at branches.

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Perceived risks

Security risks for children from phishing attempts, fraudulent email, cyber-attacks and identity thefts.

Indebtedness and financial exclusion of youth and families.

Responsible marketing and advertising, e.g., content that promotes positive financial behaviour; not hiring children to promote adult services (e.g., cash, mortgage loans, travel insurance), or using cartoons as ‘brand ambassadors.

Targeting young people for credit cards with a consequential rise in over-indebtedness.

Opportunities for SVP

Advocacy Awareness raising on risks of sharing personal details online.

Agenda setting through platforms (ASB): Access to finance for youth entrepreneurs and digital freelance workers without creditworthiness.

Business practice Policy commitment to respect and support children’s rights: Procedures for safeguarding child rights in security arrangements; Risk assessment on children’s rights in financing; Assessing the impacts of child-friendly products and services (in product design, access and delivery options, child- and youth-friendly communication strategies, the inclusion of financial education components).

Financing Strategic grants: Support youth through projects aimed towards skills-building, promotion of youth entrepreneurship.

Engaging customers: Co-branded payment cards for child-related causes.

Engaging employees: Volunteering, mentorship to schools and faculties (building financial capacity, literacy, and entrepreneurship culture).

Contribution in-kind: Direct debit for child-related donations and current accounts lifted from fees Core business & assets Innovative services: Access to finance for youth entrepreneurs and digital freelance workers; Building a child rights approach into design options for services (deposit options, mobile and internet banking access, control, and security features); University affinity card programmes.

Expertise: Strengthening youth entrepreneurship and financial literacy (building financial capability and literacy, promoting financial education and a positive financial culture).

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CONCLUSION The paper makes several contributions to CSR management studies. First, it provides theoretical and empirical insights into the business sector’s impact on children’s rights in Serbia, which is still a relatively unclear, ungoverned, and underresearched topic. The results suggest that children as a group of stakeholders need special consideration. They represent around one fifth of the total population in Serbia, and are among the most sensitive and vulnerable societal groups, impacted both directly and indirectly by the business sector, at the workplace, in the marketplace, in the community, and through the supply chain, where risks arise in terms of endangering children's rights.

Second, the research confirms theoretical results which suggest that no national strategy or processes of certification on CSR activities cover the area of children’s rights based on internationally acclaimed CRBP. Research conducted in this field clearly concludes that the business sector highly values activities toward children and frequently engages in these kinds of activities, yet children’s rights remain unregulated and unmeasured in business strategies, policies, and politics (UNICEF, 2018).

Third, the conducted research expands existing academic research on creating SV for businesses by extending the concept of CSR, as long-term competitiveness and sustainability of companies depend on social conditions where children's rights are of great importance. Accordingly, the impact of the business sector on children and their rights should be diverted from one-off donations and philanthropy to the exploration of opportunities and mechanisms for creating SV’s with organizations and institutions that support children's rights in society. In this process, it is necessary to begin identifying the areas of influence and related risks, which should create inspiration for areas of action in which it is possible to create shared values.

Results suggest that the major impact area for all three examined business sectors - ICT, food and agriculture, and the financial sector - is the marketplace. It ensures that products and services are safe for children and seeks to support children’s rights through them, which it does through using marketing and advertising that respect and supports children and their rights. The mapped key impact area creates related risks, which are also associated with the use and availability of products and services, their responsible promotion, and the environmental risks posed to the local community. The opportunities for SVP for all three sectors can be found in advocacy initiatives on raising awareness on the perceived risks that children could be exposed to using companies’ products and services, and agenda setting through influential sectoral platforms. Furthermore, child rights consideration should be integrated into all appropriate corporate procedures and committed by management processes. Financing should be covered from a broader angle, from strategic grants and contributions in-kind to employee engagement in terms of their time and expertise, and customers around the cause of the support and promotion of children’s rights in Serbia. Finally, a clear untapped potential can be found in innovative products and services and the expertise of the leading companies in the sector, which would make their offer more inclusive and impact the overall wellbeing of children, thereby creating a business contribution to the Sustainable Development Goals.

With this paper, the authors hope to inspire further research focused on initiatives the business sector participants could implement based on mapped opportunities in terms of SV creation, and quantifying this value for children in society. The development of company studies and sectoral cases to illustrate the benefits to children as important stakeholders in society, and to look at the process of creating shared value would provide significant stimulus for the business community to follow good examples using an experienced and tested approach recognised at the sectoral level.

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ACKNOWLEDGEMENTS: The research for this article was conducted within the project "Situation Analysis on Children in Serbia" for the United Nations Children's Fund during the period of March-April, 2019.

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United Nations Children Fund, The Global Compact and Save the Children. (2012). Children’s Rights and Business Principles. Retrieved March 17, 2019, from http://childrenandbusiness.org/.

Vojvodina ICT Cluster. (2018). ICT in Serbia: At a Glance 2018. Novi Sad: Vojvodina ICT Cluster. Retrieved April 5, 2019, from https://vojvodinaictcluster.org/wp-content/uploads/2018/05/ICT-in-Serbia-%E2%80%93-At-a-Glance-2018.pdf.

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UTICAJ POSLOVNOG SEKTORA NA PRAVA DECE U SRBIJI

Rezime: Poslovni sektor vrši jak direktan i indirektan uticaj na decu, na radnom mestu, tržištu, u zajednici i preko lanca snabdevanja, gde se pojavljuju rizici u smislu ugrožavanja dece i njihovih prava. Ovaj članak ima za cilj da pruži nove dokaze o uticaju poslovnog sektora na dečja prava u Srbiji. Analiza uticaja poslovnog sektora u Srbiji na prava dece sprovedena je putem kabinetskog istraživanja tri sektora koja su identifikovana kao prioritetna u pogledu njihovog uticaja na ekonomiju i prava dece – IKT, hrana i poljoprivreda, i finansijski sektor. Nakon intervjua sa rukovodiocima za društveno odgovorno poslovanje u vodećim preduzećima iz ovih sektora, mapiran je njihov potencijalni uticaj na prava dece zajedno sa sektorskim rizicima, i prikazane su prilike za angažovanjem u stvaranju zajedničkih vrednosti između preduzeća i organizacija i institucija koje podržavaju decu i njihova prava. Naše istraživanje sugeriše da zajednička vrednost u kontekstu promocije prava dece prevazilazi tradicionalnu korporativnu filantropiju i ukazuje kako osnovna poslovna delatnost, imovina i prakse, inicijative zagovaranja, veštine i stručnost mogu podržati prava dece radi postizanja Ciljeva održivog razvoja.

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Ključne reči: principi poslovanja i prava dece, društveno odgovorno poslovanje, zajednička vrednost, Srbija.

EJAE 2020, 17(1): 67 - 79 ISSN 2406-2588 UDK: 338.45:663.4(73) 339.13.012.432 DOI: 10.5937/EJAE17-22500 Original paper/Originalni naučni rad

IS THERE MARKET POWER IN THE U.S. BREWING INDUSTRY? Sanjib Bhuyan Department of Agricultural, Food and Resource Economics, Rutgers University - New Jersey, New Brunswick, USA

Abstract: Increased consolidation in the U.S. brewing industry has raised legitimate concern about brewing firms’ ability to exert market power over the downstream. Using structural models of oligopoly behavior, this research estimates the market power in the U.S. beer (manufacturing) industry over a 30-year period, during which the sector experienced a rapid increase in concentration and the demise of many small firms.

The results show that the beer market is price-sensitive, and that both distilled spirits and carbonated soft drinks were substitutes for beer.

While we were unable to detect the impact of the labor and material costs for the price of beer, we found that the federal tax on beer increased beer prices. Our results indicate that while there was some indication of market power, U.S. brewers did not exert oligopoly power over downstream firms (distributors and retailers).

Article info: Received: July 12, 2019 Correction: September 19, 2019 Accepted: September 27, 2019

Keywords: U.S. brewing industry, market power, NEIO. JEL classification: L13, D43

INTRODUCTION The United States is one of the largest producers of beer in the world, and in 2016 it ranked second amongst the countries worldwide in beer production with an amount of about 221 million hectoliters (Conway, 2018). The U.S. ranked 11th in worldwide beer consumption in 2018 with per capita consumption at 74.66 liters/year (Statistica, 2019).1 According to the Beverage Information Group (2018), light beer2 is the most commonly purchased style of beer in the U.S., accounting for 43.5 percent of total sales in 2017. Beer continues to dominate the alcoholic beverage market in the U.S. despite losing its share of the market for alcoholic beverages over last couple of decades, mostly to spirits and, to a lesser extent, to wine. The market share of beer in the alcoholic beverage market in the United States 1 The Czech Republic topped the list with an annual per capita beer consumption of 137.3 lt.

2 Beer with reduced alcohol content or reduced calorie content is called ‘light beer.’

*E-mail: bhuyan@sebs.rutgers.edu

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fell from 55.5% in 2000 to 45.5% in 2018 (DISCUS, 2019). Conway (2018) reported that the U.S. beer industry sold $34.17 billion worth of beer in 2016, mostly through convenience stores and grocery stores. The economic downturn that began in 2007 led U.S. consumers to purchasing cheap beer; nonetheless, the popularity of premium and craft beers continued to rise, and is expected to benefit the industry (IBISWorld, 2012).

Beer is bulky because it is made up of mostly water and, therefore, to reduce the costs of production and transportation, efforts to achieve economies of scale is a commonly pursued strategy in the industry.

As a result, the consolidation of breweries in the United States that started in the 1970s with the purchase of Miller Brewing by Phillip Morris continues today. Although there were reportedly over 5,600 breweries in the United States in 2017, over 95% of them were producing fewer than 15,000 barrels (1 barrel = 31 US gallons) a year, i.e., these breweries were very small (NBWA, 2019). The Boston Beer Company, producer of Samuel Adams brand, and the largest craft beer brewing company in the U.S., supplies only about 2.3% of the beer market (IBISWorld, 2012). As of 2018, the top four brewers, namely Anheuser-Busch InBev (AB InBev), MillerCoors, Constellation, and Heineken USA control almost 78% of the market (NBWA, 2019). Not surprisingly, the leading beer brands in the United States are owned by these top firms (Figure 1).

It is clear that despite the recent rapid growth of domestic craft beer and foreign imports, the U.S. brewing sector continues to be dominated by macro breweries, such as AB InBev. Figure 1 - Market share of the leading domestic beer brands in the United States in 2017

0.0%

2.0%

4.0%

Market share

6.0%

8.0%

10.0%

Bud Light

Budweiser

20.0%

6.5% 5.7%

Natural Light

3%

Busch Light

2.9%

Miller High Life

1.7%

Busch

Pabst Blue Ribbon

18.0%

8.1%

Michelob Ultra Light

Keystone Light

16.0%

9.6%

Miller Lite

Yuengling Traditional Lager

14.0%

18.4%

Coors Light

Blue Moon Belgian White Ale

12.0%

1.8% 1.5% 1.5% 1.4% 1.2%

Coors

1.1%

Bud Light Lime

1.1%

Note: United States; 52 weeks ended January 22, 2017 Source: Grocery Headquarters, 2019

After the 2008 megamergers of Anheuser-Busch with Belgium’s InBev, and Coors with SABMiller, only two major players remained in the U.S. brewing market: AB InBev and MillerCoors, respectively, controlling 42.1% and 33.2% of the U.S. brewing market (IBISWorld, 2012). Bhuyan and McCafferty (2013) argue that, although some researchers (e.g., Elzinga and Swisher, 2011) contest the argument that increased consolidations in the U.S. brewing industry led to a more concentrated market structure and raised the possibility of market power in the industry, the increasing concentration in the U.S brewing industry raised public policy concerns, and such concerns were at the heart of a decision by the U.S. 68

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Department of Justice (DOJ) to block the proposed merger between AB InBev and Mexico’s Groupo Modelo. Commenting on this particular action by the DOJ, the Wall Street Journal (WSJ, 2013) reported that the DOJ was concerned about the AB InBev potentially controlling over 46% of the market share and potentially raising prices in the post-merger period.

Given the structural changes that occurred in the U.S. beer industry over the last few decades that led to its current highly concentrated nature, one of the questions that surfaces is the following: is there market power in the U.S. brewing industry? We are not proposing to examine the relationship between market concentration and industry profitability here, but to quantitatively test for market power in the U.S. beer industry using the well-established New Empirical Industrial Organization (NEIO) approach.3 The NEIO approach uses oligopoly theory to develop market models that allow explicit testing of a hypothesis regarding market power, and we hypothesize that there is no market power in the U.S. brewing industry. For this study, we use the 1977-2006 period during which the U.S. brewing industry experienced a rapid increase in concentration, as well as a severe price war in the early to mid-1980's, leading to the demise of many small firms. This research contributes to the economics literature that focuses on the U.S. brewing industry.

PREVIOUS RESEARCH IN BRIEF As Bhuyan and McCafferty (2013) argued, increasing concentration in the U.S brewing industry has raised public policy concerns concerning the potential impact of industry consolidation on market power. They also argue that industry concentration is of importance in industry analysis because a change in concentration can have a significant effect on the behavior of firms and the economic performance of the market. As mentioned earlier, it was perhaps not surprising that the DOJ blocked the proposed merger between AB InBev and Mexico’s Grupo Modelo.

In an earlier study, Tremblay and Tremblay (1995) examined competition in the U.S. brewing industry, and rejected the hypothesis that firms were price takers over the 1950-1988 period. However, they argue that the conjectural variation estimate was close to a Bertrand oligopoly, and that the level of market power was modest. They also rejected the hypothesis that large firms and high concentration resulted in market power. They found no significance in increasing concentration resulted in an increase in the price of beer. They also found that growth in one period resulted in a negative effect on price.

This follows Demsetz’s (1973) argument that superior firms may be more efficient and able to charge lower prices. Tremblay and Tremblay’s (1995) most significant finding is the effect of advertising on its output price; they found that advertising had a significant impact on increasing rivals’ prices.

In a more recent study that uses a NEIO model to estimate demand and supply functions simultaneously, Denney et al. (2002) found that there is negligible market power in the U.S. beer industry.

Gallet and Euzent (2002) looked at how the business cycle affected competition in the brewing industry.

They found that competition was higher during periods of high demand and expectations about future profits were low.

Iwasaki, Seldon, and Tremblay (2008) used the SCP (structure conduct performance) approach to estimate a system of equations to explain industry concentration, advertising intensity, and profitability in the U.S. brewing industry. Their findings reveal that performance, as measured by the PCM, increases with concentration. They also found that concentration increased with advertising and scale efficiency.

3 Since its inception in the early 1980s, some variation of the NEIO approach has been used by researchers to measure and test for industry market power. Unlike the original model proposed by Appelbaum (1982), such variations may include a priori assumptions about non-competitive behavior (P>MC), such as in the recently proposed Stochastic Frontier (SF) approach by Kumbhakar, Baardsen, and Lien (2012). In this research, there is no a priori assumption about non-competitive behavior in the U.S. brewing industry.

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Using a brand-level data set, Rojas (2008) evaluated different pricing models in the U.S. beer industry.

This study was designed to reflect and test forms of market leadership commonly reported for this industry.

According to Rojas, the Stackelberg leadership was a somewhat better predictor of firm behavior.

Rojas’ findings are similar to earlier findings of Nevo (2001), who focused on the RTE (ready to eat) cereal market, and Slade (2004), who focused on the U.K. brewing sector; both researchers reject the notion of full collusion in their respective research. Using a nonparametric approach, Pipoblabanan (2008) rejected the hypothesis that firm behavior in the U.S. brewing industry reflects cartel behavior.

However, one of the interesting findings of this study was that there was a positive correlation between concentration and the degree of market power.

Gokhale and Tremblay (2011) followed up earlier work by Tremblay and Tremblay (2005), and examined the changes in market power from 1987 to 2009 using a traditional NEIO approach. They also looked at the relationship between concentration and market power. Their results show that, during that time period, there was an increase in competition and a fall in market power in the U.S. brewing industry. They attribute this to a price war fought between U.S. brewing firms in the 1970s and 1980s to gain market share by keeping prices low.

The research presented here complements this strand of literature that focuses on market power in the U.S. brewing industry, and thus contributes to the relevant literature.

DATA AND METHODS Secondary Data This study covers the period of 1977-2006, which witnessed a rapid increase in consolidation, ugly beer price wars, and the demise of many small breweries in the United States. As per the industry standard, the price of beer (wholesale price) is measured in 31-gallon barrels (1 gal=3.785 liters) and was normalized (deflated) by the beer price index. The beer price index was obtained from various issues of the ‘PPI Detailed Report,’ which is published by the U.S. Department of Labor. In terms of the input prices, the payroll price and price of materials were deflated by the employment cost index and agricultural grain price index, respectively.4 Disposable income was deflated with the Consumer Price Index (CPI, courtesy the Bureau of Labor Statistics, 2012) and the federal beer tax (courtesy U.S. Brewers Association, 2011) was deflated using the Producer Price Index (PPI, courtesy the Bureau of Labor Statistics, 2012).5 Table 1 presents data descriptions and sources, and Table 2 presents the descriptive statistics. The price data used in the analysis is normalized to 1984 dollars.

Conceptual Model 6 We follow Bresnahan (1989) to develop a standard NEIO model to estimate market power in the U.S. brewing industry where brewing firms sell beer to a retailing sector for sale to final consumers. We hypothesize that brewing firms exercise market power (oligopoly power) over the retail sector which is assumed to be price-takers.

4 The agricultural grain price index is used because the main materials in the production of beer are malt, corn, and rice (U.S Brewers Association, 2011).

5 The PPI for the brewing industry was used because brewers bear the cost of the per barrel tax.

6 For an extensive and more informative presentation on NEIO, see Bresnahan, 1989.

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We specify a profit maximizing firm i, maximizing its profit, πi , with respect to its own quantity, qi , at a cost Ci(w,qi) , where cost for the firm i is a function of inputs (w) and the quantity it produces. The and its own supply, qi . price that firm i faces is P which is a function of industry supply Q Then firm i's objective function is given by:

(1) Profit maximizing yields:

(2) (3) (4) One should note that the above model is a static one-shot game and provides only one-shot game solutions. It is unable to provide information on situations involving repeated games or dynamic firm behavior.7 We, therefore, use the term as a behavioral parameter describing market conduct, rather than as an indicator of expectations by rival firms. This parameter is commonly referred to as the “conjectural variation” parameter. As common in similar studies, the behavioral parameter is able to describe whether market power is present (or absent), and is represented by θi in Eq. 4.

As presented initially in Applebaum (1982) and later followed by researchers using industry-level data, in the absence of firm level data, it is possible to aggregate Eq. 4 across firms and find a market power measure that characterizes the industry market power at the aggregate level (Bhuyan, 2013).

Bhuyan (2014) argues that this is a standard assumption in this type of model stemming from the scarcity of firm-level data and the need to allow for consistent aggregation over firms. However, Bresnahan (1989) has argued that the marginal costs of firms are likely to vary in equilibrium when market power exists. The suggested alternative, as presented in Bhuyan (2014), is to interpret the aggregate conjectural variation estimated at the industry level as average industry conduct, and the market power parameter as the average industry mark-up, as in Cowling and Waterson (1976). Therefore, if one takes MC to be the average marginal cost of the industry and θ to be the average conjectural variation (or average conduct parameter), then the industrywide counterpart to Eq. 4 will become the following estimable : equation, where the measure of market power parameter in the industry is:

(5) The relationship between the market power parameter (ω) and the conjectural variation parameter (θ) is given by , and it shows how differing levels of the conjectural variation parameter can affect market power and consequently price (Bhuyan, 2014). In a competitive behavior setting, θ=-1 7 There have been attempts to allow the conduct parameter (here θ) vary over time (e.g.,θ = f (time)) to capture the dynamic nature of an industry, e.g., Dixon and Somma (2003). However, it has been found that on average, the estimated “dynamic” θ is equivalent to an estimated static θ.

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and this implies ω=0. On the other hand, a Cournot behavior implies that θ=0 and, whereas where N is the number of firms in the market. a cartel behavior implies that θ=1-N and, Also note that if and if then this means .

The estimation of the structural equation, Eq. 5, requires two equations to be solved simultaneously; one of these equations is a demand function for the beer industry and the other is the cost function for the beer industry (see Bhuyan, 2014, for a similar treatment). Once these equations are established, the market power parameter can be estimated by solving both equations simultaneously. Note from an earlier discussion that our hypothesis is ω=0 , that is, there is no market power in the U.S. brewing industry; the alternate hypothesis is that the re is market power , and the estimated market power parameter is positive and significant.

Model Estimation This section describes the construction of a simultaneous equation model to estimate the proposed model of testing for market power in the U.S. brewing industry. This model is based on the theoretically derived behavioral or conjectural variation model represented by Eq. 5. In order to estimate the market power variable, ω , (in Eq.5), we need both the market demand function and the industry supply relations. To obtain the total cost function (needed to determine the supply function), we follow Denney et al. (2002) and Pipoblabanan (2008), and use a Generalized Leontief functional cost function.8 We as define wtL as the price of payroll, as the price of materials, Kt as the quantity of capital, and the federal tax charged in the production of a barrel of beer. Then the total cost function of the U.S. brewing industry is given by:

(6) To estimate Eq. 5, we also need a demand function representing the demand for beer in the United States. As found in similar studies, a semi-log demand functional form is assumed here to represent beer demand in the U.S. where Pt is the price of beer, Pricecola is a price index for carbonated drinks, Pricespirits is a price index for spirits, Inc is disposable income, and εt is an error term. Then the demand for the U.S. brewing industry is presented as a semi-log function:

(7) Estimation of Eq. 5 also requires marginal cost function and following earlier studies, we use Eq. 6 as a proxy. The demand function in Eq. 7 is used to represent the “Q ” in Eq. 5. We then substitute Eq. 6 and Eq. 7 onto the right-hand side of Eq. 5 to obtain Eq. 8, or the empirical model we need to estimate to determine existence of market power in the U.S. brewing industry:

(8) To address the endogeneity issue in Eq. 8 (because quantity and price are contained in both Eq. 7 and Eq. 8), we simultaneously estimate Eq. 7 and Eq. 8 to determine the market power parameter ω .

8 The generalized Leontief function has primarily been used in empirical studies for cost functions in an industry with a single output. Here we consider the main product of the U.S. brewing industry as a single output, beer. Gallet and Euzet (2002) and Xia and Buccola (2003) also find the Generalized Lenotief Function is an appropriate form in estimating the cost structure of the U.S. brewing industry.

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We used PROC MODEL in SAS version 9.4. Information on the variables used, data set, and the SAS program codes used to estimate equations (7) and (8) are provided in the appendix.

Discussion of Results Estimated coefficients of the simultaneous equations are reported in Table 3 along with their respective t-statistics and a measure of the overall model performance. Starting with the demand equation (Eq. 7), the sign on the price of beer is negative, as expected, (suggesting a downward slopping demand curve) and statistically significant.

Although the estimated parameter is not equal to own price elasticity of beer (due to the semi-log form of the demand function), the estimated beer price coefficient indicates that the own price elasticity of beer demand in the U.S. is negative; this result shows the price sensitivity of beer consumers in the country.

We know from an earlier discussion that beer has been losing its market share in the U.S. to spirits.

Our results empirically confirm that beer competed with spirits in the U.S., as evident from the positive and significant coefficient for price of spirits variable, indicating the substitutability of spirits and beer. That is, as the price of spirits (beer) goes up, the quantity demanded for beer (spirits) goes up too.

According to the Washington-based Distilled Spirits Council, the market share of distilled spirits in the U.S. alcoholic beverage market grew from 28.7% in 2000 to 37.3% in 2018, increasing its share mostly at the cost of beer (DISCUS, 2019). Beer also competes with non-alcoholic carbonated drinks in the U.S. Although statistically not significant, the estimated coefficient for the carbonated soft drink price variable was positive, depicting the substitutability of carbonated drinks with beer.

Income is expected to have a positive impact on the demand for any normal good. Table 3 shows that the estimated coefficient of disposable income was positive (but statistically not significant), which indicates that beer is a normal good; this is consistent with the finding of Denney et al. (2002), who also found that beer consumption grows with income growth; this is because beer is an affordable luxury item.

We now turn our attention to the supply relation results in column four of Table 3. The signs on the coefficients for both labor cost and material cost variables were negative and statistically not significant.

We expected both variables to have a positive and significant impact on beer price, because rising labor and material costs should lead to a rise in beer prices. Similarly, decreasing labor and material costs should, in the absence of market power, pass on the cost savings to consumers in terms of lower beer price. We were unable to support our expectations because the coefficients of these cost variables were statistically insignificant. On the other hand, as one would expect, taxation leads to higher prices - the sign on the coefficient of the federal taxation variable is positive and statistically significant. This indicates that a dollar increase in the Federal beer tax per barrel will have a thirteen-fold increase in beer prices.9 Finally, the parameter estimate on capital stock is negative as expected (not statistically significant).

Higher capital stock implies firms investing in better technology, which is expected to lower the cost of beer production, leading ultimately to lower beer prices – a sign of firms becoming more efficient through improved technology and able to compete in a price-sensitive market.

Finally, results in Table 3 show that market power parameter (ω) is positive but not statistically significant, that is, we were unable to reject the null hypothesis that there is no market power in the U.S. brewing industry, i.e., (ω=0) Although statistically indifferentiable, the positive sign of the parameter (ω) indicates the existence of market power in the U.S. beer industry.

9 Although the impact of the federal tax seems excessive, we left that debate for another time, given that the focus of this study lies elsewhere.

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Given the continued consolidation that led to increasing industry concentration in the U.S. brewing industry, our finding was unexpected but was not surprising. It is not surprising because our finding is similar to Tremblay and Tremblay (1995), who also detect the existence of some statistically indifferentiable market power in the U.S. brewing industry. Given the impact of the capital stock on beer prices (negative, albeit statistically insignificant), our finding may provide a weak support to Demsetz’s (1973) proposition that efficient firms are able to grow over time, resulting in larger and more efficient firms leading to a rise in market concentration, but not necessarily market power. In the U.S. brewing industry, such cost-efficient firms with large market shares would be the macro breweries, such as AB InBev and MillerCoors.

CONCLUSION Increased consolidation in the U.S. brewing industry has left the industry with the top four firms controlling almost 78% of the beer market in the country. A legitimate concern that arises out of such continued consolidation is whether there is market power in this industry. Using structural models of oligopoly behavior, we estimate conduct parameters that identify a degree of market power in this industry over the 1977-2006 period, during which the sector experienced a rapid increase in concentration and the demise of many small firms.

We find that the U.S. beer market is price-sensitive, and both distilled spirits and carbonated soft drinks were competitors (or substitutes to beer). Given distilled spirits are gaining ground on beer over the last few years and beer consumers are sensitive to price, it may require innovations in both the production and marketing of beer to regain lost ground in the U.S. alcoholic beverages market.

While we were unable to detect the impact of labor and material costs on the price of beer, we were not surprised to find that excise tax (federal tax on alcohol) increased beer price.

In terms of the focus of this article, our empirical results indicate that although we find evidence of the existence of market power in the U.S. beer industry, we are unable to refute (statistically) the hypothesis that there is no market power in this industry. In line with an earlier study by Tremblay and Tremblay (1995), we conclude that while there is some indication of market power, there is not enough statistically supported evidence to conclude that there is market power in the U.S. brewing industry, i.e., brewers do not exert oligopoly power over downstream firms (distributors and retailers). Although none of the coefficients of variables representing cost and technology were statistically significant, their directions indicate that lower cost and better technology may allow efficient firms to lower beer prices and gain market share (and thereby leading to even higher concentration).

In terms of future research agenda, the research presented here could be extended beyond the year 2006 to include more recent data; variations of the NEIO model, such as the SF approach, could also be utilized, albeit with a priori assumptions about market competitiveness in the U.S. brewing industry.

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Kumbhakar, S.C., Baardsen, S., and Lien, G. (2012). “A new method for estimating market power with an application to Norwegian sawmilling.” Review of Industrial Organization 40(2):109-129.

National Beer Wholesalers Association (NBWA). (2019). “Industry Fast Facts.” Accessed July 8, 2019. https:// www.nbwa.org/resources/industry-fast-facts.

Nevo, A. (2001). "Measuring Market Power in the Ready-to-Eat Cereal Industry." Econometrica 69: 307-342.

Pepall, L., D. Richards, and G. Normam. (2002). Industrial Organization: Contemporary Theory and Practice.

New York: South-Western College Publishing.

Pipoblabanan C. (2008). “Essays on Parametric and Nonparametric Estimation of Market Structure and Tax Incidence in the U.S. Brewing Industry.” Ph.D. Thesis, Oregon State University.

Rojas, C. (2008). “Price Competition in U.S. Brewing.” Journal of Industrial Economics 61:1-31.

Slade, M. (2004). “Market Power and Joint Dominance in U.K. Brewing.” Journal of Industrial Economics 52: 133–163.

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Statistica. (2019). "Per-capita volume sales in the beer market worldwide, by country in 2018 (in liters)." Chart.

January 3, 2019. Statista. Accessed July 10, 2019. https://www.statista.com/forecasts/758742/per-capitavolume-sales-in-the-beer-market-worldwide-by-country Tremblay, V. J. and C.H. Tremblay. (1995). “Advertising, price, and welfare: evidence from the US brewing industry.” Southern Economic Journal 62(2): 367–381 Tremblay, V. J. and C.H. Tremblay. (2005). The US Brewing Industry: Data and Economic Analysis. Cambridge, MA: MIT Press.

U.S. Brewers Association. (2011). Brewers Almanac, various issues, Brewers Association, United States: Washington, DC. U.S. Department of Commerce. (2013). Manufacturing: Summary Series: General Summary: Industry Statistics for Industry Groups and Industries: 2007. Bureau of the Census. Available at https://www.census.gov/ data/tables/2007/econ/census/manufacturing-reports.html. Accessed on July 10, 2019.

U.S. Department of Commerce. (2012). Bureau of the Census. Annual Survey of Manufacturers various issues, Washington DC. ----------. (2011). Bureau of Economic Analysis. National Income in the United States, various issues, Washington, DC. U.S. Department of Labor, Bureau of Labor Statistics. (2012). CPI Detailed Report, various issues, Washington DC. Wall Street Journal (WSJ), (2013). “U.S. Sues to Block Big Beer Merger.” written by Brent Kendall and Valerie Bauerlein. January 31, Thursday.

Xia, Y., and S.B. Buccola. (2003). “Factor Use and Productivity Change in Alcoholic Beverage Industries.” Southern Economic Journal 70(1): 93-109.

Tables Table 1: Variable descriptions Variable

Description

Qt

Quantity of beer consumed measure in milU.S. Brewers Assoc. (2011) lions of 31-gallon barrels

Pt

Source

Price per 31-gallon barrel (Base=1984)

U.S. Brewers Assoc. (2011)

Price

Price index of spirits (Base=1984)

U.S. Department of Labor (2012)

PriceCols

Price index for carbonated drinks (Base=1984)

U.S. Department of Labor (2012)

Spirits

Inct w

Per capita Disposable income in 1984 dollars. U.S. Department of Commerce (2011).

wt

Payroll price per barrel in 1984 dollars

Bartelsman, Becker, and Gray (2005)

Material price per barrel in 1984 dollars

Bartelsman, Becker, and Gray (2005)

Real Capital Stock in 1984 dollars

Bartelsman, Becker, and Gray (2005)

L

t

M

Kt

tax

fed t

Federal excise tax rate (dollars per barrel) U.S. Brewers Assoc. (2011) in 1984 dollars

Table 2: Descriptive statistics of variables Variable Qt

Std.

Minimum

Maximum

193.190

9.847

177.813

213.094

75.327

7.870

60.875

88.1439

Price

98.697

5.300

88.059

111.667

Price

83.035

12.704

67.940

107.265

16870.139

3620.729

10476.088

21835.505

Pt Spirits Cols

Inct w L

t

wtM

Kt taxtfed 76

Mean

5.385

1.609

2.568

8.390

34.220

3.603

23.545

39.706

14535.476

2939.940

11187.494

20767.0266

13.144

3.224

8.478

17.622

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Table 3: Estimation results Variable

Parameter

Intercept

Demand Equation (Eq. 7)

Supply Equation (Eq. 8)

168.40(1.51)

66.11(0.50)

Ln(Price of beer/barrel)

lnPt

Ln(Price Index of Spirits)

lnPriceSpirits

56.97 (2.60)**

Ln(Price Index of Carb. Drinks)

lnPrice

6.08(0.37)

Ln(Disposable Inc. per Capita)

lnInct

0.91 (0.06)

Cola

Payroll Price/ barrel

wt wtL L L (wwLttLww ) tt

-8.07 (-0.79)

L

Materials Price/ barrel (Mat. Price/barrel*Payroll price/barrel)

-67.92(-4.77)***

1 2

Real Capital Stock

Kt

Federal Tax/barrel

taxt

Market Power Parameter

ω

-0.67 (-0.40) 4.44 (0.55)

1 2

-0.001 (-1.26) 13.0 (3.70)**

fed

0.08 (0.13)

R2 0.83 0.68 Note: (i) *** Indicates significance at 99%; **Indicates significance at 95%; (ii) t-statistics in parentheses.

APPENDICES: VARIABLES AND MODEL ESTIMATION DETAILS Appendix Table 1: Model variables in SAS Variables in equations (7) and (8)

Corresponding variables in the SAS program

Qt

QUANTITY

Pt

PRICE_BARREL_1984

Price

SPIRITS_PRICE_INDEX_1984

Price

CARB_DRINK_PRICE_INDEX_1984

Inct

DISINC_PER_CAPITA_1984

Spirit Cola

wtL w

PAYROLL_BARREL_1984 MAT_BARREL_1984

M

t

Kt

REAL_CAPTIAL_STOCK_1984

taxt

fed

FTAX_1984

Appendix Table 2: SAS program (SAS 9.4 used) (i) Estimation of the U.S. Brewing industry NEIO model: SAS codes proc model data=sasuser.BeerdataEJAE; exogenous lnspirits lncarb lndi_per_capita_1984 payroll_barrel_1984 mat_barrel_1984 ftax_1984 real_capital_stock_1984; parms a0 a1 a2 a3 a4 b0 b1 b2 b3 b4 b5 b6 ; label a0='dem. intercept' a1=' ln price beer' a2=' ln price spirits' a3= ' ln carb price' a4=' ln di' b0="supply inter" b1='payr/bar' b2='mat/ bar' b3='matpay' b4='capital' b5='ftax' b6='mrkt power'; **Estimated equations**; quantity=a0+a1*lnprice_barrel_1984 + a2*lnspirits + a3*lncarb + a4*lndi_ per_capita_1984 ; 77

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price_barrel_1984 = b0 +b1*payroll_barrel_1984 + b2*mat_barrel_1984 + b3*matpay + b4*real_capital_stock_1984 + b5*ftax_1984 +b6*quantity ; fit quantity price_barrel_1984 / 3sls hausman ; instruments a2 a3 a4 b1 b2 b3 b4 b5 ; run;

Appendix Table 3: Data file (ii) Log transformation of variables lnprice_barrel_1984=log(price_barrel_1984); lnspirits=log(spirits_price_index_1984); lncarb=log(carb_drink_price_index_1984);

lndi_per_capita_1984=log(disinc_per_capita_1984);

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POSTOJI LI TRŽIŠNA MOĆ U OKVIRIMA AMERIČKE INDUSTRIJE PIVA?

Rezime: Uvećana konsolidacija u industriji piva SAD-a, s pravom je dovela do brige u vezi sa sposobnošću činilaca te industrije da iskažu tržišnu moć u odnosu na naftnu industriju. Zasnivajući se na strukturalnom modelu oligopolskog ponašanja, ovo istraživanje analizira tržišnu moć industrije piva u SAD-u, tokom perioda od 30 godina, tokom kojeg je ovaj sektor iskusio ubrzan porast – kada je u pitanju koncentracija i gašenje nemalog broja malih firmi. Rezultati pokazuju da je tržište piva osetljivo na cene, kao i da je pivo zamenilo i žestoka pića i gazirana, bezalkoholna pića. Premda nismo mogli da utvrdimo uticaj vrednosti radne snage i materijala na cenu piva, utvrdili smo da je federalni porez na pivo uvećao cenu istog. Naši rezultati ukazuju na to da – iako postoji naznaka tržišne moći, američka industrija piva nije uspela da emituje oligopolsku moć u odnosu na naftnu industriju (distributere i preprodavce).

Ključne reči: američka industrija piva, tržišna moć, NEIO (novi međunarodni ekonomski poredak).

JEL classification: L13, D43

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EJAE 2020, 17(1): 80 - 103 ISSN 2406-2588 UDK: 339.564(4-672EU) 338.121(4-672EU) DOI: 10.5937/EJAE17-23638 Original paper/Originalni nauÄ?ni rad

HAVE EXPORT COMPOSITIONS INFLUENCED ECONOMIC GROWTH OF THE EUROPEAN UNION COUNTRIES IN CENTRAL AND EASTERN EUROPE? Donny Tang Economics Department, Temple University, Philadelphia, USA

Abstract: This study analyzes export composition effects on economic growth in the European Union (EU) countries of Central and Eastern Europe (CEE) during 1999-2016. The results confirm that the fuel and food exports have boosted growth after EU accession. As expected, agricultural exports have no effect on growth. Due to the different comparative advantages, the CEE countries have still relied on some raw material exports while developing manufactured exports. The results also indicate that the transportation equipment, textile, steel, and chemical exports have accelerated growth. This can be attributed to the economies of scale in production, given their greater access to EU markets.

Article info: Received: October 17, 2019 Correction: November 16, 2019 Accepted: December 4, 2019 Keywords: export composition, economic growth, economic integration.

JEL Codes: F10, F14, F15, F43, O52

INTRODUCTION The relationship between export and economic growth has been one of the most crucial issues in development economics literature. They found a positive export effect on economic growth in developing countries.

However, most of them failed to distinguish between different types of exports and their respective effects on growth. The reason why this is important is that productivity and externality effects associated with exports are likely to be higher in the case of manufactured exports as opposed to primary exports (Greenaway, Morgan, and Wright, 1999). Given that export emphasis is shifting from primary to manufactured exports, developing countries can achieve higher economic growths in the long run. This paper examines whether export compositions have played a crucial role in determining economic growth in the European Union (EU) countries in Central and Eastern European (CEE) countries during 1999-2016.

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*E-mail: dnytng@gmail.com

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The disaggregated export composition analysis would provide valuable insights into the exportgrowth relationship since EU accession in 2004. The results would indicate whether manufactured export specialization would facilitate higher growth than raw material export specialization.

Most previous studies confirmed the contribution of exports to economic growth of the CEE countries during the transition period (Dawson and Hubbard, 2004). At the early stage of development, they still relied on labor-intensive and raw material products as their major export products to earn export revenues. During the 1990s, the massive inflows of foreign direct investments had helped these countries gain significant comparative advantages in manufactured exports (Zaghini, 2005).

Specifically, the open trade policy facilitated their access to the advanced technologies of western EU countries (De Boyrie and Johns, 2013). More developed CEE countries allocated more resources toward high-technology manufactured exports to target western EU markets. The export diversification policy helped consolidate their market shares in these countries. As they achieved higher economic growth, they started to specialize in high-quality manufactured exports with comparative advantages (Naude, Bosker, and Matthee, 2010). The export specialization policy helped to expand their market shares in high-income countries. Since their EU accession, the CEE countries enjoyed economies of scale in production and marketing, as they had greater access to the entire EU markets. Their focus on high-quality manufactured exports made themselves more competitive in the EU countries (Kali, Mendez, and Reyes, 2007). Their strong export performance explained their sustainable high growth over the past two decades.

Some of the CEE countries had deepened their financial integration with western EU countries by joining the European Monetary Union (EMU) in 2008. The adoption of the euro, which eliminated exchange rate volatility, further boosted the trade flows between the CEE and the western EU countries.

The deep CEE trade ties with more affluent trading partners accelerated their economic growth. It is noteworthy that the CEE countries with export compositions similar to that of western EU countries tended to achieve higher income convergence (De Benedictis and Tajoli, 2007b; De Benedictis and Tajoli, 2008). This can be attributed to the major shift in export composition from primary to manufactured exports. The resource allocation toward more productive export sectors helped these countries achieve better export performances. This in turn reduced their income gap with western EU countries.

The emphasis on manufactured exports was very favorable for promoting their growth over the past decades (Balaguer and Cantavella-Jorda, 2004). Similar to the CEE countries, the developing eurozone countries further boosted their growth through increasing the share of high technology exports in total exports during 1988-2009. In general, market competition for high-technology products depends on quality rather than price. Their overall demand would remain very stable regardless of change in relative product prices (Wierts, Van Kerkhoff, and De Haan, 2014). The western EU countries have long served as major export markets for the CEE raw materials and manufactured exports. Their strong trade ties have facilitated the progress toward specialization in high-technology manufactured exports. An in-depth analysis of export composition pattern would suggest the best export-led growth policies for the CEE countries in the near future.

This is one of very few studies to examine the effect of CEE export compositions on economic growth.

The CEE countries have been accelerated their economic growth through export promotion policy since the early 1990s. More developed countries, such as Hungary and Poland, shifted their export emphasis from primary to manufactured exports since their EU accession. Those countries that mainly imported capital goods for producing consumer goods grew faster than those that only exported capital goods (Lewer and Van den Berg, 2003). Their change in the export composition pattern helped sustain their 81

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high economic growth for the past two decades. As some of these countries achieved middle-income status, they further diversified their export types to expand their export markets. While they allocated more resources to develop high-technology manufactured exports at the later stage of development, they also continued their raw material exports to earn stable amounts of export revenues, especially during the crisis period (Grancay, Grancay, and Dudas, 2015). To sustain high economic growth in the long run, the CEE countries would need to develop more effective export composition policies to boost their export competitiveness. In particular, they have to better coordinate industrial and trade policies for allocating resources in high-technology production innovation (Cuaresma and Worz, 2005).

This study would suggest the appropriate export composition policies for maintaining high economic growth for CEE countries in the long run.

The remainder of this study is structured as follows: Section 2 provides a brief literature review on the impact of export on economic growth. It focuses on the importance of export diversification and specialization policy for growth in developing countries. Section 3 describes the empirical models for estimating the impact of export compositions on economic growth after EU accession. Section 4 presents the results and discusses their significance. The policy implications for long-term export policies to boost growth are discussed in section 5. Section 6 concludes with a summary of the main results.

LITERATURE REVIEW Most empirical studies confirm the positive export effect on economic growth in developing countries. They further examine the importance of export diversification in boosting growth. Developing countries can achieve higher growth through export diversification at the early stage of development (Naude, Bosker, and Matthee, 2010). To a certain extent, foreign direct investment inflows help these countries diversify their exports. Local companies can adopt foreign production technologies to improve the quality of their exports. High export diversification would make their economies better insulated against foreign economic shocks. It would help these countries to facilitate deeper industrialization process in the long run (Eicher and Kuenzel, 2016). As developing countries become middle-income countries, they gradually shift their emphasis toward export specialization to boost their export competitiveness (Naude and Rossouw, 2011). Nonetheless, some of these countries still maintain export diversification in both primary and manufactured export production. Even upper middle-income countries can achieve high growth through export diversification (Gozgor and Can, 2017). However, this may indicate slow adjustment in the industrialization process when high-technology manufactured export sectors appear rapidly but low-skilled manufactured export sectors still remain (Cadot, Carrere, and Strauss-Kahn, 2011). As a result, their overall economic growth rates would slow down. Developing countries must learn how to balance export diversification in primary and manufactured exports. They should allocate appropriate resources to improve their production efficiency in the long run.

Previous studies provide strong evidence for the role of export specialization in facilitating economic growth. As developing countries become middle-income countries, they can produce high value-added industrial goods for both local and foreign markets. They can accelerate their growth through export specialization in high-technology manufactured goods. Countries exporting goods with higher productivity levels would achieve higher growth than those with lower productivity levels. The production of manufactured exports is associated with higher productivity and externality effects (Greenaway, Morgan, and Wright, 1999). These exports can bring positive externalities, such as knowledge and technology spillovers, to developing countries improving their product innovation (Wierts, Van 82

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Kerkhoff, and De Haan, 2014). Thus, countries grow faster if they primarily import capital goods to produce consumer goods. In some cases, fuel, metals, and textile export specialization can lead to higher growth than primary, food, and machinery export specialization (Naude, Bosker, and Matthee, 2010).

Further improvement in production efficiency would increase the competitiveness of fuel, metals, and textile exports. The capacity to produce more sophisticated goods would yield positive externalities that are highly conducive to growth (Hausmann, Hwang, and Rodrik, 2007). Exports of high-quality manufactured goods would help developing countries increase their export revenues which, in turn, would boost their growth.

Relevant studies argue that the growth effect of export specialization may vary depending on the prices of primary exports. If the prices of these exports remain stable, countries producing more sophisticated exports would achieve higher growth (Grancay, Grancay, and Dudas, 2015). Moreover, the product quality would affect economic growth. Compared to countries with an emphasis on lowquality exports, countries with an emphasis on high-quality exports would experience higher growth (Huchet-Bourdon, Le Mouel, and Vijil, 2018). Richer developing countries would need to focus on high-technology manufactured exports to maintain high growth. In other words, they have to shift from export diversification to export specialization as part of their long-term growth policy. Other studies point out that the income of trading partners may affect economic growth. Developing countries that mainly trade with developed countries would achieve higher growth, as they can benefit from technology transfer from these countries. More importantly, the skilled labor participation in high-technology export production would facilitate technological innovation in other types of exports (Kali, Mendez, and Reyes, 2007). Developing countries can quickly catch up with developed countries in product innovation through technology transfer.

Similar studies compare different growth effects between raw material exports and high-technology manufactured exports. In contrast to high-technology exports, raw material exports would result in lower growth among developing countries. In some cases, raw material exports may have no impact on growth at all (Poncet and De Waldemar, 2013). Developing countries should diversify their export composition to achieve sustainable higher growth (Murshed and Serino, 2011; Dreger and Herzer, 2013). While they often can boost their growth through shifting toward differentiated manufactured exports, they would need to continue to produce raw material exports for earning stable amount of export revenue. In general, global demand for raw material exports remains high because most of them are necessity products. Even major economic shocks would not substantially affect their overall demand. It is important for developing countries to better allocate resources for producing both raw materials and manufactured exports. Exposure to international competition would result in a more efficient use of production resources and boost their overall productivity (Cuaresma and Worz, 2005).

For the past decades, most developing countries have substantially boosted their economic growth through adopting export-led growth policy. Given the positive relationship between export and growth, it is important to examine whether raw materials and manufactured exports have played a crucial role in promoting economic growth among the CEE countries. The results would suggest the appropriate amount of resource allocation for their production in the long run.

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ECONOMETRIC SPECIFICATION Estimation Model The empirical model examines whether export compositions have affected the CEE economic growth during the period 1999-2016. The regression equation, which includes various types of raw material and manufactured exports, is given as: log(GDPGrowthit) = α + β1 log(Curactit) + β2 log(Debtit) + β3 log(Tconcenit) + β4 log(FDIit) + β5 log(Popgrowit) + β6 log(Capformit) + β7 log(Schoolit) + β8 log(Agricit) + β9 log(Foodit) + β10 log(Fuelit) + β11 log(Machit) + β12 log(Textilit) + β13 log(Transpit) + β14 log(Steelchemit) + εit

(1)

where GDPGrowthit is the growth rate of real gross domestic product (GDP) per capita of the CEE country i at year t (1999-2016). All variables are measured in US dollars adjusted for inflation to the base year 2005. The CEE countries include Bulgaria, Croatia, Cyprus, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Romania, Slovakia, and Slovenia. In particular, Cyprus, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, and Slovenia joined the EU in 2004. While Bulgaria and Romania joined the EU in 2007, Croatia followed suit in 2013. Only seven CEE countries have joined the EMU as they have fulfilled the convergence criteria for euro adoption.

Slovenia first adopted the euro in 2007, whereas Cyprus and Malta adopted it in 2008. Subsequently, Slovakia and Estonia adopted it in 2009 and 2011, followed in suit by Latvia and Lithuania in 2014 and 2015 (European Commission, 2019). Due to the elimination of trade barriers, the EU has allowed the CEE countries to boost their trade flows with western EU countries. Moreover, the EMU that has facilitated the use of the euro in seven CEE countries has expanded their trade flows with eurozone countries. As expected, the EMU substantially promoted trade flows among eurozone countries during 2000-2013 (Mikaa and Zymekb, 2018). Since the eurozone and non-eurozone CEE countries have established closer trade ties with western EU countries, their inclusion in this study would provide more accurate analysis of the export composition effect on economic growth.

The main variables of interest are the export composition variables – raw materials and manufactured export variables. The raw material export variables (Agric, Food, and Fuel) are the total values of agricultural exports, food exports, and fuel exports divided by the CEE GDP. To adopt export-led growth policy, developing countries always depend on raw material products as their main exports at the early stage of development. Dependence on raw material exports would help boost growth if these exports would remain in high demand (Naude, Bosker, and Matthee, 2010). The rising prices of these exports have made them to be one of the main growth determinants in developing countries for decades (Grancay, Grancay, and Dudas, 2015). On the contrary, previous studies argue that raw material exports have no impact on growth in the long run. There may be a positive relationship between raw material exports and economic growth. But this effect may not persist in the long run (Poncet and De Waldemar, 2013). Developing countries would not maintain sustainable high growth if they failed to develop high-technology manufactured industries (Murshed and Serino, 2011; Dreger and Herzer, 2013). In fact, raw material exports may have a negative effect on growth because a growing number of developing countries compete for the same export markets. To boost their export competitiveness, they need to emphasize export diversification in high-technology manufactured exports at the later stage of industrialization. Based on these arguments, the raw material export variable may have either positive or negative effect on the CEE economic growth.

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The other important export composition variables are the manufactured export variables. Mach, Textil, Transp, and Steelchem refer to the total values of machinery exports, textile exports, transportation equipment exports, and steel and chemical exports divided by the CEE GDP. In contrast to the raw material export variables, the manufactured export variables always have a positive effect on growth in developing countries. They can achieve higher growth if they specialize in new high-quality manufactured exports (Huchet-Bourdon, Le Mouel, and Vijil, 2018). Export composition has facilitated the better export performance of eurozone countries for the past two decades. In particular, they have accelerated their growth through increasing the share of high-technology exports in their total exports (Wierts, Van Kerkhoff, and De Haan, 2014). The reason is that high-technology exports tend to have higher productivity levels, which would increase profits for developing countries (Naude, Bosker, and Matthee, 2010). Moreover, these exports would yield positive externalities, such as knowledge and technology spillovers. More efficient resource allocations, along with higher productivity level and technological progress, would make these exports more competitive in global markets (Cuaresma and Worz, 2005). Developing countries would grow faster if they shifted from export diversification to export specialization policy at the later stage of development. In fact, the CEE countries have adopted export-oriented growth policies before joining the EU. They signed several free trade agreements to gain access to western EU markets during the 1990s. The EU has further boosted their trade flows because of the removal of non-tariff trade barriers. Some of the CEE countries have joined the EMU to deepen their trade ties with richer eurozone countries (Jagelka, 2013). The access to the larger export markets has allowed them to benefit from economies of scale in production of high-technology manufactured exports. The allocation of more resources to produce higher productivity exports, such as machinery and transportation equipment exports, would help accelerate their economic growth in the long run.

Therefore, the manufactured export variable would have a positive effect on the CEE economic growth.

Another variable of interest is the trade concentration variable (Tconcen). The trade concentration variable is the Hirschman Herfindahl index, which measures the dispersion of trade value across an exporter’s partners. A country with trade that is concentrated in a very few markets would have an index value close to one. Similarly, a country with a perfectly diversified trade portfolio would have an index value close to zero. Developing countries can achieve higher growth from trade concentration due to the benefit of economies of scale. This would allow them to allocate their major production resources for a few high value-added export products. The large-scale production would facilitate more foreign technology transfer and adoption. Their exports would become more competitive in high-income countries (Kali, Mendez, and Reyes, 2007). The CEE countries have showed high trade concentration since the 1990s. The EU accession has boosted their trade concentration in western EU countries because of the strong consumer demand for manufactured exports. The good export performance has been conducive to high economic growth. Therefore, the higher trade concentration variable would have a positive effect on the CEE economic growth.

Foreign direct investment is considered one of the main growth determinants for the CEE countries.

The foreign direct investment variable (FDI) is equal to the FDI inflows divided by the CEE GDP. The local manufacturing industries would benefit from FDI inflows through technology and management skill transfer. The positive externalities can help accelerate their technological process and therefore raise productivity level. The export-oriented firms can further boost their export competitiveness (Sohinger, 2005). The CEE countries have received huge FDI inflows from western EU countries since the mid-1990s because they have very favorable investment environments for foreign investors. Most of these inflows have included the imports of high-technology product components.

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The local firms have adopted the latest foreign technologies to facilitate their product innovation.

To some extent, they have accelerated their technological progress to produce more high value-added industrial goods. Better export performance has further boosted their economic growth (Jouini, 2015).

The FDI variable would have positive effect on the CEE economic growth.

The other explanatory variable that can significantly affect growth is the current account variable (Curact).

Curact is the current account balance which is equal to the sum of net exports of goods and services, net primary income, and net secondary income as a share of the CEE GDP. The current account deficit would incur heavy financial burden on interest repayment, which eventually would lead to major debt default in the CEE countries (Cuestas, 2013). Moreover, the persistent current account deficit would make their economies more volatile to external economic shocks. Therefore, current account deficit would have a negative effect on their economic growth. However, it may have a positive effect on their economic growth. Since EU accession, the CEE countries have received the higher level of foreign borrowing because of greater access to the larger EU financial markets. Their current account deficits have further increased (Slavov, 2009). EMU membership has deepened their stock market and banking sector integration with western EU countries. Greater access to massive foreign capital inflows has financed their growing consumption and investment (Herrmann and Winkler, 2009). To a certain extent, the larger current account deficits have sustained their high economic growth since EU accession. Therefore, the current account variable may have either a positive or negative effect on the CEE economic growth.

The public debt variable (Debt) is the total amount of public debt divided by the CEE GDP. An increase in debt level would have positive or negative effect on economic growth. The short-term debt effect on economic growth is positive. But the positive effect from short-term economic stimulus from more debt may decrease when the initial debt level is high. Eventually, the debt level would have negative effect on growth (Baum, Checherita-Westphal, and Rother, 2013; Eberhardta and Presbiteroc, 2015).

More debt accumulation would have a very negative growth impact among countries with debt-toGDP ratios above 95 per cent. The increase in public debt, followed by the relaxation of fiscal austerity programs, may not boost economic growth in high indebted countries. In fact, it may increase their decline (Gomez-Puig and Sosvilla-Rivero, 2015). This is the case for the twelve eurozone countries with debt-to-GDP ratios of about 90-100 percent (Checherita-Westphal and Rother, 2012). High levels of debt in Greece, Italy, and Spain primarily triggered the eurozone debt crisis in 2010. They experienced very slow economic growth for several years after the crisis. Therefore, the public debt variable would have a negative effect on the CEE economic growth. It is noteworthy that most of the CEE countries have maintained very low public debt levels to avoid loan default. The moderate debt increase due to growing economic activities since EU accession has boosted their economic growth. Hence, the public debt variable may have positive effect on the CEE economic growth.

The three control variables are the population growth (Popgrow), capital formation (Capform), and education (School) variables. Popgrow is the annual population growth rate in the CEE countries.

Population growth is considered a key measure for human capital. At the early stage of economic development, developing countries need a large number of jobs for economies of scale production. The labor productivity would increase as jobs have become more skillful in adopting the latest technologies.

Hence, population growth has a positive effect on economic growth because of more efficient production processes (Grancay, Grancay, and Dudas, 2015). A more educated labor force and high population growth both have strong positive effects on growth (Naude, Bosker, and Matthee, 2010). Compared to other developing countries, the CEE countries have a very educated labor force for efficient production.

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They can facilitate more a efficient production of high-technology manufactured exports. Hence, the population growth variable would have a positive effect on the CEE economic growth.

The capital formation variable (Capform) refers to the gross capital formation as a share of the CEE GDP. It includes the expenditures on additions to fixed assets of the economy and net changes in inventory level as a share of GDP. Capital formation measures domestic investment. High domestic investment would boost economic growth in countries with high institutional quality (Dort, Meon, and Sekkat, 2014). Since EU accession, the CEE countries have substantially improved the quality of their investment environments for foreign investors. More importantly, lower labor costs within the CEE countries have helped reduce total production costs. The substantial increase in production activities has certainly boosted their economic growth. Therefore, the capital formation variable would have a positive effect on the CEE economic growth. Finally, the secondary school enrollment variable (School) is the proportion of the labor force that has secondary school education as a percentage of the total labor force in the CEE countries. It indicates the number of educated job positions available for complex productions. Secondary school enrollment is used in this study because complete data is available for all of the CEE countries. The more educated labor force would be highly favorable for research and development activities (Sterlacchini, 2008). The quick conversion of research innovations into commercial products would make CEE exports more competitive in western EU countries. Strong leadership in advanced technology would contribute to their sustainable higher economic growth in the long run. Hence, the secondary school enrollment variable would have a positive effect on the CEE economic growth.

The major variables included in equation (1) have been described in details. Table 7 reports summary statistics for the dependent and independent variables, including the instrumental variables.

Two-Stage Least Squares and Generalized Method of Moments Estimations There may be an endogeneity problem in the FDI variable. It is possible that FDI inflows would boost economic growth due to their positive externalities. But countries with higher growth can better attract more FDI inflows as they are considered more profitable markets by foreign investors. They can tap consumer markets for their expensive imports. To address the endogeneity problem, this study would use the two-stage least squares (2SLS) method to re-estimate the endogenous variable, namely, the foreign direct investment variable (FDI). The instrumental variables (IV) would replace FDI. The research and development expenditure variable (Rdexp) and inflation rate variable (Inflat) are the IV for FDI. Rdexp is the research and development (R&D) expenditures as a share of the CEE GDP. R&D expenditures include current and capital expenditures (both public and private) on creative work undertaken to increase knowledge and the use of knowledge for new applications. Higher R&D spending indicates the CEE determination to become research-intensive locations for foreign investors (Hubert and Pain, 2002). This would help attract more foreign direct investment in CEE countries.

Hence, higher R&D spending would boost foreign direct investment inflows into the CEE countries.

Moreover, the inflation rate variable (Inflat) is the inflation rate of the CEE countries measured by the consumer price index. It reflects the annual percentage change in the cost of living of average consumers, i.e., for buying goods and services that may be fixed or changed at a specified interval (yearly). The higher inflation rate would substantially affect the total production costs for foreign investors as it would result in higher wage increases. Therefore, higher inflation rates would deter foreign direct investment inflows into the CEE countries.

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In addition to the 2SLS method, equation (1) is also re-estimated by the dynamic generalized method of moments (GMM) method to control for the biases related to endogeneity, omitted variables, and unobserved country fixed effects. It can also address the heteroscedasticity and serial correlation problem. The first difference procedure is chosen as the transformation method to remove the crosssection fixed effects. The two IV for the FDI variable in the 2SLS methods are included as the IV in the dynamic GMM method.

The dataset for this study was available at Mendeley data website. The details on statistical software and procedures to derive the results were provided in my dataset (Tang, 2019).

Data Sources The World Bank’s World Development Indicators database provides the data for the dependent variable (growth of real GDP per capita) and some of the independent variables (current account balance, foreign direct investment inflow, population growth, capital formation, and education). Trade concentration data are obtained from the World Bank’s World Integrated Trade Solution database. The public debt data are available in the International Monetary Fund’s Historical Public Debt database.

All of the raw material and manufactured export data are taken from the Eurostat database.

ESTIMATION RESULTS Export Composition Effects on the CEE Economic Growth This study examines whether the export compositions have affected the economic growth in the CEE countries during 1999-2016. The EU and EMU have boosted the CEE export flows to the other member countries. To better assess the EU financial integration effect, the entire study period is divided into two subperiods for further analysis. The comparison of the two periods (1999-2003 and 2004-2016) would suggest whether the higher export flows triggered by the deeper financial integration have accelerated the CEE economic growth since EU accession in 2004. Moreover, the 2008 financial crisis and the 2010 eurozone debt crisis resulted in a drastic fall in consumer demand for the CEE exports to western EU countries. This led to their lower economic growth for several years after the crisis outbreak. Hence, this study would compare the pre-crisis and crisis periods (2004-2008 and 2009-2016) to determine whether the change in export flows due to the crisis outbreak has substantially affected the CEE economic growth during 2009-2016.

The main issue of this study is whether the export compositions have facilitated the higher CEE economic growth for the past two decades. To better analyze the disaggregated export effects, the estimations would be conducted for the raw material and manufactured export effects on growth respectively.

The 2SLS and GMM estimation results for both types of exports are provided in Tables 1 and 4. The results for the raw material exports are given in Tables 2 and 5. The overall results indicate that the fuel exports have a strong positive impact on the CEE economic growth after the EU accession. As seen in Tables 4 and 5, the GMM coefficient on Fuel is positive and statistically significant at the 1 percent level for the entire period 1999-2016. Moreover, the subperiod results in Table 5 indicate that the size and statistical significance of the coefficient became larger in 2004-2016 than in 2009-2016. This suggests that the positive fuel effect on growth appeared to be stronger during the EU period 2004-2016.

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But this effect has somewhat declined due to the outbreak of the US financial crisis and the eurozone debt crisis in 2009-2016. In contrast to the strong fuel export effect, the food exports only have a positive effect on the CEE growth during the crisis period 2009-2016. The coefficient on Food is positive in 2004-2016 and 2009-2016, but only becomes statistically significant in the latter period. This result should be interpreted with caution. The positive food effect during 2009-2016 could be attributed to the crisis factor. To offset the drastic drop in the export demand, the CEE countries might have allocated main resources to produce necessity goods, such as food exports, to maintain stable export revenue.

This explains why the food export effect on growth has become positive during the crisis period.

The overall results confirm the positive fuel export effect on the CEE economic growth after the EU accession. To a certain extent, the export specialization in major raw material exports, such as fuels, can boost growth (Naude, Bosker, and Matthee, 2010). Despite the ongoing shift toward manufactured exports, the CEE countries have maintained significant amounts of fuel exports to sustain their growth.

Due to the different comparative advantages, most of the low-income CEE countries have still relied on raw material exports as their major exports (Zaghini, 2005). In particular, fuel exports have accelerated their growth since EU accession in 2004. The deep trade ties have consolidated western EU countries as the major markets for the CEE exports for decades (De Benedictis and Tajoli, 2007). Moreover, the EU has allowed the CEE countries to have greater access to the entire EU markets for their fuel exports.

The adoption of the euro has further expanded their trade flows with other eurozone countries because of the elimination of exchange rate volatility (Mikaa and Zymekb, 2018). The drop in fuel export prices has further boosted their competitiveness in western EU countries. The steady gain in the market shares has contributed to the CEE economic growth since the EMU accession. Moreover, it is noteworthy that the fuel and food exports have continued to promote their growth despite the crisis outbreak in 2009-2016. There has been a sharp drop in the consumer demand for the CEE exports as a result of the 2010 eurozone debt crisis. Nonetheless, the demand for necessity exports, such as fuels and food, has remained high in western EU countries despite the onset of the crises. The rising prices and steady demand for raw material exports have helped sustain the CEE economic growth during the crisis period.

Contrary to expectations, agricultural exports have no impact on CEE growth at all. As presented in Tables 1, 2, 4, and 5, the coefficient on Agric is not statistically significant at all during the entire period 1999-2016. This may be attributed to the adoption of the EU Common Agricultural Policy for several decades. Due to food security reasons, western EU countries have allocated a large amount of funding to their local agricultural sectors to ensure a steady supply of food products. They have given income subsidies and investment funds to farmers to modernize their farming sectors. This has made it difficult for the CEE agricultural and food exports to gain market shares in western EU countries. This can explain why the CEE agricultural and food exports have no positive effect on their economic growth during the study period. Most importantly, the results highlight the importance for the CEE countries to boost their growth through export diversification toward manufactured exports. This would allow them to reduce their dependence on raw material exports. Furthermore, it would better insulate their export industries against economic shocks (Eicher and Kuenzel, 2016). In particular, this can mitigate the negative impact of price fluctuations in raw material exports on export growth (Naude, Bosker, and Matthee, 2010). Export diversification would enable the CEE countries to maintain high economic growth after they become middle-income countries.

Most of the manufactured exports, excepting machinery exports, have a positive effect on the CEE economic growth. Specifically, the transportation equipment exports consistently have positive growth effect before and after the EU accession.

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As noted in Table 1, the 2SLS coefficient on Transp is positive and statistically significant in 1999-2003 and 2004-2016, with the level of statistical significance increasing to the 5 percent level during the EU and crisis periods 2004-2016. In contrast, the textile and steel and chemical exports only have positive effects after the EU accession in 2004-2008. As shown in Tables 1, 3, 4, and 6, the 2SLS and GMM coefficients on Textil are all positive and highly significant in 2004-2008. As presented in Tables 1 and 3, the same results are found in the 2SLS coefficients on Steelchem during the same period. Moreover, the overall results suggest that the textile and steel and chemical exports have the larger growth-enhancing effect than the transportation equipment exports. As noted in Table 1, the size of the 2SLS coefficients on Textil and Steelchem is larger than that on Transp. However, the transportation equipment exports have positive growth effect before and after the EU accession.

The overall results indicate that manufactured exports, such as textile, transportation equipment, and steel and chemicals, have accelerated CEE growth after their EU accession. The CEE countries have enjoyed the economies of scale in production because of greater access to western EU markets.

The shift toward manufactured exports has helped sustain the higher growth for the past decade (Kali, Mendez, and Reyes, 2007). Moreover, to maximize the benefits of strong export growth, they started to adopt a export diversification policy two decades ago. It has facilitated the risk diversification, which has reduced the possibility of adverse economic shocks in specific export sectors (Estevesa and Prades, 2018). In particular, this has stabilized the demand for the CEE manufactured exports in western EU countries. In sum, the export diversification policy has helped the CEE countries earn more export revenue despite the intense global competition. This has sustained their higher economic growth for the past decade. Another reason for the strong export-growth relationship is that the production of manufactured exports has resulted in the higher productivity and externality effects (Greenaway, Morgan, and Wright, 1999). The CEE companies have further improved their productivity levels through higher FDI and trade flows. The FDI inflows from western EU countries have facilitated the transfer of more efficient production methods to the CEE companies (Zaghini, 2005). They have adopted foreign production technology to improve the quality of differentiated manufactured exports. The resource allocations toward higher productivity export sectors have contributed to their higher economic growth (Naude, Bosker, and Matthee, 2010). Moreover, the production of manufactured goods has led to the higher externality effects, especially in high-technology manufactured exports. They have a higher potential for positive externalities, including the transfer of foreign knowledge and technology spillovers to the local companies through FDI. Meanwhile, the CEE countries have achieved better resource allocation and higher productivity in export industries rather than local industries due to the global competition factor (Cuaresma and Worz, 2005). Due to the EU membership, they have targeted western EU countries as the major export markets for their differentiated manufactured exports. The export diversification policy has accelerated their economic growth in the long run.

It is noteworthy that the similar export composition between the CEE and western EU countries has contributed to their higher income convergence. The CEE countries have adopted the export-oriented growth policy to accelerate their industrialization process since the early 1990s. The western EU countries have long served as the main export markets for the CEE manufactured exports. On average, they have imported 70 percent of the CEE exports for several decades. Their good export performance has been partly attributed to the implementation of western EU export policy. To boost their export competitiveness, CEE countries, such as the Czech Republic, Hungary, and Poland, have shifted their export emphasis toward high-quality manufactured exports. They have gained comparative advantages in manufactured exports through FDI and trade flows.

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Those countries that have similar export compositions with western EU countries have enjoyed higher income convergence since the 1990s (De Benedictis and Tajoli, 2008). The export-oriented growth policy with an emphasis on manufactured exports has helped reduce their substantial income gap with western EU countries (Huchet-Bourdon, Le Mouel, and Vijil, 2018). At the early stage of economic development, the CEE countries have continued to rely on raw material exports while diversifying high-quality manufactured exports. They have realized that the sole reliance on raw material exports has hindered their long-term economic growth (Dreger and Herzer, 2013). Based on their comparative advantages, they have allocated more resources toward high-quality manufactured goods (Naude and Rossouw, 2011). More research funding has facilitated the conversion of research innovation into commercial products. Given substantial improvement in product innovation, they have consolidated their market shares in western EU countries. This in turn has helped the CEE countries maintain sustainable high economic growth in the long run.

Other Explanatory Variables Affecting the CEE Economic Growth Several explanatory variables are considered as conventional determinants of economic growth.

First, the higher trade concentration has a positive effect on CEE growth during the crisis period. The estimation models in Tables 5 and 6 include the raw material and manufactured export variables, respectively. In both tables, the GMM coefficients on Tconcen are indeed positive and statistically significant in 2009-2016. This suggests that the trade concentration in the raw material and manufactured exports has promoted CEE growth after EU accession. The adoption of the euro has also boosted the intra-trade flows among the member countries during 2002-2013 (Mikaa and Zymekb, 2018). The CEE countries have expanded their market shares in the western EU countries as their differentiated manufactured exports have become more competitive in these markets (Kali, Mendez, and Reyes, 2007). It is interesting to note that the trade concentration effect became significant during the crisis period 2009-2016.

This can be explained by the fact that western EU countries have remained the primary export markets for the CEE exports. While the CEE countries have tapped other non-EU countries as alternative export markets, they have still relied on the EU markets because of their EU membership. Therefore, the higher trade concentration variable has a positive effect on the CEE economic growth during 2009-2016.

A surprising result is the lack of foreign direct investment effect on the CEE economic growth. As showed in Tables 1 to 6, the 2SLS and GMM coefficients on FDI are not statistically significant for the entire period. This is contrary to the argument that FDI inflows would benefit domestic industries through technology transfer in developing countries. The positive externalities of FDI can help improve their technological capability and raise their productivity level. The export-oriented industries can further boost their export competitiveness, which in turn can boost their economic growth (Sohinger, 2005; Jouini, 2015). It follows that the FDI inflows, mostly from western EU countries, should have accelerated the CEE economic growth. However, the result of this study provides no support for this argument.

The current account has a negative effect on the CEE economic growth during 2004-2008, but it has a positive effect during 2004-2016. As presented in Tables 1 to 3, the 2SLS coefficients on Curact are negative and statistically significant in 2004-2008, but become positive in 2004-2016 and 2009-2016.

The results are consistent with the argument that the current account variable has both a negative and positive effect on CEE growth. The increasing current account deficit diminished growth right after EU accession in 2004-2008. The CEE countries have increased their foreign borrowing from western EU countries to enhance their production activities.

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The current account deficit has incurred high financial burden on interest payment which has resulted in debt default (Cuestas, 2013). This lowered their economic growth during the early EU period 2004-2008.

But the current account has a positive growth effect after this period. The EMU has deepened the CEE stock market and banking sector integration with western EU countries. Greater access to foreign capital has helped finance the CEE growing consumption and investment (Herrmann and Winkler, 2009). The larger current account deficit has sustained their high economic growth since 2009. This can explain why the current account deficit accelerated the CEE growth in 2009-2016.

Finally, public debt has either little or no effect on CEE economic growth during the entire period.

As shown in Table 3, the only coefficient on Debt that is statistically significant and negative is found in 2004-2008, while the rest of the coefficients are not significant at all. The lack of the debt effect on growth is expected because of low public debt level among the CEE countries. In contrast to the eurozone crisis countries, such as Greece, Italy, and Spain, most of the CEE countries have maintained very low debt levels even before their EU accession. Their high economic growth performance has been partly attributed to low debt accumulation for decades (Gomez-Puig and Sosvilla-Rivero, 2015).

When existing debt levels have been kept very low, the increase in debt for economic stimulus reasons has resulted in their higher economic growth (Baum, Checherita-Westphal, and Rother, 2013). The CEE countries have maintained very low debt levels despite their greater access to foreign capital. This can explain why the debt level has no impact on the CEE economic growth during the entire period.

Policy Implications for the CEE Long-Term Economic Growth The results have provided very important policy implications on how to boost CEE economic growth through restructuring export policy. First, the results confirm that the raw material exports, such as fuel and food exports, have boosted CEE growth after the EU accession. Food exports have even become a crucial growth factor during the crisis period. In general, raw material exports, rather than manufactured exports, would lead to higher growth because the former have more stable prices than the latter during crisis periods (Grancay, Grancay, and Dudas, 2015). The demand for raw material exports would remain high as most of them are necessity products. The CEE countries should continue to emphasize production of raw materials for export. More resources should be allocated to this export sector, as it would earn stable amounts of export revenue (Dawson and Hubbard, 2004). In particular, the CEE countries should further improve production efficiency for fuel and food exports. To achieve this, the CEE countries should attract more foreign direct investments in raw material processing industries.

The transfer of foreign production technologies would help the CEE companies to boost their fuel and food export competitiveness in western EU countries. In the long run, resource allocations toward more industrialized export sectors would accelerate economic growth in the CEE countries (Balaguer and Cantavella-Jorda, 2004). While the CEE countries gradually shift their export emphasis toward manufactured exports, they should also maintain raw material exports as part of their export-led growth policy.

Second, the results suggest that the manufactured exports (textile, transportation equipment, and steel and chemical) have a positive effect on the CEE economic growth both before and after EU accession.

In particular, the textile and steel and chemical exports have larger growth effects than transportation equipment exports. The manufacturing export sectors in the CEE countries have developed rapidly after the EU accession. The massive FDI inflows from western EU countries have improved their product innovation in export industries. In particular, these inflows have upgraded the production facilities for manufacturing industries (Zaghini, 2005). The imports of advanced capital goods have facilitated the 92

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CEE companies to produce high value-added industrial exports. To some extent, the emphasis on importing capital goods for producing consumer goods has sustained their high economic growth (Lewer and Van den Berg, 2003). In the long run, they have to develop technology leadership to maintain their product competitiveness. Compared to western EU countries, the CEE countries have allocated a small amount of funding on research and innovation. The governments, rather than private companies, have primarily paid for the majority of these expenditures. This has slowed their progress in commercial product innovation. The CEE countries should plan industrial and trade policy to boost their technological progress in high-technology export industries. More resources should be allocated to efficient export sectors that can enhance economic growth (Cuaresma and Worz, 2005). The export-oriented countries, such as the Czech Republic, Poland, Hungary, and Slovenia, should concentrate on exporting manufactured goods with comparative advantages. Their main exports include automotive parts, electrical equipments and components, petroleum, and medicine. More research funding would boost their product innovation which, in turn, can consolidate their market shares in western EU countries.

Access to these high-income consumer markets would be favorable to their long-term economic growth.

Third, the CEE countries should implement deeper financial market reforms to facilitate research funding. Although they have become more integrated with western EU countries after the EU integration, their stock markets have not been fully developed to receive the growing capital inflows. The foreign investor entry has accelerated their stock market expansion. Nonetheless, their stock markets have remained underdeveloped in terms of financial depth (Caporale et al., 2015). They have become partially integrated with some of the major foreign stock markets, such as the German and US stock markets. The financial crises since 2008 have slowed the CEE stock market development. To expand the stock market size, the CEE countries should undertake major stock market reforms to establish proper institutional and corporate governance framework. Better market regulatory and supervisory mechanisms would increase their appeal to eurozone stock markets. The continued stock market expansion would facilitate more foreign capital inflows for product innovation research. Moreover, the CEE banking sectors have been dominated by western EU banks through massive bank mergers and acquisitions since the 1990s. However, the increase in bank capital has not been allocated for productive investment, such as technological innovation (Ductor and Grechyna, 2015). The CEE countries should deepen their banking sector reforms. Better banking regulatory frameworks would improve banking supervision and access to foreign bank capitals. As western EU mergers and acquisitions have resulted in excessive bank concentration, new anti-trust legislations should be established to maintain high level of bank competition. This would diversify foreign bank entry from non-EU countries. The increase in bank credit flows would be better allocated for research and development projects. More developed stock markets and banking sectors would help finance product innovation projects in the CEE countries. This would allow them to maintain sustainable high economic growth in the long run.

CONCLUSION This study examines whether export compositions have affected economic growth in the CEE countries during 1999-2016. First, the results confirm that major raw material exports have continued to play a crucial role in boosting CEE growth. Specifically, fuel exports have had a positive growth impact during the EU period 2004-2016. However, this effect has somewhat declined due to the outbreak of the US financial crisis and the eurozone debt crisis in 2009-2016. In contrast, food exports only have a positive growth effect during the crisis period 2009-2016. To offset the decline in the export demand, the 93

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CEE countries might have shifted their major resources toward necessity products, such as food exports, to maintain stable export revenues. This explains their positive growth effect during the crisis period. As expected, agricultural exports have no growth effect. This may be largely due to adopting the EU Common Agricultural Policy several decades ago. EU protectionist policies, such as farm subsidies and investment funds, have made it difficult for CEE farmers to gain market shares in these countries. Second, the results indicate that manufactured exports, such as transportation equipment, textile, steel, and chemical exports, have a positive growth effect before and after EU accession. The results also suggest that textile and steel and chemical exports have a larger growth effect than transportation equipment exports. The substantial growth effects can be attributed to the fact that the CEE countries have enjoyed the economies of scale in production. EU membership has led to their greater access to western EU markets.

Table 1: 2SLS Estimation of Export Composition Effects on CEE Economic Growth (1)

(2)

(3)

(4)

(5)

1999-2016

1999-2003

2004-2008

2009-2016

2004-2016

Curact

0.181*** (2.790)

0.043 (0.120)

-0.528* (-1.698)

0.198*** (2.459)

0.149** (2.236)

Debt

-0.079 (-0.311)

1.233 (0.560)

-0.702 (-1.239)

0.458 (0.804)

-0.191 (-0.632)

Tconcen

0.251 (0.728)

-1.409 (-0.234)

0.038 (0.104)

0.588 (0.610)

0.443 (1.182)

FDI

0.245 (0.711)

-0.537 (-0.158)

0.072 (0.114)

-0.065 (-0.312)

-0.002 (-0.008)

Popgrow

0.146* (1.853)

0.241 (0.953)

0.073 (0.613)

0.162 (1.506)

0.077 (0.958)

Capform

0.024 (0.027)

0.036 (0.020)

4.280*** (2.473)

-0.079 (-0.077)

0.084 (0.109)

School

-0.116 (-0.083)

3.117 (0.296)

7.164 (1.173)

1.444 (0.641)

-0.737 (-0.473)

Agric

-0.441 (-0.853)

-2.055 (-1.036)

-0.978 (-0.376)

-9.339*** (-2.662)

-1.190 (-1.218)

Food

0.216 (0.359)

-0.855 (-0.429)

-0.289 (-0.127)

8.573*** (2.624)

0.382 (0.436)

Fuel

0.161*** (2.359)

0.623 (0.799)

0.315 (1.546)

0.187* (1.751)

0.119* (1.662)

Mach

-0.173 (-0.702)

0.427 (0.347)

-0.837 (-1.241)

-0.990 (-1.280)

-0.813 (-1.574)

Textil

0.161 (0.551)

1.991 (0.471)

2.947*** (3.402)

-0.369 (-0.620)

0.204 (0.558)

Transp

0.197 (0.947)

0.814* (1.748)

0.714 (0.865)

0.791* (1.701)

0.635** (2.056)

SteelChem

-0.004 (-0.008)

-1.855* (-1.701)

2.150*** (2.656)

-0.438 (-0.739)

0.440 (1.227)

Adjusted R2

0.155

0.324

0.631

0.243

0.335

104

169

Observations 234 65 65 Notes: 2SLS refers to the two-stage least squares estimation.

All variables are in logarithm. T-statistics are reported in parentheses. ***, **, * indicate significance at 1%, 5%, and 10%.

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Table 2: 2SLS Estimation of Export Composition Effects on CEE Economic Growth (Raw Material Exports Only) (1)

(2)

(3)

(4)

(5)

1999-2016

1999-2003

2004-2008

2009-2016

2004-2016

Curact

0.196*** (2.989)

-0.089 (-0.219)

-0.613 (-0.711)

0.205*** (2.497)

0.189*** (2.130)

Debt

-0.112 (-0.471)

0.560 (0.282)

-0.641 (-0.861)

0.220 (0.386)

-0.298 (-1.030)

Tconcen

0.287 (0.901)

0.668 (0.174)

0.163 (0.367)

1.303* (1.748)

0.508 (1.428)

FDI

0.126 (0.416)

-1.344 (-0.521)

0.506 (0.294)

-0.105 (-0.461)

-0.065 (-0.079)

Popgrow

0.142** (2.057)

0.359 (1.151)

0.046 (0.228)

0.132 (1.254)

0.067 (0.848)

Capform

0.187 (0.266)

1.193 (0.435)

2.029 (1.053)

-0.342 (-0.357)

0.057 (0.077)

School

-0.152 (-0.123)

5.743 (0.423)

-4.214 (-0.592)

-0.131 (-0.069)

-1.894 (-1.280)

Agric

-0.361 (-0.766)

1.454 (0.632)

0.840 (0.325)

-9.041*** (-2.814)

-0.439 (-0.506)

Food

0.149 (0.320)

-1.916 (-0.830)

-1.436 (-1.134)

8.343*** (2.769)

0.004 (0.004)

Fuel

0.158*** (2.584)

0.752 (1.230)

-0.092 (-0.287)

0.145 (1.330)

0.124** (2.012)

Adjusted R2

0.212

0.066

0.329

0.223

0.322

Observations

234

65

65

104

169

Notes: 2SLS refers to the two-stage least squares estimation.

All variables are in logarithm. T-statistics are reported in parentheses. ***, **, * indicate significance at 1%, 5%, and 10%.

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Table 3: 2SLS Estimation of Export Composition Effects on CEE Economic Growth (Manufactured Exports Only) (1)

(2)

(3)

(4)

(5)

1999-2016

1999-2003

2004-2008

2009-2016

2004-2016

Curact

0.173*** (2.404)

0.222 (0.723)

-0.653*** (-2.426)

0.152** (1.876)

0.165*** (2.552)

Debt

-0.037 (-0.139)

-0.097 (-0.064)

-1.180** (-2.214)

0.038 (0.069)

-0.281 (-0.984)

Tconcen

0.375 (1.168)

2.677 (0.895)

0.330 (0.960)

1.164 (1.194)

0.490 (1.287)

FDI

0.255 (0.758)

1.127 (0.487)

0.018 (0.039)

-0.045 (-0.209)

0.072 (0.313)

Popgrow

0.150* (1.843)

0.140 (0.679)

0.020 (0.180)

0.149 (1.307)

0.075 (0.903)

Capform

0.282 (0.371)

1.116 (0.478)

3.129** (2.258)

0.805 (0.777)

0.072 (0.099)

School

-0.313 (-0.244)

9.003 (0.959)

5.242 (1.055)

-0.697 (-0.318)

-2.270 (-1.549)

Mach

-0.177 (-0.716)

1.068 (1.046)

-1.103* (-1.769)

-0.457 (-0.584)

-0.780 (-1.531)

Textil

-0.014 (-0.059)

-1.049 (-0.517)

2.332*** (4.592)

-0.274 (-0.440)

-0.026 (-0.077)

Transp

0.170 (0.913)

0.327 (0.608)

(1.366) 1.905***

0.529 (1.121)

0.455 (1.435)

SteelChem

0.095 (0.295)

-1.298 (-0.692)

1.905*** (2.537)

-0.768 (-1.489)

0.245 (0.796)

Adjusted R2

0.132

0.330

0.608

0.162

0.308

104

169

Observations 234 65 65 Notes: 2SLS refers to the two-stage least squares estimation.

All variables are in logarithm. T-statistics are reported in parentheses. ***, **, * indicate significance at 1%, 5%, and 10%.

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Table 4: GMM Estimation of Export Composition Effects on CEE Economic Growth (1)

(2)

(3)

(4)

(5)

1999-2016

1999-2003

2004-2008

2009-2016

2004-2016

Curact

0.156** (1.950)

0.031 (0.064)

0.130 (0.259)

0.127 (1.260)

0.148* (1.787)

Debt

-0.263 (-0.572)

-1.239 (-0.481)

-1.506 (-0.101)

0.445 (0.659)

0.046 (0.093)

Tconcen

0.441 (1.702)

2.306 (0.555)

0.020 (0.038)

1.556 (1.547)

0.423 (1.041)

FDI

-0.065 (-0.216)

2.873 (0.944)

-0.674 (-0.450)

-0.061 (-0.210)

-0.209 (-0.809)

Popgrow

0.203*** (2.560)

0.072 (0.202)

0.122 (0.611)

0.167 (1.407)

0.163* (1.713)

Capform

0.501 (0.531)

2.012 (0.513)

1.989 (0.998)

0.427 (0.309)

1.148 (1.153)

School

-1.945 (-0.973)

14.149 (0.959)

0.256 (0.042)

-0.813 (-0.246)

-3.648* (-1.684)

Agric

-0.677 (-0.663)

0.793 (0.185)

0.776 (0.427)

-10.232** (-2.035)

-0.732 (-0.499)

Food

0.192 (0.233)

0.015 (0.006)

-0.845 (-0.705)

9.220** (1.979)

0.537 (0.429)

Fuel

0.291*** (2.859)

0.038 (0.061)

-0.060 (-0.168)

0.162 (1.228)

0.218** (1.985)

Mach

-0.029 (-0.071)

-0.358 (-0.178)

0.610 (0.704)

-0.069 (-0.073)

-0.545 (-1.047)

Textil

0.115 (0.215)

-2.548 (-0.632)

2.227*** (2.594)

-0.639 (-0.650)

0.045 (0.076)

Transp

0.276 (1.044)

0.617 (0.599)

-0.111 (0.173)

-0.013 (-0.022)

0.285 (0.788)

SteelChem

-0.653 (-1.311)

-1.237 (-0.466)

2.759 (0.940)

-0.591 (-0.945)

-0.657 (-1.348)

J-Statistic

0.277

0.053

5.138

1.357

0.218

AR(1) Test

-3.969

-0.982

-1.394

-2.264

-3.611

(p-value)

(0.001)

(0.326)

(0.163)

(0.024)

(0.003)

0.332

-0.619

1.021

0.015

0.845

(0.740)

(0.535)

(0.307)

(0.988)

(0.398)

AR(2) Test (p-value)

Observations 234 65 65 104 Notes: GMM refers to the dynamic generalized method of moments estimation.

All variables are in logarithm. T-statistics are reported in parentheses.

169

***, **, * indicate significance at 1%, 5%, and 10%.

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Table 5: GMM Estimation of Export Composition Effects on CEE Economic Growth (Raw Material Exports Only) (1)

(2)

(3)

(4)

(5)

1999-2016

1999-2003

2004-2008

2009-2016

2004-2016

Curact

0.152** (1.935)

0.010 (0.013)

0.628 (1.382)

0.118 (1.183)

0.126 (1.554)

Debt

-0.383 (-0.875)

-0.650 (-0.144)

-1.241 (-1.101)

0.578 (0.858)

0.003 (0.006)

Tconcen

0.522 (1.291)

3.374 (0.539)

0.355 (0.502)

1.712** (1.958)

0.509 (1.258)

FDI

-0.091 (-0.357)

4.760 (0.609)

-1.300 (-1.557)

-0.085 (-0.319)

-0.271 (-1.221)

Popgrow

0.197*** (2.544)

-0.112 (-0.147)

0.216 (0.966)

0.157 (1.365)

0.157* (1.684)

Capform

0.330 (0.361)

-0.662 (-0.107)

0.500 (0.234)

0.423 (0.319)

1.269 (1.322)

School

-1.660 (-0.868)

24.590 (0.811)

-1.963 (-0.398)

-0.404 (-0.129)

-3.168 (-1.493)

Agric

-0.481 (-0.463)

-0.056 (-0.011)

3.032 (1.461)

-12.015*** (-2.404)

-0.800 (-0.532)

Food

0.210 (0.247)

1.036 (0.214)

-0.955 (-0.636)

10.475** (2.270)

0.313 (0.259)

Fuel

0.274*** (3.147)

-0.239 (-0.167)

-0.201 (-0.848)

0.195* (1.780)

0.215*** (2.401)

J-Statistic

0.127

0.001

0.061

0.749

0.444

AR(1) Test

-4.325

-0.607

-1.013

-2.478

-4.046

(p-value)

(0.001)

(0.544)

(0.311)

(0.013)

(0.001)

0.308

-0.318

0.939

0.161

0.934

(0.758)

(0.750)

(0.348)

(0.872)

(0.350)

AR(2) Test (p-value)

Observations 234 65 65 104 Notes: GMM refers to the dynamic generalized method of moments estimation.

All variables are in logarithm. T-statistics are reported in parentheses. ***, **, * indicate significance at 1%, 5%, and 10%.

98

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Table 6: GMM Estimation of Export Composition Effects on CEE Economic Growth (Manufactured Exports Only) (1)

(2)

(3)

(4)

(5)

1999-2016

1999-2003

2004-2008

2009-2016

2004-2016

Curact

0.149* (1.804)

-0.003 (-0.005)

0.154 (0.355)

0.129 (1.229)

0.136 (1.580)

Debt

-0.149 (-0.328)

-1.051 (-0.431)

-1.569 (-1.408)

0.529 (0.843)

0.192 (0.405)

Tconcen

0.729* (1.681)

2.251 (0.643)

0.045 (0.111)

1.767* (1.686)

0.569 (1.366)

FDI

-0.028 (-0.094)

3.063 (0.975)

-0.714 (-0.601)

-0.058 (-0.215)

-0.159 (-0.629)

Popgrow

0.207*** (2.515)

0.052 (0.144)

0.129 (0.698)

0.191 (1.563)

0.171* (1.774)

Capform

0.895 (0.879)

2.237 (0.541)

1.859 (1.204)

1.216 (0.877)

1.455 (1.373)

School

-3.180 (-1.561)

14.478 (0.980)

-0.199 (-0.036)

-3.739 (-1.191)

-4.442** (-2.070)

Mach

-0.046 (-0.109)

-0.329 (-0.159)

0.405 (0.434)

-0.060 (-0.063)

-0.452 (-0.884)

Textil

-0.557 (-1.226)

-2.314 (-0.872)

2.454** (2.341)

-1.282 (-1.421)

-0.583 (-1.189)

Transp

0.230 (0.905)

0.853 (0.980)

-0.021 (-0.031)

-0.227 (-0.394)

0.131 (0.389)

SteelChem

-0.597 (-1.165)

-1.186 (-0.443)

2.730 (1.192)

-0.956 (-1.572)

-0.575 (-1.128)

J-Statistic

0.118

0.031

5.212

0.850

0.497

AR(1) Test

-4.547

-1.076

-1.271

-2.601

-3.969

(p-value)

(0.001)

(0.282)

(0.204)

(0.009)

(0.001)

0.141

-0.566

1.025

0.014

0.738

(0.888)

(0.571)

(0.305)

(0.989)

(0.460)

234

65

65

104

169

AR(2) Test (p-value) Observations

Notes: GMM refers to the dynamic generalized method of moments estimation.

All variables are in logarithm. T-statistics are reported in parentheses. ***, **, * indicate significance at 1%, 5%, and 10%.

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Table 7: Summary Statistics Observation

Mean

Median

Standard Deviation

Maximum

Minimum

GDP growth

234

0.526

0.583

0.371

1.163

-1.068

Current account

234

2.517

2.637

0.461

3.407

1.058

Debt

234

3.531

3.592

0.309

4.033

2.566

Trade concentration

234

0.908

0.896

0.144

1.474

0.614

FDI

234

2.689

2.665

0.841

4.655

0.682

Population growth

234

1.528

1.691

0.573

0.455

-1.621

Capital formation

234

3.362

3.357

0.082

3.594

3.068

School enrollment

234

3.989

3.991

0.031

4.077

3.894

Agricultural export

234

2.614

2.625

0.286

3.179

2.019

Food export

234

2.483

2.527

0.273

3.110

1.917

Fuel export

234

2.293

2.326

0.543

3.232

-1.658

Machinery export

234

3.067

3.127

0.433

3.721

1.572

Textile export

234

2.276

2.358

0.435

2.918

0.660

Transportation equipment export

234

2.740

2.789

0.581

3.721

1.014

Steel and chemical export

234

2.620

2.657

0.286

3.171

1.823

Research and development

234

1.867

1.863

0.228

2.416

1.334

Inflation

234

2.241

2.455

0.815

3.694

0.001

Variable

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TANG, D.  HAVE EXPORT COMPOSITIONS INFLUENCED ECONOMIC GROWTH OF THE EUROPEAN UNION COUNTRIES IN CENTRAL AND EASTERN EUROPE?

DA LI JE STRUKTURA IZVOZA UTICALA NA EKONOMSKI RAST ZEMALJA EVROPSKE UNIJE U CENTRALNOJ I ISTOČNOJ EVROPI?

Rezime: Ova studija analizira uticaj izvoza na ekonomski rast od 1999. do 2016. godine u zemljama centralne i istočne Evrope koje su članice Evropske unije. Rezultati potvrđuju da je izvoz goriva i hrane podstakao rast nakon pridruživanja Evropskoj Uniji. Kao što se očekivalo, izvoz poljoprivrednih proizvoda nema uticaja na rast. Zbog različitih komparativnih prednosti, zemlje centralne i istočne Evrope su se još uvek oslanjale na izvoz nekih sirovina dok su radile na povećanju izvoza gotovih proizvoda. Rezultati takođe pokazuju da je izvoz transportne opreme, tekstila, čelika i hemijskih proizvoda ubrzao rast. To se može pripisati obimu proizvodnje s obzirom na pristup mnogo većem tržištu u okviru Evropske Unije.

Ključne reči: struktura izvoza, ekonomski rast, ekonomska integracija.

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EJAE 2020, 17(1): 104 - 112 ISSN 2406-2588 UDK: 336.652:331.56(4-191.2+4-11) 364.652:34 DOI: 10.5937/EJAE17-23673 Original paper/Originalni naučni rad

DOES UNEMPLOYMENT LEAD TO CRIMINAL ACTIVITIES? AN EMPIRICAL ANALYSIS OF CEE ECONOMIES Nemanja Lojanica*, Saša Obradović Department of Macroeconomics, Faculty of Economics, University of Kragujevac, Serbia

Abstract: This paper attempts to shed light on the linkages between criminal behavior and unemployment, with special reference to Central and East European countries (CEE). The adequacy of this paper relies on the premise that an econometric modeling of the relation between these variables is very important for the explanation of economic and social growth. Our result is in line with the premise that crime goes up when unemployment rises. These findings suggest that, to combat crime, all strategies oriented to mitigate unemployment should be investigated.

Furthermore, in order to successfully mitigate crime, governmental authorities in the CEE economies need to achieve macroeconomic stability.

Article info: Received: October 19, 2019 Correction: December 19, 2019 Accepted: December 19, 2019 Keywords: unemployment rate, crime, panel data econometrics, Eastern and Central Europe.

INTRODUCTION Criminal activities are a source of instability and insecurity in each national economy. This phenomenon, which is as old as a society itself and has global proportions, causes monetary costs and psychological consequences. Each country has a classification of illegal activities that are prohibited and sanctioned. Manners, definitions, and consequent sanctions of illegal activities differ among the countries. Availability of the data on crime represents a huge problem. The theory of economics treats crime market as an alternative to the labor market, and each individual’s choice between these markets depends on personal cost-benefit analysis.

It is extremely significant for economic policy makers to determine the links between macroeconomic indicators and crime. Since unemployment is a key macroeconomic indicator and directly related to 104

*E-mail: nemanjalojanica@yahoo.com

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the labor market, it is essential to determine the relations between unemployment and the crime rate in order to deepen the understanding of the economic determinants of criminal activities. Taking into consideration the importance of this issue, this study aims to examine the impact of unemployment on crime rates in the selected CEE countries. The social welfare system in these countries was disassembled in the shift toward democracy (Pridemore et al., 2007). The choice of these countries relies on the fact that the transition process in most of them resulted in conflicts, turmoil, and an increase in crime rate. However, some issues, such as a higher crime rate, have still remained. The CEE countries experienced domestic and transnational crime. (Gruszczyńska, 2004). In order to examine the relevant aspects of the relations between unemployment and crime, panel data analysis was used as an appropriate approach in this study.

Within this framework, an appropriate test of co-integration was selected in order to examine potential relations. The contribution of this paper is twofold. First, there are only a few studies which examine the crime problem on the sample of CEE countries. Second, there is no study which tested empirical regularity on the long-term relationship between crime and unemployment on the sample of CEE countries.

The paper is organized in the following way: it begins with a review of studies on the topic of the relation between unemployment and crime rates. This segment of the paper emphasizes controversies and differences in theoretical and empirical approaches to this problem. The third part begins with the presentation of panel tests of co-integration; the relations between the two variables are determined based on the results of the appropriate test of elasticity. The fourth part of the study reveals the main results, while the conclusion is dedicated to discussions concerning the obtained results.

LITERATURE REVIEW The issue concerning the relations between macroeconomic indicators and crime rate has been a question of numerous debates. Pioneer initiatives in this field were undertaken by Becker (1968) and Ehrlich (1973). They opened the question of socioeconomic indicators that have an influence on crime rate, focusing primarily on poverty, social exclusion, incomes, income inequality, educational level, and unemployment. The relation between crime and economic performance from any angle cannot be overemphasized (Estrada and Ndoma, 2016). The previous studies in this field have most often tried to determine the relations between unemployment and crime rate. The main reason for the growing interest in this topic is that unemployment has profound effects on all spheres of society. Lee (2016) pointed out that the relation between unemployment and crime is equivocal. The nature of this relation is quite complex (Rosenfeld and Messner, 2013).

Pridemore (2005) employed socioeconomic data to examine the effects of social structure characteristics on the homicide rate in the Russian regions. Results revealed that poverty was positively associated with regional crime rates. Similarly, Kim and Pridemore (2005) suggested that high crime rate is associated with negative socioeconomic changes. By exploring crime trends across European countries, McCall and Brauer (2014) have shown that, in the short-run, positive changes in welfare spending are related with reductions in crime.

Macroeconomic models that examine criminal activities forecast that an increase in unemployment rates reduces opportunity costs of crime, while simultaneously increasing criminal activities. However, some empirical studies did not confirm this hypothesis. Namely, Mustard (2010) emphasizes that, after five decades of research dedicated to the links between labor market and crime, the primary problem is the gap that exists between theoretical and empirical results.

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The assumption that improvements in the labor market reduce crime seems economically justifiable, but empirical studies did not prove this in some cases. The differences in the results vary from a low impact of unemployment on crime rate, through statistical insignificance of the obtained results, to higher influence of some other macroeconomic indicators. For example, Gould et al. (2002) emphasize that income and unemployment are associated with crime rate, but that the income rate has a stronger influence still.

According to a study conducted for all Italian provinces, there is a positive causality between the unemployment rate and crime rate (Speziale, 2014). This complies with the results showing that there is a positive link between economic indicators, including unemployment rates, inflation rates, and inequality, as well as crime rates (Cheong and Wu, 2015). This is not a coincidence, because the unemployment rate has a major role in explaining crime rate, according to some authors (Justus and Kassouf 2013).The majority of recently conducted empirical studies with panel samples have confirmed the hypothesis about the statistical significance of the correlations between labor market and crime (Papps and Winkelmann 2000; Cerro and Meloni, 2000; Edmark, 2003; Baharom and Habibullah, 2008; Almen and Nordin, 2011). The selected studies have had different econometric approaches. Furthermore, some studies have observed crime in aggregate and disaggregate manners.

Unemployment duration is usually mentioned as one of the main reasons for involvement in criminal activities (Bindler, 2014). The author tried to examine the relations between the conditions on the labor market and crime rate in the context of long-term unemployment in the USA. Using the quasi-experimental analysis of variations in unemployment and duration of unemployment benefits, the relations between higher crime rates and higher unemployment rates were confirmed. Longer-time periods of unemployment lead to the devaluation of human capital, and increases inclination towards criminal activities. Of all the studies conducted in this field, it is important to mention the following: Mocan and Unel (2011), Long and Polito (2014) and Bell et al. (2014). Mocan and Unel (2011) used the panel sample to determine variations in incomes of unqualified workers in the USA and their influence on their involvement in criminal activities. Long and Polito (2014) investigated individual motivations for finding a job in the presence of random criminal opportunities. These opportunities reduce moral hazard, since individuals sometimes commit a crime before they start searching for a job.

According to Bell et al. (2014), recession leads to short-term job loss, which results in income reduction. There is growing evidence that workers who join labor markets during economic crises face the inability to find a job fast and easily, which eventually has a negative impact on their earnings and further career development. Young people who finish school during a recession are more likely to engage in criminal activities than those who graduate in floating-market conditions. These effects are long-lasting and significant. Economic factors, therefore, play one of the crucial roles in determining crime rate levels, both when crime is observed aggregately and when it is disaggregated into separate components. Levitt (2001) notes that time-series analyses are too crude a tool for testing the connection between unemployment and crime and, consequently, the author gives priority to the panel sample analysis. CEE countries dealt with increased immigration during ‘90s and ‘00s, which might be an underlying factor for the crime rate increase (Altindag, 2012). This paper will mention some of the most relevant studies that examined the relation between macroeconomic variables and crime rates in separate countries using time-series analysis, and focusing on their results and the trends of the relation. The sequence of operations in such studies consists of finding the order of integration of variable and then determining potential co-integration and causality. Numerous studies have shown that there is a long-term relation between macroeconomic variables and crime rates, and that there is causality 106

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which goes from macro variables towards crime (Luiz, 2001; Narayan and Smyth, 2004; Gillani et al., 2009; Teranda and Clement, 2014). On the other hand, recent literature in this field has also presented quite contrary conclusions of no long-term relation between the variables (Janko and Pople, 2015). The time-series analyses have used unemployment, poverty, and inflation as macroeconomic variables, as well as instruments of monetary and fiscal policy (Teles, 2004).

DATA AND METHODOLOGY The primary objective of this paper is to examine a potential influence that unemployment may have on crime. The paper uses the unemployment rate and crime rate as variables of interest. The countries included in this study are: Czech Republic, Poland, Slovakia, Slovenia, Latvia, Lithuania, Bulgaria, Romania, Croatia, Hungary, and Estonia. The selected time period ranges from 1995 until 2015. The data are taken from WDI 2018 Database. As an indicator of unemployment, the data about the number of unemployed in the total labor force has been used (UN). As an indicator of criminal activities, we have used the intentional homicides (CR) – registered number of homicides. The values of these indicators are presented as logarithms (ln). Panel dataset is available publicly at Mendeley Data repository (https:// data.mendeley.com/datasets/7tptnz7ydz/1). For statistical analysis, we used STATA13 software package.

The econometric model is specified as the following:

(1) where i=1,2, …, N is the index of the country, t = 1,2, ..., T is the index of the temporal dimension, ß1 indicate the long-term effects of the independent on the dependent variable. θi is country-specific fixed effects, while δit is deterministic time trends specific for the country, while εit is error term. In our study, we have presented the period of 21 observations in 11 countries and the total number of observations is 231. Table 1 shows the descriptive statistics for variables during the mentioned period of time: Table 1 – Descriptive Statistics Results Country

Mean (CR)

Std. Dev (CR)

JB (CR)

Mean (UN)

Std. Dev (UN)

J-B (UN)

Czech Republic

131.62

36.28

1.89

338169.1

77645.36

1.57

Poland

690.71

432.77

18.49

2211310

712277.6

2.16

Slovakia

100.81

29.09

1.34

377171.8

77574.99

1.41

Slovenia

22.81

10.26

1.97

69791.76

15229.18

1.70

Latvia

156.43

82.01

2.15

147750.2

48088.62

0.59

Lithuania

297.33

87.72

0.35

199279.3

69142.57

1.16

Bulgaria

243.28

117.59

2.17

423289.8

131451.9

0.48

Romania

491

137.73

1.00

700883.3

98533.2

6.09

Croatia

79.67

34.14

2.55

254953.8

48226.9

1.38

Hungary

202.14

55.78

1.73

349128.9

83393.84

1.38

Estonia

116.10

58.35

1.55

67575.14

20494.95

0.18

Notes: Jarque and Bera (1980)

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Before applying specific unit root test, it is very important to specify cross-sectional dependencies.

The Pesaran (2004) CD test for cross-equation correlation is used for testing cross-sectional dependence.

The result of the cross-sectional independence test is reported in Table 2. The probability value is under 5%, so the effect is statistically significant. In this panel data model, disturbances are cross- sectionally dependent. In that sense, the second-generation panel unit root test should be used. These tests imply that there is a correlation between individual units of panel. Since the existence of the correlation between cross-sectional data has been already shown, this study applies the test developed by Pesaran (2007).

Table 2 – Pesaran (2004) CD Test Results H0: No cross-section dependence (correlation) in residuals

Statistic

Probability

Pesaran CD test

23.64

0.000

In order to determine a long-term relationship between variables, the Westerlund (2007) test will be used. Based on the error correction model (ECM), this test implies 4 panel co-integration tests (Ga, Gt, Pa and Pt). These four test statistics are normally distributed, and based on structural dynamics, rather than residual dynamics. The Westerlund (2007) co-integration test is appropriate for small samples and it is possible to get reliable results. Moreover, this test has a power relative to other popular residual-based panel co-integration tests. The null hypothesis is tested by determining whether error correction is present for individual panel members and for the panel as a whole. If the null of no cointegration is rejected, then co-integration between the variables exists. Taking into account that all the variables are stationary after conversion into the first difference, co-integration test assumes the following data generating process:

(2) Where

holds the deterministic components,

of parameters, while

is the speed adjustment term. If

represents the associated vector then co-integration exists, while if

, there is no co-integration. After testing co-integration, evaluation of the long-run parameters is carried out with the help of the panel Dynamic Ordinary Least Square (DOLS) developed by Pedroni (2001). This approach allows greater flexibility in the case of presence of heterogeneous co-integration vectors. Panel Dynamic OLS model can be represented as follows:

(3) where

represents the coefficients of the lead and lag differences, which accounts for possible se-

rial correlation and endogeneity of the regressor(s), while is the number of lags and leads. DOLS generates unbiased estimates for co-integrating variables, even with endogenous regressors, which is a very important feature of this procedure.

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EMPIRICAL RESULTS Table 3 presents the results of the panel unit root test for the variables. Following the stationarity test, the null hypothesis cannot be rejected. PESCADF test starts from H0: Variable is non stationary (has unit root). After conversion into the first difference, both variables became stationary. The null hypothesis is rejected at 1% level of significance.

Table 3 – Results of the Panel Unit Root Test Series

PESCADF (constant & trend) Level

ln(CR)it

ln(UN)it

First difference

t-bar test

cv5

cv1

t-bar test

cv5

cv1

-2.510

-2.760

-2.960

-3.636

-2.260

-2.470

-2.534

-2.760

-2.960

-3.287

-2.260

-2.470

Notes: cv5 and cv1 are critical value at 5 and 1%, respectively.

Therefore, we can conclude that the order of integration for both variables is I (1). In order to examine cointegration between the variables, the Westerlund (2007) test is used. Taking into consideration that the main focus of this paper is to examine the economic determinants of criminal activities, this paper presents only the case when ln(CR)it is a dependent variable. In Table 4, the null hypothesis about the non-existence of co-integration is rejected. In the specification when ln(CR) is considered as a dependent variable, 4 among 4 statistics of Westerlund (2007) were found to be statistically significant.

In accordance with the aforementioned, it can be noted that variables are co-integrated.

Table 4 – Results of the Panel Co-integration Test Westerlund (2007) ECM co-integration test

Null hypothesis: No co-integration

Test statistics

Value

Z- value

Probability

Gt

-3.714

-5.500

0.00*

Ga

-15.284

-1.596

0.05**

Pt

-11.654

-5.314

0.00*

Pa

-15.027

-3.296

0.00*

Notes: *and ** refer to 1 % and 5 % of the test significance.

For the evaluation of the long-term effects of unemployment on crime rate, i.e., the long-term elasticity coefficient, the Dynamic Ordinary Least Square method was used. This study examines the case when lnCRit is a dependent variable. The findings reveal that there is a positive and statistically significant relation between the variables, which is in accordance with the hypothesis of this study (Table 5). Such a result is consistent with the theoretical views that higher unemployment rates, in some way, compel individuals to engage in illegal activities. The coefficient of elasticity in relation to unemployment and crime rate equals 0.6-0.63. The interpretation of this result could be as the following: in the long run, an increase in the unemployment rate by 1% results in an increase in the crime rate of 0.6-0.63%.

Table 5 – DOLS Results Variables

Dependent Variable: Crime Pooled

unemployment 0.62 (3.06)* Notes: *Denotes the significant at 1% levels.

Weighted

Grouped

0.60 (3.29)*

0.63 (2.76)* 109

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CONCLUSION The goal of this study is to evaluate empirically the relationships between unemployment and the crime rate in the sample of 11 Eastern and Central European economies. Thus, it intends to fill the gap in the literature in this tradition. The aim of this research is important, because the countries chosen for this study share some characteristics, including changes in social structure and greater crime opportunities. This analysis does not differentiate between different categories of crime, but uses aggregated values of the variables. The appropriate test of dependence revealed that the unemployment rate has a positive impact on criminal activities, and that a 1% increase in the unemployment rate is related to a 0.6-0.63% increase in the crime rate. The key contribution of this paper is related to the fact that this is the first study that operates with the sample of CEE countries. Furthermore, this research can be a good starting point for future examining the relation between crime and unemployment in this part of Europe.

Policy implications of the results obtained in this manner refer to the fact that, in the selected countries, criminal activities can be decreased through a reduction of unemployment, i.e., through an improvement of the conditions on the labor market. Public policy against crime should include: more investment in human capital (education) and the reception of unemployment compensation. While the effect of unemployment benefits may be temporary, educational achievements should have permanent positive effects. In the context of future research, it was important to include additional variables in the analysis. First of all, appropriate indices of income inequality distribution must be included so that their impact and effect on crime can be examined.

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Bell, B. Bindler; A. & Machin, S. (2014). Crime Scars: Recessions and the Making of Career Criminals. CEP Discussion Paper 1284.

Bindler, A. (2014). Still Unemployed, What Next? Crime and Unemployment Duration. University College London Job Market Paper Cerro, A. & Meloni, O. (2000). Determinants Of The Crime Rate In Argentina During The '90s. Estudios de Economia, 27(2), 297-311.

Cheong, T. S. & Wu, Y. (2015). Crime rates and inequality: a study of crime in contemporary China. Journal of the Asia Pacific Economy, 20(2), 202-223. doi:10.1080/13547860.2014.964961.

Edmark, K. (2003). The Effects of Unemployment on Property Crime: Evidence from a Period of Unusually Large Swings in the Business Cycle. Working Papers 14.

Ehrlich, I. (1973). Participation in Illegitimate Activities: A Theoretical and Empirical Investigation. Journal of Political Economy, 81(3), 521-565. doi:10.1086/260058 Estrada, M. & Ndoma, I. (2016). Assessing the impact of crime on the economic performance: the case of central America. Quality and Quantity, 50, 1201-1211. doi: 10.1007/s11135-015-0198-9 Gillani, S. Rehman, H. & Gill, R. (2009). Unemployment, Poverty, Inflation and Crime Nexus: Cointegration and Causality Analysis of Pakistan. Pakistan Economic and Social Review, 47(1), 79-98.

Gould, E. Weinberg, B. & Mustard, D. (2002). Crime rates and local labor market opportunities in the United States: 1979-1997. The Review of Economics and Statistics, 84(1): 45-61. doi:10.1162/003465302317331919 110

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Gruszczyńska, B. (2004). Crime in Central and Eastern European Countries in the Enlarged Europe. European Journal on Criminal Policy and Research, 10(2), 123-136. doi: 10.1007/s10610-004-3784-2 Im, K. Pesaran, H. & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115 (1), 53-74. doi:10.1016/s0304-4076(03)00092-7 Jarque, C., & Bera, A. (1980). Efficient test for normality, heteroskedasticity and serial independence of regression residuals. Economic Letters, 6(3), 255-259.

Janko, Z. & Popli, G. (2015). Examining the link between crima and unemployment: a time-series analysis for Canada. Applied Economics, 47(37), 4007-4019. doi:10.1080/00036846.2015.1023942 Justus, M. & Kassouf, A. (2013). A cointegration analysis of crime, economic activity, and police performance in Sao Paulo city. Journal of Applied Statistics, 40(10), 2087-2109. doi:10.1080/02664763.2013.804905 Kim, S-W. & Pridemore, W. (2005). Social support and homicide in transitional Russia. Journal of Criminal Justice, 33(6), 561–572. doi:10.1016/j.jcrimjus.2005.12.001 Lee, K. (2016). Unemployment and crime: the role of apprehension. European Journal of Law and Economics, doi:10.1007/s10657-016-9526-3 Levitt, S. (2001). Alternative Strategies for Identifying the Link Between Unemployment and Crime. Journal of Quantitative Criminology, 17(4), 377-390. doi:10.1023/a:1012541821386 Long, I. & Polito, V. (2014). Unemployment, Crime and Social Insurance. Working Papers E2014/9. Cardiff Economics. doi:10.2139/ssrn.2483912.

Luiz, J. (2001). Temporal Association, the Dynamics of Crime, and their Economic Determinants: A Time Series Econometric Model of South Africa. Social Indicators Research, 53(1), 33-61. doi:10.1023/a:1007192511126 McCall, P. & Brauer, J. (2014). Social welfare support and homicide: Longitudinal analyses of European countries from 1994 to 2010. Social Science Research, 48, 90–107. doi:10.1016/j.ssresearch.2014.05.009 Mocan, N. & Unel, B. (2011). Skill-Biased Technological Change, Earnings of Unskilled Workers, and Crime.

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Westerlund, J. 2007. ‘Testing for Error Correction in Panel Data’. Oxford Bulletin of Economics and Statistics 69(6): 709-748, doi:10.1111/j.1468-0084.2007.00477.x World Bank (2016). World Development Indicators. D.C. The World Bank Group.

ACKNOWLEDGEMENTS: This work was supported by the Ministry of Science, Education and Technological Development of Serbia under Grant [179015] 111

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DA LI STOPA NEZAPOSLENOSTI DOVODI DO KRIMINALNIH RADNJI? EMPIRISJKA ANALIZA EKONOMIJA CENTRALNE I ISTOČNE EVROPE (CEE)

Rezime: Ovaj rad pokušava osvetliti odnos između kriminogenog ponašanja i nezaposlenosti, sa posebnim osvrtom na zemlje u centralnom i istočnom delu Evrope. Rad se oslanja na pretpostavku da je ekonometrijsko modeliranje odnosa između ovih promenljivih veoma važno za objašnjenje ekonomskog i društvenog rasta. Naš rezultat je u skladu sa pretpostavkom da se zločin povećava kada raste nezaposlenost. Ovi rezultati sugerišu da se, u cilju borbe protiv kriminala, analiziraju sve strategije za ublažavanje nezaposlenosti. Nadalje, radi uspešnog smanjenja stope kriminala, vlade u zemljama centralne i istočne Evrope treba da rade na postizanju makroekonomske stabilnosti.

112

Ključne reči: stopa nezaposlenosti, kriminal, ekonometrija panel podataka, istočna i centralna Evropa.

EJAE 2020, 17(1): 113 - 127 ISSN 2406-2588 UDK: 339.56(519.3) 339.982(100) DOI: 10.5937/EJAE17-24702 Original paper/Originalni nauÄ?ni rad

AN ANALYSIS OF NORTH KOREAN TRADE AMID WARMING GLOBAL RELATIONS UTILIZING RCA, RSCA, AND TBI Aaron Rae Stephens*, Ramin Kasamanli Department of Business Administration and Accounting, Hartwick College, Oneonta, NY, USA

Abstract: Based on high profile meetings with South Korean and US leaders, North Korean denuclearization and global economic cooperation is within sight. This paper seeks to outline trade policy opportunities for North Korea given such breakthroughs. This research summarizes international trade in North Korea and identifies sectors of the economy that are poised to further develop the North Korean economy. Employing three measures of trade specialization – RCA, RSCA, and TBI, this paper analyzes time series trade data from 2011 to 2018 in order to reveal specialized export sectors in North Korea. Chinese and UN bans on exports have been particularly damaging to exports from North Korea based on 2018 data. North Korea has comparative advantages in several sectors: coal, fisheries, and apparel among other areas. Trade in North Korea would lead to rapid industrialization and development if the country opens its economy; a well-managed approach would serve to direct development in specific sectors without sacrificing others; moreover, this research advises which sectors can be used to develop North Korea in an early stage of openness.

Article info: Received: January 7, 2020 Correction: February 18, 2020 Accepted: February 20, 2020

Keywords: North Korea, international trade, RCA, RSCA, TBI.

INTRODUCTION North Korea remains one of the most secretive and least understood countries in the world today; hence, one of its common nicknames, the Hermit Kingdom. Despite the inherent challenges, scholars must find creative ways to study North Korea. Many studies break through those constraints and manage to publish meaningful research regarding North Korea. *E-mail: stephensa@hartwick.edu

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This paper reviews some of the distinguishing economic changes taking place inside North Korea through a literature review, and connects that to an econometric analysis of the licit economic trade that takes place between North Korea and other countries throughout the world. The results of this research are designed to provide a framework for future studies, policymakers, and trade practitioners interested in North Korea.

In spite of many complex sanctions blocking trade, licit trade does take place; moreover, understanding that trade should help policymakers to understand how the North Korean economy could develop better trade relations if it were to change course and further integrate into the international community. Considering North Korea has improved its relationship with the US and neighboring nations (Jo, 2019), it is now more appropriate than ever before to conduct this kind of study to prepare a framework for future studies regarding North Korean trade.

There has been a longstanding public display of disagreement between the global community and North Korea, especially regarding nuclear proliferation (BBC, 2017). This disagreement nearly boiled over in 2017 before rapid de-escalation in 2018 through a series of meetings, negotiations, and summits that began during the 2018 Winter Olympics in Pyeongchang, South Korea; however, South Korean agreements have outpaced US North Korean talks (Jo, 2019). The two Koreas have continued on a path towards peace and cooperation (The Japan Times, 2019; Song and Kang, 2019). South Korea is keenly interested in developing longstanding peace on the Peninsula; indeed, this could have lasting benefits to the South Korean economy with respect to regional stability and the financial markets alone (Dibooglu and Cevik, 2016), and perhaps more important, long-term cooperative economic growth (Kim, 2019; Song and Kang, 2019).

Reunification is frequently the subject of many papers studying North Korea (Grzelczyk, 2014); however, it seems that reunification is no longer the desire of most South Koreans (Ha and Jang, 2016).

Moreover, a two state solution with trade and economic cooperation at the center is more in line with the sentiments of South Korean’s today; thus, it is relevant to look at reunification through the lens of trade and economic cooperation.

Many South Korean entrepreneurs are eager to develop labor, mineral resources, and other business opportunities should the political landscape allow it (Kim, 2019, Song and Gang, 2019). The South Korean government, bureaucrats and entrepreneurs have struggled to speed up Korean economic growth; moreover, they see North Korea as an opportunity for future growth, with its cheaper labor and abundant raw materials (Kim, 2019; Song and Gang, 2019). Youth unemployment and other demographic issues have exacerbated the concerns of South Korean policymakers and business practitioners alike, highlighting the potential benefits of economic cooperation with North Korea (The Japan Times, 2019).

Understanding the trade situation in North Korea before deeper economic integration between the two Koreas should be an imperative before further integration. This paper provides an inaugural framework for future trade and economic cooperation between the two Koreas and the world.

This study is designed to map out the latest trade scenario and identify comparative trade advantages of North Korean goods; then provide important policy and practitioner implications in order to guide future trade in North Korea.

This research employs several methodologies to describe the international trade scenario for North Korea, including: top exports and imports, top import and export nations, and specialized sectors. This paper is divided into several sections; first, a literature review that is divided into four parts, including trade specialization, North Korean political economy, trade in North Korea, and inter-Korean cooperation.

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STEPHENS, A. R., KASAMANLI, R.  AN ANALYSIS OF NORTH KOREAN TRADE AMID WARMING GLOBAL RELATIONS UTILIZING RCA, RSCA, AND TBI

After the literature review, several trade measures are utilized to measure comparative advantage in order to identify strong economic sectors for trade, including the Balassa Index, the symmetrical revealed comparative advantage, and the trade balance index, also known as the Lafay Index. The methodology section describes each method and its utilization. Following the methodology is the analysis section, which reports on the results of the research with a discussion of the implications. Finally, the conclusion summarizes the contributions, touches on the limitations, and highlights possible future studies.

THEORETICAL UNDERPINNINGS AND NORTH KOREAN TRADE Multiple prominent theories explain trade specialization between nations: the absolute advantage theory by Adam Smith (1776), the comparative advantage theory proposed by David Ricardo (1817), the factor endowment theory defined by Heckscher-Ohlin (1925), and the competitive advantage theory articulated by Porter (1980). Each theory builds on a broader understanding of trade specialization and why countries conduct international trade. Absolute advantage theory argues that a country exports a product because it is simply the best producer in a particular sector; however, countries continue to trade products without a single country dominating all trade; moreover, many exports do not maintain clear absolute advantages. Absolute advantage theory lacks the ability to describe how countries that do not have an absolute advantage in a particular sector continue to specialize in such sectors.

Ricardo’s theory of comparative advantage theory utilizes opportunity costs to explain how a sector can export while not maintaining an absolute advantage. Heckscher-Ohlin (1925) built on Ricardo’s theory to explicate the details of how comparative advantage is created through factor endowment theory; moreover, differences in factors of production between countries lead to different opportunity costs, which lead to specialization in different sectors. For decades, comparative advantage theory was used to explain trade at a national level. Porter (1980) focused on industry-based factors that lead to export specialization within specific industries. Previous theories focused on country-level phenomena while Porter looked at industry-level phenomena to describe trade specialization. There are inherent advantages to utilizing each theoretical framework for describing trade specialization; however, it is notably challenging to examine North Korean firms at the industry-level. North Korean industries remain closed to outsiders; thus, out of reach for any firm-based study. Considering the constraints of studying North Korean firms, trade specialization must be examined at the country-level where data is attainable. Comparative advantage theory remains the basis of establishing trade specialization today, and three methods are utilized to measure it: RCA, RSCA, and TBI (Laursen, 2015; Lafay, 1990); the methods are further described in the methodology.

North Korea has been termed a theater state wherein politics are both performative and performed; moreover, many aspects of the economy play a role in those performances (Kwon and Chung, 2012; Winstanley-Chesters, 2018). When studying North Korea, it is imperative to understand its performance-based political economy (Kwon and Chung, 2012). Other scholars have found it necessary to comprehend the NK political economy in this manner in order to frame a theoretical understanding of its political economy, including Winstanley-Chesters (2018) and Connell (2019). According to Winstanley-Chesters (2018), North Korea is a resource-rich country with a great variety of minerals and metals; however, the exploitation and trade of those resources is highly guarded and closely managed by the autocratic government of North Korea in a theatric manner. Connell (2019) frames North Korean tourism as a political theater.

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Considering the corrupt and unusual context of North Korea’s political economy, it is necessary to utilize this unique theoretical underpinning of the theater state in order to understand its political and economic behaviors; indeed, much of what is done in North Korea is through the façade of socialism and saving international face.

Lately, the North Korean economy has experienced internal growth that has been driven by internal market forces, including entrepreneurialism compelled by women; this is in part because of economic failures from the mid-1990s (Jung et al., 2018). Marketization was noted by a 2008 survey of North Korean defectors living in South Korea; 69% of defectors claimed that more than half of their income was from private income (Haggard and Noland, 2010). Indeed, economic growth lately is in contrast to the fact that the North Korean economy shrank by as much as 30% from 1991 to 1996 (Jung and Dalton, 2006); furthermore, it is estimated that between 600,000 to one million people died during that period because of famine (Haggard and Noland, 2009). Marketization of North Korea is the result of necessity; moreover, food and necessities are bought and sold in a private market in order to meet demands that are not met through the government-owned sectors (Haggard and Noland, 2010).

Table 1: Top Nations Receiving Exports from North Korea Importers:

2012

2015

Exports

Percent

TOTAL

3,156,495

China

2,502,531

Zambia

2018

Exports

Percent

Exports

Percent

100%

3,059,350

79%

2,567,685

100%

293,932

100%

84%

213,208

73%

1,611

0%

13,919

0%

20,090

7%

Mozambique

3,462

0%

10,579

0%

9,212

3%

Pakistan

52,285

2%

45,730

1%

7,379

3%

Ghana

461

0%

-

0%

6,152

2%

India

137

0%

99,006

3%

4,844

2%

Burkina Faso

78

0%

37,196

1%

4,468

2%

Fiji

357

0%

9,612

0%

4,225

1%

Nigeria

34

0%

10,754

0%

3,932

1%

Export values are in US Dollar thousand.

Ironically, private enterprise is illegal; however, the government turns a blind eye to such practices and allows it out of necessity; furthermore, private enterprises are registered as state owned enterprises in order to maintain the façade (Lankov, 2017). Accordingly, international trade through private enterprise is also illegal; however, the government takes international trade more seriously (Haggard and Noland, 2010); trade is likely restricted in order to control trade in information and foreign currency.

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Table 2: Top Nations of Origin for Imports Entering North Korea Exporters:

2012

2015

2018

Imports

Percent

Imports

Percent

Imports

Percent

China

3,532,383

82%

2,942,917

85%

2,218,140

96%

Russia

58,428

1%

78,267

2%

32,083

1%

India

230,158

5%

110,902

3%

31,004

1%

Brazil

3,428

0%

2,482

0%

7,287

0%

Switzerland

2,602

0%

5,173

0%

3,295

0%

Germany

29,933

1%

8,172

0%

3,179

0%

Colombia Mozambique Hong Kong Peru

-

0%

0

0%

2,787

0%

969

0%

227

0%

2,075

0%

57,009

1%

4,988

0%

1,600

0%

562

0%

21,187

1%

1,521

0%

Import values are in US Dollar thousand.

Trade with North Korea is not without extreme moral hazard; to begin with, its governance is responsible for the political incarceration of over 140,000 citizens in Soviet-style concentration camps, the execution of those opposing its policies, including foreign citizens outside of North Korea, and the indoctrination of its citizens (Salam and Haag, 2018). North Korea is globally considered a rogue nation for violating global nuclear nonproliferation agreements, which is substantiated by not only developing nuclear weapons, but also developing the means to deliver those weapons (Department of Defense, 2019). Global condemnation has resulted in numerous economic sanctions designed to punish the regime and encourage its compliance with nuclear non-proliferation; nevertheless, the country remains defiant and international trade continues, albeit, disturbed.

Despite a multitude of UN Security Council sanctions, trade in North Korea does continue. The country has an extensive trade relationship with China, its closest political and economic partner. This is not surprising, given China’s proximity and history with NK; nevertheless, it is remarkable that in 2018, 96% of North Korean imports came from China alone. That is up from 2012, when only 82% of its imports came from China. North Korean exports for the year 2018 are more diversely directed throughout the world: 72% of exports go to China, while an additional 15% make it to five countries in Africa: 7% to Zambia, 3% to Mozambique, 2% to Ghana, 2% to Burkina Faso, and 1% to Nigeria. Pakistan received 3% and India received 2% of North Korea’s exports in 2018. According to the numbers, North Korea is slightly less dependent on China for its exports, and more dependent for its imports. Despite its dependence on China, imports from China fell by 88% in 2018, as China is putting more pressure on North Korea (Nagai, 2019).

Although Russia and North Korea are geopolitically aligned and share a border, trade between the two nations remains weak at $34 million in 2018 (Snyder, 2019; Zakharova, 2016); nevertheless, Russia is North Korea’s second largest trading partner (Lukin and Zakharova, 2018). A trade relationship between the two has been considered academically and politically; however, there are reasons the two nations do not have better trade relations: one Russia’s border with NK is remotely populated and two financial sanctions make trade with North Korea difficult (Lukin and Zakharova, 2018). During the era of the Soviet Union, North Korea relied on Russian/Soviet imports to prop up its economy; after the fall of the Soviet Union, Russia focused on redeveloping its own economy, thereby neglecting North Korea (Zakharova, 2016). Perhaps geopolitical influences may change the Russia-North Korea trade relationship, but thus far it remains marginalized.

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Table 3: Top Exports Code

2012 Exports

2015 Percent

Exports

2018 Percent

Exports

Percent

72

158,004

5%

93,473

3%

35,274

12%

91

191

0%

43

0%

31,347

11%

84

107,294

3%

54,870

2%

26,270

9%

26

358,493

11%

204,743

7%

25,371

9%

67

707

0%

928

0%

24,178

8%

99

719

0%

75,824

2%

18,798

6%

90

9,619

0%

10,566

0%

13,154

4%

27

1,347,342

43%

1,160,136

38%

12,770

4%

39

35,585

1%

31,062

1%

10,909

4%

25

48,796

2%

47,207

2%

10,552

4%

Export values are in US Dollar thousand.

Exports by sector for 2018 remain more diverse: sector 72, iron and steel, made up 12% (mostly ferrous alloys), sector 91, clocks, watches, and parts thereof made up 11%, sector 84, (primarily items from code 8477: machinery for working rubber or plastics) made up 9%, sector 26, ores, slag, and ash, made up 9%, and sector 67, feathers and down, made up 8% of exports out of North Korea. Additionally, exports that do not fit into any particular sector made up 6 percent, while sectors 90 (primarily category 9023: instruments, apparatus, and models deigned for demonstrational purposes, e.g., in education), 27 (primarily coal), 39 (plastics going to China and Africa) and 25 (mostly graphite and magnesium carbonate) each made up 4% of exports (trademap.org). Sector 27, associated primarily with coal, was an important sector for North Korean exports; based on the data, 43% of their exports were coal-based exports in 2012. Coal imports from NK were halted by China in February 2017 in order to put greater pressure on North Korea (Denyer, 2017).

Table 4: Top Imports Code

2012

2015

Imports

Percent

Imports

Percent

Imports

Percent

39

142,036

3%

175,047

5%

221,167

10%

15

81,213

2%

106,651

3%

159,988

7%

54

129,980

3%

152,043

4%

138,384

6%

31

69,999

2%

19,673

1%

84,780

4%

8

16,164

0%

60,165

2%

82,499

4%

60

49,509

1%

75,453

2%

80,649

3%

61

66,428

2%

98,401

3%

74,670

3%

3

48,804

1%

102,988

3%

71,943

3%

24

66,328

2%

41,458

1%

71,359

3%

11

73,633

2%

21,142

1%

68,279

3%

Import values are in US Dollar thousand.

118

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South Korean investors are eager to develop better trade relations with North Korea to capture opportunities (Kim, 2019). The South Korean economy shrank for the first quarter of 2019, putting pressure on the current president Jae In Moon to develop better economic prospects for his constituents (Song and Kang, 2019; Kim, 2019 ). Moon has been relatively soft on North Korea in an effort to develop better economic ties and even open the North Korean economy to South Korean business interests (Song and Kang, 2019). North Korean economic prospects are appealing to South Korean businesses; however, they are also quite risky; investments have proven to be risky to many businesses, even turning them away to future ventures (Kim, 2019).

The Kaesong Industrial Complex (KIC) is a well-known inter-Korea cooperative industrial complex that has gained international attention primarily because of its conflicting position as both an enabling agent of the North Korean regime and as a means of coaxing the regime out of isolation and towards inter-Korean cooperation. Given the geopolitical and economic significance of Kaesong, it is important to weigh its position in North Korean trade. According to data collected by Manyin and Nanto (2010), the KIC at its peak employed 47,000 North Korean employees; hosted 121 Korean firms: 71 textile or apparel firms, 4 kitchen and utensil firms, 4 auto parts firms, 2 semi-conductor firms, and 1 printer toner firm among others. Sadly, the average monthly income for one North Korean worker in 2010 was only $35 a month after the regime took its share; furthermore, production in 2010 was around $323 million; thus, Kaesong exports amounted to 10% of all North Korean exports that year (Manyin and Nanto, 2010). Since 2010, the South Korean government scaled back punitively in retaliation for aggressions against the South.

METHODOLOGY Revealed comparative advantage (RCA) was first proposed to measure comparative advantage in Balassa (1965). The equation and methodology illustrated in Figure 1 is touted as an easy-to-apply method for measuring comparative advantage; however, it is significantly limited by a lack of mathematical symmetry with regard to results (Yeats, 1985; Vollrath, 1991); nevertheless, it remains a steadfast measure of export specialization (French, 2017). It is utilized by multiple global institutions to measure trade specialization including the United Nations and the OECD, among others (UNIDO, 1986; OECD, 2011). Hinloopen and Marrewijk, (2008) provide detailed guidance on measuring the level of comparative advantage afforded to an export sector while utilizing RCA; moreover, results indicate varying degrees of comparative advantage based on specified intervals: an RCA between 0-1 indicates no comparative advantage; 1-2 indicates a weak comparative advantage; 2-4 indicates a moderate advantage, and an RCA over four indicates a strong comparative advantage. Considering the simplicity of RCA, its ubiquity in trade specialization studies, and its ability to indicate varying levels of advantage, this study adopts the RCA as a methodology for analysis.

RCA

DPRK

æx = çç è

DPRK

i

x

DPRK

÷ö / çæ x ÷ø çè

WLD

i

x

WLD

÷ö ø÷

(1)

Despite years of persistent use, RCA is pointedly limited by mathematical symmetry (Yeats, 1985; Vollrath, 1991); thus, it is frequently coupled with RSCA, a mathematically symmetrical measure of RCA. With a symmetrical measure of trade specialization, it is possible to examine advantages and disadvantages on congruent terms; values above 0 are considered a comparative advantage, while values below 0 are considered comparative disadvantages; the bounds are limited to 1 and -1.

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RSCADPRK = ( RCADPRK - 1) / ( RCADPRK + 1)

(2)

Lafay Index Lafay (1990) describes the trade balance index (TBI) as an alternative methodology for measuring trade specialization utilizing import data, as well as export data. A noted deficiency of RCA and RSCA is the sole use of export data to establish trade specialization; ultimately, a better-rounded picture of trade specialization includes both imports and exports (Lafay, 1990); therefore, TBI has been adopted for another means of analysis.

(3) One study by Rossato et al. (2018) combined the three indices (RCA, RSCA and TBI) together in order to analyze a single export sector in multiple countries. Another study by Reyes 2014 used RCA and TBI to analyze multiple sectors among a group of countries. The combination of methodologies serves to corroborate and diversify the findings by using established methods and a variety of data points (export and import data); moreover, utilizing three methodologies creates a broader and more holistic image of trade specialization.

Due to increased international pressure to curb North Korean denuclearization, trade was greatly restricted through sanctions and bans from late 2017: i.e., bans on food exports, China’s ban on coal imports from North Korea, and the UN’s ban on textiles from North Korea; thus, data in 2018 cannot represent export specialization in North Korea. Indeed, data from many sectors was greatly distorted based on a review of 2018 exports and imports; thus, it was decided that, for this study, 2017 data would provide the best indication of trade specialization without the latest set of bans affecting the analysis. All countries are artificially affected by trade barriers similarly to North Korea, yet these trade methodologies are utilized to give valid results globally; thus, it is acceptable to use these methods with such limitations duly noted.

ANALYSIS Industries maintaining comparative advantages increased from 2011 to 2017; only 11 industries exhibited a comparative advantage in 2011, while 14 industries exhibited a comparative advantage in 2017. Furthermore, some sectors improved their comparative advantage, indicating positive economic changes in North Korea. The most remarkable comparative advantages and changes over time are noted in this analysis. Comments on each remarkable sector as evidenced by the data are linked back to literature in order to fully depict the situation in each sector. Finally, the analysis is brought full circle towards theory, such that discrepancies are considered for additional theoretical contributions.

A strong comparative advantage in fisheries (code 03) increased from an RCA of 4.6 in 2011 to 12.5 in 2017; moreover, a comparative advantage is corroborated in the other two indices. A comparative advantage in fisheries likely exists because fishing rights in North Korea were sold to Chinese fishing companies in order to make up for insufficient North Korean capital (primarily ships and equipment) 120

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(Ma, 2017; Shimbun, 2019). Unfortunately, such exports likely do not come back to benefit the average North Korean citizen; the fishing rights were granted by the state, and the income is retained by the state for the benefit of its autocracy (Shimbun, 2019).

Some food stuffs displayed a comparative advantage, which is unexpected considering frequent news coverage of famine in North Korea. Nuts (code 8) held a weak comparative advantage (RCA 1.6) in 2011, but improved to a strong comparative advantage (RCA 5.9) by 2017; according to data at trademaps.org, most nuts were sent to China. The quantity of nut exports peaked in 2014, but North Korea gained a greater comparative advantage through 2017. Other foods (code 7 and 12) exhibited weak comparative advantages in 2017 (RCA 1.6 and 1.3 respectively). Food exports from North Korea were banned December 22, 2017 by UN resolution 2397 (UN.org); thus, data collected in 2018 would be unlikely to reveal any natural comparative advantages.

Table 5: Results of the Analysis Code

2011

2014

2017

BI

RSCA

TBI

BI

RSCA

TBI

BI

RSCA

TBI

3

4.653

0.646

7.652

7.282

0.759

11.392

12.465

0.851

24.601

7

0.237

-0.617

-0.222

0.914

-0.045

0.589

1.604

0.232

2.192

8

1.665

0.250

2.786

5.842

0.708

11.152

5.988

0.714

10.065

12

0.786

-0.120

-0.745

0.803

-0.109

0.754

1.266

0.117

2.909

25

8.821

0.796

7.708

5.774

0.705

4.910

9.752

0.814

9.897

26

8.338

0.786

46.063

8.233

0.783

45.458

8.393

0.787

43.510

27

2.102

0.355

79.972

2.207

0.376

147.718

1.988

0.331

83.524

36

0.000

-1.000

-0.001

0.281

-0.562

0.033

0.524

-0.312

0.038

46

0.796

-0.114

0.053

0.352

-0.479

0.020

0.525

-0.311

0.021

49

0.129

-0.771

0.116

0.298

-0.540

0.319

0.123

-0.780

0.097

50

2.313

0.396

0.165

2.129

0.361

0.132

0.919

-0.042

0.050

61

1.514

0.204

1.743

2.793

0.473

3.287

2.636

0.450

0.287

62

10.982

0.833

58.637

15.311

0.877

88.762

19.887

0.904

113.064

67

0.544

-0.296

-0.091

0.467

-0.363

-0.006

10.494

0.826

0.975

71

0.027

-0.947

0.453

0.319

-0.517

5.039

0.013

-0.974

0.118

72

2.312

0.396

16.799

1.808

0.288

5.791

1.972

0.327

7.845

74

0.200

-0.666

-0.599

0.626

-0.230

1.791

0.063

-0.881

-1.084

75

0.313

-0.523

0.177

0.113

-0.797

0.082

0.020

-0.961

0.004

78

1.310

0.134

0.217

1.660

0.248

-0.806

3.190

0.523

0.293

79

22.605

0.915

7.728

13.963

0.866

5.543

1.095

0.045

0.537

80

0.006

-0.989

-0.386

0.000

-1.000

-6.820

10.906

0.832

0.791

89

0.504

-0.330

0.415

0.174

-0.704

-1.057

0.658

-0.206

2.331

92

0.899

-0.053

-0.122

1.633

0.240

-0.012

1.757

0.275

0.031

93

0.046

-0.913

0.018

0.000

-0.999

-0.016

0.040

-0.923

0.013

97

0.183

-0.690

0.026

0.006

-0.988

0.002

0.001

-0.999

0.000

121

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Textile and apparel sectors (codes 61, 62, and 67) also experienced significant and steady increases in comparative advantage from 2011 to 2017, with exports reaching $752 million in 2016 (Reuters or Korea Trade-Investment Promotion Agency). Sector 61, knitted and crocheted apparel, exhibited a weak comparative advantage in 2011, but increased to a moderate advantage (RCA 2.6) by 2017.

Sector 62, non-knitted apparel, was already strong in 2011 (RCA 10.9), but increased (RCA 19.88) by 2017. Sector 67, feathers and feather stuffing, increased from no comparative advantage in 2011 to a strong comparative advantage (RCA 10.49) in 2017. Increased comparative advantages in these sectors are significant, because they exhibited the strongest comparative advantage in 2017 and remain the only industry-based area showing a significant increase from 2011 to 2017. This indicates the type of early industrial development that may guide North Korea into economic development. Low wages and skilled workforce in the apparel industry have enabled this industry’s export potential; moreover, exports of apparel industry products were also made possible due to the willingness of Chinese textile companies to subcontract orders from United States, Europe, Japan, South Korea, Canada, and Russia to North Korean textile factories, and subsequently deliver those orders with a “Made in China” label (Washington Post, 2017).

Between 2011 and 2017, the North Korean economy also exhibited steady comparative advantages in sector 26 (ore, slag, and ash) with an RCA of 8.3 in 2011, and an RCA of 8.3 in 2017 and 27 (mineral fuels, mineral oils, and products of their distillations) with an RCA of 2.1 in 2011 and an RCA of 1.9 in 2017. Most of the ore exports of North Korea is made up of iron ore exports, with revenue peaking at $415 million in 2013, then declining to $187 million in 2017 due to a reduction in global demand for iron ore. In 2013, North Korea surpassed Vietnam to become the world's number one exporter of anthracite coal, which is the highest ranked type of coal, generating $1.4 billion in revenue (Pavone and Sun, 2014). A comparative advantage was found in coal exports through 2017 before exports were completely halted in order to put greater pressure on North Korea (Denyer, 2017).

Some metals and products made of those metals maintained comparative advantages: steel (72), lead (78), tin (80) and zinc (79). Steel (code 72) was the largest export sector in 2018, and maintained a comparative advantage from 2011 (RCA 2.3) to 2017 (RCA 1.9). Lead (code 78) had a weak comparative advantage in 2011 (RCA 1.3) that increased to a moderate comparative advantage in 2017 (RCA 3.1). Tin (code 80) increased from no comparative advantage in 2011 (0.0) to a strong comparative advantage in 2017 (RCA 10.9). Zinc (code 79) lost its comparative advantage from 2011 (22.6) to 2017 (1.09). The other trade indices corroborate the comparative advantages indicated by RCA. The findings of this analysis are cross validated. The three trade indices generally corroborate each other throughout Table 5, with few exceptions. This validates the comparative advantages exhibited in each sector. This study is exceptional in its use of three trade indices across several sectors. Although theoretically the results should indicate uniform results, it is still remarkable that the indices uniformly comply with each other throughout this study in practice. Regarding the use of all three indices, future studies should utilize these three indices together in order to continue to theoretically test the validity of each index, and to confirm comparative advantages in each sector.

Policymakers and practitioners must carefully examine export specialization when making decisions regarding trade and trade policy. Trade practitioners can see the trends in this data to note the most specialized sectors in North Korea for future trade. Investments in specialized sectors tend to be durable, especially when the long-term trends indicate increasing specialization. Policymakers need to utilize the results of this study as well. Export specialization can indicate which sectors a government needs to protect, and which sectors need to be bolstered through policy.

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Moreover, exceptionally savvy policymakers will harness trends in order to boost economic development. As a benchmark, South Korea and Malaysia invested in apparel industries early in economic development before diversifying to other areas (Athukorala and Narayanan, 2018 and Ha-Brookshire and Lee, 2010). This model is appropriate given North Korea’s trade specialization in apparel. Dependence on natural resources may hinder economic development if not managed properly (Danish, Baloch, Mahmood and Zhang, 2019). Moreover, this research remains an important starting point for those interested in North Korea and trade. As a note of caution, comparative advantages are dynamic; with time, these advantages may diminish or strengthen. It will be necessary update this data and maintain current statistics. Policymakers and practitioners should be aware of this time limitation.

CONCLUSION North Korea is a notoriously problematic country to study; however, this study manages to break through those challenges to provide an evocative analysis of its trade scenario and identify specialized trade sectors; moreover, this research provides valuable evidence for practitioners and policymakers.

Trade practitioners are eager to trade with North Korea if the country opens its economy to the rest of the world; however, there remains little guidance on which sectors should be developed. This study provides guidance as to which sectors could prove to be most valuable for practitioners. Policymakers need to be able to make trade policies that protect and develop the country in the long-term; this research identifies specialized sectors that can lead to development and economic prosperity. Finally, this study distinctively uses three measures of comparative advantage to compare and contrast the results among the three indices; the results among the three indices remain remarkably stable, confirming the results of this study, and prescribing this method in future studies.

Multiple limitations remain for this study; firstly, North Korea’s heavily sanctioned economy revealed the complexity of analyzing its trade position. Based on 2018 data and analysis, North Korea’s trade specialization changed dramatically. It is likely that other sectors were distorted because of trade bans or sanctions before 2018; thus, the measured trade specialization likely does not represent how North Korea could be with no sanctions or bans. Indeed, most nations throughout the world have some level of trade distortion because of global trade policies; however, North Korea remains exceptional because of its isolationism. Academic contributions to the methods employed remain limited to a single country’s data; additional data across many nations may find similar or contradictory results.

Future studies regarding North Korea would likely have mixed results regarding trade specialization depending on sanctions and trade bans. Many sectors were banned or heavily sanctioned in late 2017. If those bans continue, those sectors will likely become distorted; future studies can map those distortions. If the bans and sanctions are lifted it is likely that the results of this study would be corroborated by future studies. Given the dynamic nature of trade in North Korea it will be important to keep abreast to changes in policies. Studies measuring trade specialization in the future should consider this threefold method. It is possible that discrepancies could be more visible in other economies; moreover, this method and its results should be further tested across many nations.

Comparative advantages are dynamic. As the North Korean economy changes, comparative advantages will also change; thus, this research is time sensitive. It will be necessary to continuously review this data in order to monitor changes in comparative advantages. Some industries change more readily than others. It is likely that some comparative advantages will remain longer-term while others will dissipate more quickly. There is a continuous demand for this type of research.

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ANALIZA SEVERNOKOREJSKOG TRGOVANJA U USLOVIMA ZAGREJANIH ODNOSA U SVETU – UZ UPOTREBU RCA, RSCA I TBI INDEKSA

Rezime: Na osnovu sastanaka na visokom nivou sa južnokorejskim i američkim liderima, denuklearizacija Severne Koreje i globalna ekonomska saradnja se čini dostižnom. Imajući u vidu takve pomake, ovaj rad želi da naglasi mogućnosti u okviru trgovinske politike za Severnu Koreju.

Ovo istraživanje ukratko opisuje međunarodnu trgovinu u Severnoj Koreji i identifikuje ekonomske sektore koji su spremni da pospeše dalji razvoj ekonomije u Severnoj Koreji. Koristeći tri mere trgovinske specijalizacije - RCA, RSCA i TBI, ovaj rad analizira podatke o trgovini u vremenskom periodu od 2011. do 2018. Godine, kako bi se otkrili specijalizovani izvozni sektori u Severnoj Koreji. Zabrane na izvoz od strane Kine i zemalja članica Evropske Unije posebno štetno utiču na izvoz iz Severne Koreje na osnovu podataka za 2018. godinu. Severna Koreja ima komparativne prednosti u nekoliko sektora: ugalj, ribarstvo i odeća, između ostalog. Rast trgovine u Severnoj Koreji doveo bi do brze industrijalizacije i razvoja ako se zemlja otvori u oblasti ekonomije; dobro vođen pristup služio bi usmeravanju razvoja u određenim sektorima bez žrtvovanja drugih; Štaviše, ovo istraživanje savetuje koji sektori se mogu koristiti za razvoj Severne Koreje u ranoj fazi otvorenosti.

Ključne reči: Severna Koreja, međunarodno trgovanje, RCA, RSCA, TBI.

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EJAE 2020, 17(1): 128 - 145 ISSN 2406-2588 UDK: 338.48-44(497.11) 005.96:338.48 DOI: 10.5937/EJAE17-21424 Original paper/Originalni naučni rad

THE INFLUENCE OF HUMAN RESOURCES ON THE DEVELOPMENT OF LEADING TOURISM DESTINATIONS IN SERBIA Nikolina Kordić*, Snežana Milićević Faculty of Hotel Management and Toursim in Vrnjačka Banja, University of Kragujevac, Serbia

Abstract: The aim of this paper is to show that the number of tourism overnight stays and the number of beds are related with the scope and dynamics of employment in the hotel industry of Serbia. Serbia’s most developed destinations (in the field of city tourism Belgrade and Novi Sad, spa tourism - Vrnjačka Banja and Sokobanja, and mountain tourism Zlatibor and Kopaonik) were chosen as the subject of research. The first part of the paper discusses the connection between the study of tourist destinations, on the one hand, and human resources, on the other hand. In the second part of the paper, corresponding conclusions are drawn regarding the influence of human resources on the development of the leading tourist destinations in Serbia by using systematization and presentation of statistical data, by analyzing the quantification of the effect of employment on tourism overnight stays, based on regression analysis, using the analytic-synthetic and comparative methods.

Article info: Received: April 22, 2019 Correction: June 10, 2019 Accepted: August 24, 2019

Keywords: tourism destination employment, overnight stays, accommodation capacity, Serbia.

INTRODUCTION Tourism is one of the world’s leading industries. In 2017, 1,326 million international tourist arrivals took place. That same year, international tourism revenue amounted to 1,340 billion US dollars (UNWTO, 2018). In 2017, tourism participated in the world GDP (taking into account the direct, indirect and multiplied - induced effects) with 10%, and in total world exports with 6.5% (WTTC World, 2018).

In 2017, Serbia was visited by approximately 3.1 million tourists, of which 1.6 million were national, and 1.5 million international. From 2013 to 2017, an increase in the number of tourists, especially foreign tourists, and overnight stays was noted in this country (Republic Statistical Office of Serbia.

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*E-mail: nikolinakordic83@gmail.com

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Statistical Yearbook of Serbia, 2018). In 2017, tourism participated in the GDP of Serbia with 6.7% (taking into account the direct, indirect and multiplied - induced effects), and in total exports with 7.1% (WTTC World, 2018). Tourism is the world’s largest generator of jobs. In 2017, 118,454,000 jobs directly involved in tourism were registered (employees in hotels, restaurants, and other types of establishments in the hotel industry, in travel agencies, in entertainment and recreation facilities, in airlines and other types of traffic organizations engaged in passenger transport, with the exception of suburban traffic), which accounted for 3.8% of the total scope of global employment. Taking into account other jobs indirectly related to tourism, about 313,221,000 jobs were registered for the same year, which amounted to 9.9% of total world employment. Every 10th job in the world belonged to the tourism sector (WTTC World, 2018). In Serbia in 2017, 37,000 jobs directly involved in tourism were registered, which accounted for 1.9% of the total scope of employment. This was twice as low compared to the global average. That same year, the number of jobs indirectly linked to tourism amounted to 96,500, accounting for 4.9% of total employment in Serbia (WTTC Serbia, 2018) which was also twice as low as the global average. Every 20th job is directly or indirectly associated with the tourism industry.

Tourism services are processes involving a significant interaction between tourists and employees.

The temporary stay of tourists in a tourist destination imposes the provision of hotel services. Therefore, the hotel industry as the receptive element of tourist destinations is considered to be a key indicator of the degree of its development and the assessment of its quality. The hotel industry requires the involvement of a large number of employees. The largest number of employees in the tourism industry can be found precisely in the hotel industry. We therefore focus on the hotel industry employment as a representative part of the total number of employees in a tourist destination.

It is important for Serbian tourism that there be human resources who possess the knowledge and skills for the valuation of natural and anthropogenic attractions, which abound in Serbia. In light of this data, however, Serbia has lagged behind the global average. The data referring to human resources indicate that Serbia does not have the required scope of employment in this area. Therefore, the main objective of this paper is to consider in detail the interdependence of the scope of employment and the basic indicators of touristic development, such as the volume of tourist overnight stays and accommodation capacity. This is the initial step in considering the effect of human resources on the development of tourist destinations. In line with the need to point out the importance of human resources for the development of tourism in Serbia, research was conducted in destinations that are taken as an example and basis for analysis. These destinations (Belgrade, Novi Sad, Vrnjačka Banja, Sokobanja, Zlatibor, and Kopaonik) are representative, especially regarding tourism overnight stays and accommodation facilities. It is to be expected that leading tourism destinations have devoted extraordinary attention to employees in tourism sector, primarily in hotel industry.

LITERATURE REVIEW Today tourism has great social significance. Goeldner & Brent Ritchie (2009) emphasize that tourism will continue growing in the future under conditions of fundamental changes that will occur under the influence of high technology, which will require a greater need for human contact. Stasiak (2013) points out that hotels have long ceased to be the only elements of the tourist infrastructure, but have become places where a complex tourist experience is enjoyed, and that requires the appropriate personnel able to meet the expectations of guests.

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Kosar & Kordić (2018) point to the complexity of the hotel product and the wide range of components, i.e., services that meet the many and varied needs of consumers. The focus of the future study of tourism theory and practice will be the hotel product as the basis of the tourism product, i.e., the tourist destination product. Kosar & Rašeta (2005) state that the quality of the hotel product is largely determined by the quality of human resources, and argue that the quality of the hotel product is integrated into the quality of the tourist destination offer. The complexity and diversity of tourist destination products are reflected in different approaches to the conceptual definition of the tourist destination, which Milićević & Djordjević (2016) discuss. According to them, tourist destinations are places where all the content is subordinated to tourism and its development, as well as cities, large metropolises, and entire countries, where tourism is manifested as an aspect of development.

The tourist destination is, therefore, a starting point for the study of tourism. Saraniemi & Kylan (2011) insist on an innovative developmental concept of tourism that is based on the destination approach and that involves all stakeholders. Tourist consumers are important stakeholders. Therefore, it is particularly necessary to understand their consumer behavior, according to the Seddighe & Theocharous (2002), when selecting a tourist destination. Popesku (2011) believes that the destination approach to the study of tourism treats tourist destinations as complex systems. Key aspects of these studies refer to the competitiveness and sustainability of tourist destinations, with the focus on the analysis of the tourist experience as a starting basis for the consideration of competitiveness.

Addressing competitiveness of tourist destinations includes quantitative models, such as the Competitiveness Monitor (CM), created by the World Travel and Tourism Council (WTTC). Mazanec, Wober & Zins (2007) emphasize that this model is widely applicable due to the public availability of data and the possibility of result interpretation by independent research groups. The search for the most acceptable ways to assess the competitiveness of tourist destinations is reflected in the creation of models that contain different sets of factors. Ritchie & Crouch (2010) discuss the operation of these factors, primarily under the influence of so-called qualifying and amplifying determinants (location, safety and security, price and value, interdependence, awareness and image, carrying capacity) on the increase or decrease in the competitiveness and sustainability of tourist destinations.

Among the many and varied factors of tourist destination competitiveness, a special place belongs to human resources. Gruescu, Nanu & Tanasie (2009) point out that the tourism industry is based on people, and that human resources development should be the main preoccupation of tourism professionals. According to Vengesayi, Mavondo & Reisinger (2013), human capital strongly influences the development of a tourism destination. They believe that high quality human resources represent a relevant competitive advantage for tourism destination. Human resources management, according to Ahmmad (2017), implies the process of selection, recruitment of employees, monitoring their development through education, training, the improvement of skills, motivation, the provision of workplace security and safety, their well-being, the betterment of their health, and interpersonal relationships.

Brewster, Sparrow, Vernon & Houldsworth, (2011) suggest that differences in human resource management originate from the cultural and institutional environment of different countries, hence the need for flexibility and understanding of this area of management.

Given that people are carriers of organizational knowledge, human resource management manifests itself as an important factor in economic development. Petrović & Stanišić (2015) believe that knowledge as a vital developmental resource of economic progress has not been sufficiently exploited.

This has been shown by the research on the effect of the knowledge economy index (KEI) on the gross domestic product (GDP) in countries in transition.

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Emotional intelligence is an important feature of human resources. Pekaar, Linden, Bakker, & Born (2017) have demonstrated that different levels of the emotional intelligence of employees significantly affect the quality of their contact with clients in the service industry. Wolfe & Kim (2013) have presented the results of surveys conducted among hotel managers in a hotel company in the Midwestern United States, indicating that certain components of emotional intelligence (interpersonality, general mood, and stress management skills) are indicators of job satisfaction. Employee motivation is an essential component of job satisfaction. Sarajevo (2016) states that today managers are constantly engaged in employee motivation, especially in times of crisis.

The tourism sector offers wide and diverse employment opportunities. Unković (2017) points out that the demand for professionals in the field of economics and management in tourism is dominant thanks to a large number of tourist companies that operate rationally and respond to market demands.

However, Baum (2007) believes that traditional employment models still exist in developing countries, which slows down their tourism development. Similar problems are also identified in the hotel industry of Serbia, especially regarding the unsatisfactory qualification structure of employees, as well as low wages, as stated by Kordić & Milićević (2018).

Therefore, strategic human resource management is of great importance for the development of tourism and the hotel industry, as emphasized by Čerović (2013), where employee performance, motivation, and reward should be analyzed thoroughly. Given that the hotel industry is a service industry, and that it engages a substantial scope of employment, Hoque (2013) points out that the effect of human resource management on organizational and technological changes, innovative business strategies, and economic results in the industry should be subject to continuous analyses. The effect of human resources on tourist destination competitiveness is often emphasized in many scientific and professional circles. The research conducted to quantify the effect of human resources on tourist destination competitiveness, according to Milićević & Petrović (2018), shows a statistically significant relationship between the number of employees and gross earnings in tourism in the EU member countries and their competitiveness as tourist destinations.

EMPLOYMENT AS A FACTOR OF TOURISM DEVELOPMENT IN SERBIA Serbia is considered to be an attractive tourist destination whose potential has not been exploited sufficiently. In this sense, Serbia awaits a number of activities that, among other things, refer to sustainable business improvement, legal environment, business climate, innovativeness, brand strengthening, and reputation (Teodorović & Popesku, 2017). In accordance with the development goals of the Tourism Development Strategy of the Republic of Serbia for the period 2016-2025, changes in the structure of accommodation facility offers are envisaged in terms of increasing the orientation towards hotels.

Accordingly, an increase in the number of employees whose jobs are directly involved in the tourism sector is expected.

The importance of the realization of the objective related to employment should be noted, all the more so because, in the previous strategy, the actual number of employees was about one-third lower than expected (Ministry of Trade, Tourism, and Telecommunications, 2016).

The effect of human resources on the development of Serbia as a tourist destination has not been given adequate attention, as can be seen from Table 1.

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Table 1 presents the data pertaining to the world’s leading tourist destinations, as well as the Balkan countries and the region, in order to consider the situation closely and assess the prospects in Serbia.

It is evident that Serbia is lagging behind. Countries where tourism is a priority bearer of economic development, as reflected by the share in the total scope of employment, which is also expected in the future, are primarily Malta, Greece, and Croatia, with a share of over 10% directly employed in the tourism sector. At the same time, including employees whose jobs are indirectly related to tourism, the contribution of tourism exceeds 20% of the total employment in the mentioned countries. When it comes to the total (direct and indirect) contribution of tourism to the number of employees, Albania, Cyprus, Montenegro, Austria, Italy, and Spain stand out. Serbia is in second-to-last place when it comes to the direct share of employees in tourism in relation to the total number of employees, and when it comes to the overall contribution of tourism expressed in the number of direct and indirect jobs, it is in last place. It is envisaged that an increase of only 0.1% in direct jobs, and 0.3% in the total (direct and indirect) jobs of the tourism sector will happen by 2028, which will position Serbia in last place compared to the projections in the mentioned countries.

Table 1 - The share of employees in tourism in relation to the total scope of employment (in %) Territory

Share of directly employed

Share of directly and indirectly employed

2017 state

2028 projection

2017 state

2028 projection

World

3.8

4.2

9.9

11.6

Malta

15.7

20.1

28.3

35.4

Greece

12.2

13.3

24.8

28.5

Croatia

10.1

11.8

23.5

27.2

Albania

7.7

8.8

24.1

27.3

Montenegro

7.6

8.1

19.3

21.5

Germany

7.1

8.4

13.8

14.8

Cyprus

6.9

8.4

22.7

29.1

Austria

6.5

8.3

16.1

19.2

Italy

6.5

7.5

14.7

16.5

India

5.0

5.3

8.0

8.4

Spain

4.9

5.8

15.1

16.9

France

4.2

5.0

10.0

11.2

Slovenia

3.7

4.8

12.3

15.4

China

3.6

4.3

10.3

14.7

Hungary

3.5

4.1

7.3

8.0

USA

3.4

3.9

8.9

10.1

Bosnia and Herzegovina

3.2

4.4

11.2

15.2

Bulgaria

2.9

4.1

10.7

13.4

Romania

2.5

2.6

6.3

6.5

Serbia

1.9

2.0

4.9

5.2

Macedonia

1.6

1.8

6.1

7.0

Source: WTTC: TRAVEL & TOURISM ECONOMIC IMPACT 2018 World, TRAVEL & TOURISM ECONOMIC IMPACT 2018 Country-data, TRAVEL & TOURISM ECONOMIC IMPACT 2018 Serbia

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KORDIĆ, N., MILIĆEVIĆ, S.  THE INFLUENCE OF HUMAN RESOURCES ON THE DEVELOPMENT OF LEADING TOURISM DESTINATIONS IN SERBIA

METHODOLOGY The methodology applied in this paper is based on the analysis of statistical data from secondary sources. The state in the leading tourist destinations in Serbia was analyzed on the basis of a ten-year observation period (2008-2017). The number of overnight stays and the number of beds were used as indicators of tourist destination development. Accommodation capacity measured by the number of beds is the basic assumption of tourist arrivals and overnight stays. The fact is that there are destinations in Serbia that have a remarkable accommodation capacity, but they are not competitive. The reason for this is the technological obsolescence on the part of the accommodation offer, and therefore nonconformity with modern market demands. Therefore, the number of overnight stays may be considerably lower than the offered accommodation opportunities. The number of employees as a quantitative indicator represents the starting assumption of the growth dynamics of human resources. It is the basis for further qualitative research, which should be done in order to precisely define different forms of human resource impact on a tourism destination development. In this paper, by using the available data presented in tables, fluctuations in these indicators were analyzed. The percentage of individual destinations in the number of overnight stays, accommodation capacity, and the number of employees of broader territorial units were also determined to demonstrate their leading status. Based on the available data, we applied correlation analysis to determine the degree of reciprocity in the number of employees and the number of overnight stays. We used a linear regression model, which quantified the effect of the number of overnight stays on the number of employees. The regression analysis enabled the design of the fluctuation in the number of employees based on the fluctuation in the number of overnight stays. By looking at the data on number of employees, we can observe that a major change happened in the year 2015, which is a result of change in the way the workers are registered. In order to split the periods of different workers’ registration methodology, we included a dummy variables into the regression model. With the aim of avoiding the illogicality in the number of employees assuming the inactivity of the hotel industry, the regression intercept is set to zero.

We should point out certain limitations in the methodology application. The number of employees in the hotel industry of Serbia was analyzed on the basis of available data, which were disclosed in national publications in the economy sector under the name “Accommodation and Food Services.”

Employees in the tourist sector of Serbia, primarily in travel agencies, are not registered separately, so their number cannot be determined using official statistical sources. The lowest territorial level for statistical monitoring of the number of employees is a municipality. However, the tourist destinations Zlatibor and Kopaonik are not separate municipalities, so we used the number of employees in the municipality of Čajetina, to which Zlatibor belongs, and the number of employees in the municipality of Raška, to which Kopaonik belongs. The abovementioned destinations are dominant in the municipalities to which they belong, the number of employees in the hotel industry of Čajetina and Raška municipalities can essentially be treated as the number of employees on Zlatibor and Kopaonik. Based on the available data and the results of correlation and regression analysis, by applying the analyticsynthetic and comparative methods, we have drawn corresponding conclusions, which can serve as the starting point for taking certain steps in the field of human resources, in the context of increasing the competitiveness of the leading tourist destinations in Serbia.

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THE EFFECT OF HUMAN RESOURCES ON THE DEVELOPMENT OF LEADING CITY TOURISM DESTINATIONS IN SERBIA Belgrade and Novi Sad are the main carriers of city tourism development in Serbia. Their position is reflected in the biggest share of the number of overnight stays and accommodation capacity in relation to the country level, as can be seen in Table 2.

The fluctuation in the number of tourism overnight stays was turbulent in all three destinations during the observed period, where stable growth characteristics were expressed by the number of foreign overnight stays, which increased faster in Belgrade and Novi Sad in relation to the country level, while this was not the case with accommodation capacity. A decrease in the number of overnight stays can be explained by the restructuring of the existing offer in accordance with modern European standards.

However, the last two years showed an increase in accommodation capacity in Belgrade. The share of Belgrade in the total number of tourism overnight stays was 24.2% in the last year of observation. The role of Belgrade in the foreign tourism of Serbia was shown by the share in the total foreign traffic of the country, which was 63.5% in 2017. However, the share of Belgrade in the total accommodation capacity in Serbia was only 15%. This discrepancy showed that the occupancy of accommodation capacity was significantly higher in Belgrade than in Serbia.

Although much lower, the share of Novi Sad in the total (4.1%), and in the number of foreign (7.2%) overnight stays, as well as the total number of beds (8%), according to 2017 data, was not negligible.

Table 2 – The number of tourism overnight stays (in 000) and accommodation capacity of leading city tourism destinations in Serbia Year

2008

Serbia

Belgrade

Novi Sad

Total overnight stays

Foreign overnight stays

Number of beds

Total overnight stays

Foreign overnight stays

Number of beds

Total overnight stays

Foreign overnight stays

Number of beds

7334

1399

116182

1233

739

15892

185

95

2638

2009

6762

1469

112815

1187

813

15585

162

97

2919

2010

6414

1452

119427

1130

791

15688

168

100

4209

2011

6645

1643

127664

1149

848

17014

214

143

10312

2012

6485

1797

113385

1431

939

15874

231

151

3938

2013

6567

1988

107256

1490

1036

15390

253

170

7976

2014

6086

2161

102940

1535

1142

14149

287

183

9674

2015

6652

2410

106102

1686

1286

13936

297

184

8278

2016

7534

2739

109469

1867

1460

15389

361

242

9534

2017

8325

3175

106029

2016

1620

15925

343

229

8463

Source: Republic Statistical Office of Serbia: Statistical Yearbooks of the Republic of Serbia: 2009 (339,341), 2010 (339,341), 2011 (326, 328), 2012 (328, 330), 2013 (336, 338), 2014 (352, 354), 2015 (352, 354), 2016 (354, 356), 2017 (384, 386), 2018 (350, 352)

These relations confirmed that, along with Belgrade, Novi Sad was a leading destination of city tourism in Serbia.

The following table shows the extent to which the scope of employment in the hotel industry followed the dynamics of the indicators presented in Table 3.

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Table 3 - The scope of employment in the leading city tourism destinations in Serbia (in 000) Year

Serbia

Belgrade

Novi Sad

Total

Accom. and food services

Total

Accom. and food services

Total

Accom. and food services

2008

1428.5

23.6

465.7

10.3

102.1

1.1

2009

1396.8

22.5

473.5

10.3

100.1

1.2

2010

1355

21

469

10

99

…

2011

1342.8

20.4

461.2

9.6

102.2

1.3

2012

1341.1

20.3

468.7

10.3

101.8

1.3

2013

1338.1

19.9

472.2

10.4

100.5

1.2

2014

1323.8

19.8

470.0

10.7

99.8

1.1

2015

1896.3

65.4

667.2

23.0

136.8

4.8

2016

1920.7

67.9

666.7

23.7

136.7

4.7

2017

1977.4

71.7

688.5

25.3

141.6

5.0

Source: Republic Statistical Office of Serbia: Municipalities in Serbia: 2009 (120-123); 2010 (120-123); Municipalities and regions in the Republic of Serbia: 2011 (128, 129); 2012 (88); 2013 (134); 2014 (152); 2015(152); 2016 (152); 2017 (152); 2018 (160).

The total scope of employment in Serbia had a negative trend until 2015. The same was the case with fluctuations in the number of employees in the sectors of accommodation and food. In Belgrade, there was an apparent increase in the sector of accommodation and food during the last three years of observation. In Novi Sad, in the sector of accommodation and food, after an increase in 2015, there was a slight decrease in 2016, and then an increase again in 2017.

In the total scope of employment, Belgrade represented Serbia with 35.5%, and with almost the same share in the sector of accommodation and food (35.3%). The share of Novi Sad was significantly lower - 7.5% in the total number of employees, and 7.0% in the number of employees in the accommodation and food sector. Belgrade is the main carrier of city tourism development, and particularly for foreign tourism development in Serbia, therefore, it is expected that a substantial scope of employment is engaged in it. Belgrade’s strikingly higher share in the number of employees in the hotel industry of Serbia (35%), compared to the number of beds (15%), indicates the complexity and quality of the capital’s hotel product, i.e., a higher share in high-class facilities characterized by the appropriate personnel.

Table 4 shows the regression analysis results for Serbia, and for leading city tourism destinations, such as Belgrade and Novi Sad.

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Table 4 - Regression analysis results for Serbia, Belgrade and Novi Sad Serbia (regress no. of employees d1 no. of total overnight stays, noconstant) Source

SS

df

MS

Number of obs.

=

10

Model

17144.8543

2

8572.42716

F ( 2,

8)

=

16662.95

Residual

4.11568243

8

.514460304

Prob

F

=

0.0000

10

1714.897

R – squared

=

0.9998

Adj R – squared =

0.9997

Root MSE

.71726

Total

17148.97

>

=

No. of employees

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

d1

44.40457

.5151736

86.19

0.000

43.21657

45.59256

No. of. total overnight stays

.0031889

.0000408

78.08

0.000

.0030948

.0032831

Belgrade (regress no. of employees d1 no. of total overnight stays, noconstant) Source

SS

df

MS

Number of obs.

=

10

Model

2456.44949

2

1228.22474

F ( 2,

8)

=

1325.93

Residual

7.41051307

8

.926314134

Prob

F

=

0.0000

2463.86

10

246.386

R – squared

=

0.9970

Adj R – squared =

0.9962

Root MSE

.96245

Total

No. of employees d1

Coef.

9.658796

No of. total overnight stays

.0077256

>

=

Std. Err.

t

P>|t|

[95% Conf.

Interval]

.7551864

12.79

0.000

7.917333

11.40026

.0002755

28.04

0.000

.0070903

.0083608

Novi Sad (regress no. of employees d1 no. of total overnight stays, noconstant) Source

SS

df

MS

Model

78.3365339

2

39.168267

Residual

.473466094

7

.067638013

Total

78.81

9

8.75666667

Number of obs.

=

9

F ( 2,

8)

=

579.09

F

=

0.0000

=

0.9940

Adj R – squared =

0.9923

Root MSE

.26007

Prob

>

R – squared

=

No. of employees

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

d1

3.106621

.2167505

14.33

0.000

2.594088

3.619155

.005175

.0004685

11.05

0.000

.0040672

.0062827

No of. total overnight stays

Predictors: (Constant): Overnight stays; Dependent Variable: Employees Source: Derived on the basis of the data contained in Tables 2 and 3; STATA Statistics

The results show that the regression model is statistically significant. The coefficient of determination value (R - squared) indicates that more than 99% variations of the dependent variable (the number of employees) can be explained by changes in the number of overnight stays. The regression equation for Serbia is: y = 0.0031889x.

According to this result, one person should be employed in the hotel industry for every 314 nights per year.

The regression equation for Belgrade is: y = 0.0077256x. According to this result, one person should be employed in the hotel industry for every 129 nights per year.

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The regression equation for Novi Sad is: y = 0.005175x. According to this result, one person should be employed in the hotel industry for every 193 nights per year.

Differences in employment needs depending on the number of nights can be explained by the type of accommodation facilities and by the assortment of offered services.

THE EFFECT OF HUMAN RESOURCES ON THE DEVELOPMENT OF LEADING SPA TOURISM DESTINATIONS IN SERBIA Vrnjačka Banja and Sokobanja are the leading spa tourism destinations in Serbia. This has been confirmed by their share in the total number of overnight stays and the accommodation capacity of spa resorts. The dominant position of Vrnjačka Banja in the spa tourism in Serbia has been shown by its share in the total number of tourism overnightstays of spa resorts, which amounted to 31.5% in 2017.

The share in the total number of beds was at 17.4%, so it can be concluded that the accommodation capacity of Vrnjačka Banja was much better occupied compared to the average in Serbian spa resorts.

Although much lower, the share of Sokobanja in the total number of overnight stays of spa resorts, which amounted to 11.8% in 2017, was also remarkable. The number of tourism overnight stays in Sokobanja had a less favorable trend compared to the average in Serbian spa resorts. The share of Sokobanja in the accommodation capacity of spa resorts in Serbia amounted to 23.6% in 2017. Compared to Vrnjačka Banja, Sokobanja had a lower share in the number of tourism overnight stays, and a higher share in the accommodation capacity of spa resorts in Serbia. This implies insufficient occupancy of accommodation capacity in Sokobanja.

Table 5 – The number of tourism overnight stays (in 000) and accommodation capacity in spa tourism resorts in Serbia, in Vrnjačka Banja and Sokobanja Year

Spa tourism resorts Total overnight stays

Foreign overnight stays

Number of beds

Vrnjačka Banja Total overnight stays

Foreign overnight stays

Sokobanja

Number of beds

Total overnight stays

Foreign overnight stays

Number of beds

2008

2368

101

42243

539

39

7169

393

4

14165

2009

2287

97

36919

609

41

6011

321

5

14097

2010

2211

104

37306

560

45

6138

295

5

14067

2011

2308

132

37445

579

50

6144

325

4

14137

2012

2035

134

35543

506

51

5620

307

3

13933

2013

2134

181

26536

595

63

13110

307

5

6786

2014

1852

201

24399

498

66

4144

220

11

6194

2015

1855

231

25459

563

82

4232

195

11

5972

2016

2085

254

26343

678

104

4342

219

7

6238

2017

2228

271

25207

702

98

4396

262

7

5958

Source: Republic Statistical Office of Serbia: Statistical Yearbooks of the Republic of Serbia: 2009 (341), 2010 (341), 2011 (328), 2012 (330), 2013 (336-338), 2014 (352-354), 2015 (352-354), 2016 (354-356), 2017 (384-386), 2018 (350-352)

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A decrease in the number of beds in the observed period was a result of insufficient adaptation to modern market requirements and, accordingly, the restructuring of the existing capacity.

Vrnjačka Banja is the leading tourist destination in the Raška District. This was indicated by the share of the total scope of employment in the accommodation and food sector, which amounted to as much as 42.9% in 2008. At the end of the observed period, there was a decrease in the share of Vrnjačka Banja in the total number of employees in the hotel industry, which was 24.3%.

However, the already high share of Vrnjačka Banja in the number of tourism overnight stays of Raška District of 44.0% in 2008, increased further to 56.7% in 2017. Thus, it can be concluded that the number of overnight stays in Vrnjačka Banja was not accompanied by appropriate dynamics of the number of employees in the hotel industry.

Table 6 shows how the scope of employment fits into the established relations.

Table 6 - Scope of employment in the leading spa tourism destinations in Serbia (in 000) Year

Raška District

Vrnjačka Banja

Zaječar District

Sokobanja

Total

Accomm. and food services

Total

Accomm. and food services

Total

Accomm. and food services

Total

Accomm. and food services

2008

41.8

1.4

5.1

0.6

20.7

0.3

2.5

0.2

2009

40.6

1.1

5.1

0.5

18.9

0.3

2.4

0.2

2010

39.0

1.0

5.0

0

17.0

0

2.0

0

2011

38.1

1.0

4.5

0.4

17.0

0.3

2.3

0.2

2012

37.5

1.0

4.0

0.4

16.3

0.3

2.2

0.2

2013

37.3

1.0

4.0

0.3

16.3

0.3

2.2

0.2

2014

36.6

0.8

3.7

0.2

15.5

0.2

2.0

0.2

2015

61.0

3.2

6.1

0.7

22.5

0.9

2.9

0.2

2016

61.7

3.6

6.6

0.9

22.1

0.9

3.0

0.2

2017

64.2

3.7

6.9

0.9

22.2

1.0

3.0

0.3

Source: Republic Statistical Office of Serbia: Municipalities in Serbia: 2009 (125,127); 2010 (125, 127); Municipalities and regions in the Republic of Serbia: 2011(133, 135); 2012 (92, 94); 2013 (138, 140); 2014 (156, 158); 2015 (156, 158); 2016 (156, 158); 2017 (156, 158); 2018 (164, 166).

In the Zaječar District, the dominance of Sokobanja was even more prominent, judging by the share in the total number of overnight stays at the beginning (75.8%) and end (69.2%) of the observed period. The share of Sokobanja in the total number of employees in the hotel industry of the Zaječar District ranged from 66.7% in 2008 to 30% in 2017. A decline of the Sokobanja share in the total number of overnight stays was followed by a more rapid decline in the share in the number of employees in the Zaječar District hotel industry.

The results of regression analysis for the leading Serbian spa tourist destinations, such as Vrnjačka Banja and Sokobanja, are shown in Table 7.

Statistical significance can be noticed. The value of the coefficient of determination (R - squared) indicates that more than 90% variations of the dependent variable (the number of employees) can be explained by changes in the number of overnight stays. The regression equation for Vrnjačka Banja is: y = 0.0006226x. According to this result, one person should be employed in the hotel industry for every 1,608 nights per year. The regression equation for Sokobanja is: y = 0.0005487x. According to this result, one person should be employed in the hotel industry for every 1,822 nights per year.

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The regression model shows much slower employment dynamics in the hotel industry in spa resorts than in city resorts. This is due to the structure of the type of accommodation in spa resorts, mostly oriented towards private houses and apartments, where a large number of employees is not required.

Table 7 - Regression analysis results for Vrnjačka Banja and Sokobanja Vrnjačka Banja (regress no. of employees d1 no. of total overnight stays, noconstant) Source

SS

df

MS

Number of obs.

=

10

Model

2.92820414

2

1.46410207

F ( 2,

8)

=

48.44

Residual

.241795858

8

.030224482

Prob

F

=

0.0000

10

.317

=

0.9237

Adj R – squared =

0.9047

Root MSE

.17385

Total

3.17

No. of employees

Coef.

Std. Err.

.430075 .1260647

d1 No of. total overnight stays

.0006226

.0001178

>

R – squared

=

t

P>|t|

[95% Conf.

Interval]

3.41

0.009

.1393693

.7207808

5.29

0.001

.0003511

.0008942

Sokobanja (regress no. of employees d1 no. of total overnight stays, noconstant) Source

SS

df

Model

.370901613

2

Residual

.039098387

8

Total

.41

10

No. of employees d1 No of. total overnight stays

Coef.

MS

Number of obs.

=

10

.185450806

F ( 2,

8)

=

37.95

.004887298

Prob

F

=

0.0001

.041

Std. Err.

t

>

R – squared

=

0.9046

Adj R – squared =

0.8808

Root MSE

.06991

P>|t|

= [95% Conf.

Interval]

.109685

.0445992

2.46

0.039

.0068391

.2125308

.0005487

.0000842

6.52

0.000

.0003546

.0007429

Predictors: (Constant): Overnight stays; Dependent Variable: Employees Source: Derived on the basis of the data contained in Tables 5 and 6; STATA Statistics

THE EFFECT OF HUMAN RESOURCES ON THE DEVELOPMENT OF LEADING MOUNTAIN TOURISM DESTINATIONS IN SERBIA Mountain tourism, along with spa tourism, is the main form of stationary tourism in Serbia. Zlatibor and Kopaonik are the leading destinations of mountain tourism in this country, as evidenced by the data presented in Table 8.

Table 9 shows the scope of employment in Zlatibor and Kopaonik.

In the total number of overnight tourism stays at Serbian mountain resorts, Zlatibor, according to the 2017 data, had a share of 34.4%. When it comes to Kopaonik, its share in the same year amounted to 26.5 %. In 2017, there were 26.7% of beds on Zlatibor and 29.6% on Kopaonik, of the total number of beds in the mountain tourism resorts of Serbia. These figures indicate a high degree of concentration of mountain tourism in Serbia, given the distinct dominance of the two mountain tourism destinations.

The municipality of Čajetina had a share of 32.4% in the total number of employees in the hotel industry of the Zlatibor District in 2017. That same year, the municipality of Čajetina achieved as much as 74.9% of 139

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the total number of overnight stays in the Zlatibor District. Observed dynamically, based on the presented data, it can be concluded that an increase in the number of tourism overnight stays on Zlatibor, as well as in the municipality of Čajetina, was not accompanied by an adequate increase in the number of employees.

Table 8 – The number of overnight tourism stays and accommodation capacity in the leading mountain tourism destinations of Serbia (in 000) Year

Spa tourism resorts Total overnight stays

Foreign overnight stays

Vrnjačka Banja Total overnight stays

Number of beds

Foreign overnight stays

Sokobanja

Number of beds

Total overnight stays

Foreign overnight stays

Number of beds

2008

1912

136

21782

436

46

3917

423

41

4741

2009

1673

125

21817

389

44

3771

324

30

4742

2010

1467

129

22808

404

52

4890

234

24

4325

2011

1590

148

22379

483

65

4629

271

29

4018

2012

1601

155

24209

463

62

5404

298

37

4480

2013

1558

194

20551

456

67

4814

341

48

4221

2014

1412

215

20646

427

79

5110

323

51

4437

2015

1661

242

21222

557

101

5936

427

64

5404

2016

1929

287

22709

652

120

6306

496

89

4563

2017

2079

321

22604

714

132

6030

551

105

6686

Source: Republic Statistical Office of Serbia: Statistical Yearbooks of the Republic of Serbia: 2009 (341), 2010 (341), 2011 (328), 2012 (330), 2013 (336-338), 2014 (352-354), 2015 (352-354), 2016 (354-356), 2017 (384-386), 2018 (350-352)

The share of the Raška Municipality in the total number of employees in the hotel industry of the Raška District amounted to 18.9% in 2017. That same year, the share of Raška in the total number of overnight stays in the Raška District was 35.3%. The number of employees in the municipality of Raška and, therefore, on Kopaonik, showed an unfavorable fluctuation dynamic compared to the number of overnight tourism stays.

Table 9 - Scope of employment in the leading mountain tourism destinations in Serbia (in 000) Year

Zlatibor District Total

Accomm. and food services

2008

47.8

2009 2010

Čajetina

Raška District

Total

Accomm. and food services

1.4

2.8

46.0

1.3

45.0

1.0

2011

44.3

2012

Raška

Total

Accomm. and food services

Total

Accomm. and food services

0.7

41.8

1.4

4.0

0.5

2.6

0.6

40.6

1.1

3.4

0.3

3.0

1.0

39.0

1.0

3.0

0

1.2

2.8

0.7

38.1

1.0

3.6

0.2

44.2

1.1

2.8

0.6

37.5

1.0

3.2

0.3

2013

43.5

1.1

2.9

0.7

37.3

1.0

3.2

0.3

2014

42.7

1.0

2.8

0.6

36.6

0.8

3.2

0.3

2015

65.8

3.3

4.4

1.1

61.0

3.2

5.0

0.6

2016

65.3

3.3

4.5

1.1

61.7

3.6

5.5

0.7

2017 66.4 3.4 4.8 1.1 64.2 3.7 5.5 0.7 Source: Republic Statistical Office of Serbia: Municipalities in Serbia: 2009 (124.126); 2010 (133, 135); Municipalities and regions in the Republic of Serbia: 2011 (131, 133); 2012 (90, 92); 2013 (136, 148); 2014 (154, 156); 2015 (154, 156); 2016 (154, 156); 2017 (154, 156); 2018 (162, 164).

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Table 10 shows the results of regression analysis for Zlatibor and Kopaonik.

Results are statistically significant, and they indicate that more than 95% variations of the dependent variable (the number of employees) can be explained by changes in the number of overnight stays.

The regression equation for Zlatibor is: y = 0.0015748x. According to this result, one person should be employed in the hotel industry for every 635 nights per year.

The regression equation for Kopaonik is: y = 0.0009022x. According to this result, one person should be employed in the hotel industry for every 1,108 nights per year.

The slower employment dynamics in the hotel industry of Kopaonik compared to Zlatibor should be connected to the differences in the structure of the accommodation offer. Recently in Zlatibor, first-class and luxury hotels that employ a large number of employees have been built. Furthermore, seasonality is less pronounced on Zlatibor than on Kopaonik.. This also affects the faster growth of employees in connection with the movement of the number of overnight tourism stays. Similar to the observed leading spa resorts, there is a strong orientation towards private apartment accommodation, which does not require a large number of employees on Kopaonik.

Table 10 - Results of regression analysis for Zlatibor (Čajetina) and Kopaonik (Raška) Zlatibor (regress no. of employees d1 no. of total overnight stays, noconstant) Source

SS

df

MS

Number of obs.

=

10

Model

6.9903844

2

3.4951922

F ( 2,

8)

=

147.46

Residual

.189615597

8

.02370195

Prob

F

=

0.0000

10

.718

=

0.9736

Adj R – squared =

0.9670

Root MSE

.15395

Total

7.18

No. of employees

>

R – squared

=

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

d1

.0905776

.1228313

0.74

0.482

- .192672

.3738272

No. of Total overnight stays

.0015748

.0001323

11.91

0.000

.0012698

.0018797

Kopaonik (regress no. of employees d1 no. of total overnight stays, noconstant) Source

SS

df

MS

Number of obs.

=

10

Model

1.92693523

2

.963467617

F ( 2,

8)

=

122.22

Residual

.063064766

8

.007883096

Prob

F

=

0.0000

10

.199

=

0.9683

Adj R – squared =

0.9604

Root MSE

.08879

Total

1.99

>

R – squared

=

No. of employees

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

d1

.2233749

.0723695

3.09

0.015

.0564907

.3902592

No. of Total overnight stays

.0009022

.000104

8.68

0.000

.0006625

.001142

Predictors: (Constant): Overnight stays; Dependent Variable: Employees Source: Derived on the basis of the data contained in Tables 8 and 9; STATA Statistics

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CONCLUSION The destination approach amounted to the study of tourism insists on the quality of hotel and tourism products. The substitution process is present in tourism. Due to the existence of destinations with the same type of tourist attractions, the quality of the hotels’ offer should be expressed as a comparative advantage. It doesn't refer solely to the technical aspect of quality, but to the human dimension of a hotel service as well. So, we can conclude that human resources in the hotel industry should be an important factor of tourism destination comparative advantage. Given that human resources are a key component of hotel and tourism product quality, an assessment of their effect on the future development of tourism is inevitable.

The basic contribution of this paper is reflected in the statistically proven correlation between the number of employees and the number of overnight stays. The application of the regression model showed that the required number of employees can be predicted depending on the number of overnight stays.

The scope of employment is a quantitative framework for undertaking further qualitative research of human resources at the level of a tourist destination. It is about research where the focus should be on the qualifications, knowledge and skills of employees, according to the needs of consumers.

The research results obtained from this paper indicate problems related to employment in the tourism and hotel industry of Serbia, and that it is lagging behind the world and region, something which must be considered as one of the major limiting factors for future development. The competitiveness of tourist destinations and the effect of human resources on the development of tourist destinations have been approached in terms of an analysis of the relationship between the number of employees in the hotel industry, the number of overnight stays, and the volume of accommodation capacity in the leading destinations of the city, mountain and spa tourism in Serbia. We have found a turbulent, mainly unfavorable fluctuation in all the indicators as a result of the insufficient commitment to the strategic market positioning of Serbia as a tourist destination.

The stabilization and the signs of positive development have become evident in recent years. However, it seems that the growth dynamic of accommodation capacity is not a result of planned activities since it is not accompanied by an adequate increase in employment. The primary orientation towards accommodation in private houses results in an increase in the number of overnight stays, but not in an increase in employment. Service providers in private houses remain largely outside the registration of the number of employees in the accommodation and F&B sector.

On the other hand, given the established regression, an increase in the number of overnight tourism stays should lead to an increase in the number of employees in the hotel industry. New personnel should contribute to increasing the quality of hotel and tourism products. They should be the initiators of activities regarding the hotel offer restructuring, repositioning in the market and increasing the competitiveness of Serbia and its leading tourist destinations.

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UTICAJ LJUDSKIH RESURSA NA RAZVOJ VODEĆIH TURISTIČKIH DESTINACIJA U SRBIJI

Rezime: Cilj rada je da pokaže povezanost između obima i dinamike turističkog prometa i smeštajnih kapaciteta i obima i dimamike zapošljavanja u hotelijerstvu Srbije. Za predmet istraživanja izabrane su najrazvijenije turističke destinacije Srbije (na polju gradskog turizma, Beograd i Novi Sad, banjskog turizma, Vrnjačka Banja i Sokobanja i planinskog turizma, Zlatibor i Kopaonik). Prvi deo rada govori o povezanosti izučavanja turističke destinacije, s jedne i ljudskih resursa, s druge strane.

U drugom delu rada se prezentuje kvantifikacija uticaja zaposlenosti na broj noćenja u vodećim turističkim destinacijama Srbije, zasnovana na korelacionoj i regresionoj analizi i izvode zaključci primenom analitičko-sintetičkog i komparativnog metoda.

Ključne reči: turistička destinacija, zaposlenost, broj noćenja, smeštajni kapacitet, Srbija.

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Vol. 17 Nº 1

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Vol. 17 Nº 1 APRIL 2020 journal.singidunum.ac.rs

2020

Determinants of Profitability of the Agricultural Sector of Vojvodina: The Role of Corporate Income Tax p. 1-19

Herd Behaviour in the Cryptocurrency Market: Fundamental vs. Spurious Herding p. 20-36

Potential Effects of Cryptocurrencies on Monetary Policy p. 37-48

Impact of the Business Sector on Children’s Rights in Serbia p. 49-66

Have Export Compositions Influenced Economic Growth of the European Union Countries in Central and Eastern Europe? p. 80-103

An Analysis of North Korean Trade Amid Warming Global Relations Utilizing RCA, RSCA and TBI p. 113-127

Does Unemployment Lead to Criminal Activities?

An Empirical Analysis of CEE Economies p. 104-112

The Influence of Human Resources on the Development of Leading Tourism Destinations in Serbia p. 128-145

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