Embed Size (px)
Citation preview
ISSN (online) 2029-8501
MYKOLAS ROMERIS UNIVERSITY
INTERNATIONAL SCIENTIFIC CONFERENCE
“WHITHER OUR ECONOMIES– 2019”
Conference Proceedings
Vilnius, 2019
2
ORGANIZED BY MYKOLASROMERISUNIVERSITY
Faculty of Economics and Business
IN COOPERATION WITH
EUROACADEMY (ESTONIA)
UNIVERSITY OF FOGGIA (ITALY)
Department of Economics
GDAŃSK UNIVERSITY OF TECHNOLOGY (POLAND) Faculty of Management and
Economics
UNIVERSITY of ŽILINA (SLOVAKIA)
Faculty of Management Sciences and Informatics
RIGA TECHNICAL UNIVERSITY (LATVIA)
Faculty of Engineering Economics and Management
CONFERENCE PROCEEDINGS CONTAIN FULL PAPERS
INTERNATIONAL SCIENTIFIC COMMITTEE
Chairperson: Gintaras Černius (Lithuania)
Co-Chairs: Pasquale Pazienza (Italy) Tatjana Polajeva (Estonia) Błażej Prusak (Poland) Natalia Lace (Latvia) Martina Blaškova (Slovakia)
Members
Vytautas Azbainis (Lithuania) Bruno Amann (France) Kiril Angelov (Bulgaria) Artūras Balkevičius (Lithuania) Małgorzata Gawrycka (Poland) Dusan Baran (Slovakia) Martina Blaškova (Slovakia) Oleksandr Brovar (Ukraine) Francisco Carballo Cruz (Portugal) Francesco Conto’ (Italy) Ivan Dakov (Bulgaria) Caterina De Lucia (Italy) Fernando Garcia Garcia (Spain) Francisco Guijarro (Spain) Gintarė Giriūnienė (Lithuania) Lukas Giriūnas (Lithuania) Kristina Kalašinskaitė (Lithuania) Eglė Kazlauskienė (Lithuania) Mindaugas Kiškis (Lithuania) Grygorij Khoruzhyy (Ukraine) Alena Kocmanova (Czech Republic) Stanislava Kovacheva (Bulgaria) Todor Kralev (Macedonia) Alena Kocmanova (Czech Republic) Natalia Lace (Latvia) Marius Laurinaitis (Lithuania) Tadas Limba (Lithuania) Lienite Litavniece (Latvia) Hana Lostakova (Czech Republic)
Irena Mačerinskienė (Lithuania) Tomas Mendelsonas (Lithuania) Mangirdas Morkūnas (Lithuania) Peter Nemecek (Czech Republic) Tatjana Oriekhova (Ukraine) Tomas Pavelka (Czech Republic) Tatjana Polajeva (Estonia) Violeta Pukelienė (Lithuania) Ona Gražina Rakauskienė (Lithuania) José António Cadima Ribeiro (Portugal) Elina-Gaile Sarkane (Latvia) Irmantas Rotomskis (Lithuania) Lyudmila Shaybakova(Russia) Andreea Claudia Serban (Romania) Žaneta Simanavičienė (Lithuania) Jusif Seiranov (Lithuania) Jelena Stankevičienė (Lithuania) Rimantas Stašys (Lithuania) Iveta Šimberova (Czech Republic) Darius Štitilis (Lithuania) Rima Tamošiūnienė (Lithuania) Jelena Titko (Latvia) Irina Teleshova (Russia) Neviana Taneva (Bulgaria) Gediminas Valantiejus (Lithuania) Asta Vasiliauskaitė (Lithuania) Sergey Vdovin (Russia) Vladen Velev (Bulgaria) Vincenzo Vecchione (Italy) Natalia Volgina (Russia) Rima Žitkienė (Lithuania)
3
ORGANIZING COMMITTEE
Chairperson: Rima Žitkienė Deputy chair: Liucija Birškytė Marius Laurinaitis
Members: Ligita Gasparėnienė Gintarė Giriūnienė Tadas Limba Ona Gražina Rakauskienė Asta Vasiliauskaitė
ALL PAPERS WERE PEER REVIEWED. LANGUAGE IS NOT EDITED
BOARD OF REVIEWERS
Natalia Lace (Latvia)
Andreea Claudia Serban (Romania)
Caterina De Lucia (Italy)
Dusan Baran (Slovakia)
Liucija Birškytė (Lithuania)
Gintarė Giriūnienė (Lithuania)
Lukas Giriūnas (Lithuania)
Daiva Jurevičienė( Lithuania)
Kristina Kalašinskaitė (Lithuania)
Žaneta Karazijienė (Lithuania)
Larisa Kapranova (Ukraine)
Eglė Kazlauskienė (Lithuania)
Irena Mačerinskienė (Lithuania)
Askoldas Podviezko ( Lithuania)
Irmantas Rotomskis (Lithuania)
José António Cadima Ribeiro (Portugal)
Mangirdas Morkūnas (Lithuania)
Ligita Gasparėnienė (Lithuania)
Ona Gražina Rakauskienė (Lithuania)
Rima Tamošiūnienė (Lithuania)
Rima Žitkienė (Lithuania)
Rimantas Vaicenavičius (Lithuania)
Rita Remeikienė (Lithuania)
Tatjana Polajeva (Estonia)
Tomas Pavelka (Czech Republic)
Asta Vasiliauskaitė (Lithuania)
Inga Žilinskienė (Lithuania)
4
CONTENT
George Abuselidze, Lela Mamaladze NEW BANKING REGULATIONS AND THEIR EFFECT ON ECONOMIC ACTIVITIES IN GEORGIA
4
Yaroslava Levchenko, Asta Vasiliauskaite IMPROVEMENT OF THE METHODOLOGY FOR INTEGRATED ASSESSMENT OF ENTERPRISE INVESTMENT ATTRACTIVENESS
15
Aleksejus Sosidko, Ligita Gaspareniene EVALUATION OF THE UNEMPLOYMENT RATE ANNOUNCEMENT IMPACT ON EURO STOXX 50 INDEX RETURNS BASED ON SEMI-STRONG EFFICIENT MARKET HYPOTHESIS
30
Irena Mačerinskienė, Simona Survilaitė INTELLECTUAL CAPITAL IN THE LISTED COMPANIES OF THE BALTIC STATES 46
Saulius Kromalcas, Žaneta Simanavičienė, Daiva Besagirskaitė ECONOMIC SECURITY AS A PHENOMENON AND CONCEPT 64
Inna Kremer-Matyškevič, Gintaras Černius COUNTRY'S ECONOMIC SECURITY CONCEPT: THEORETICAL INSIGHTS 78
Ligita Gasparėnienė, Rita Remeikienė, Renata Šivickienė IMPACT OF FOREIGN DIRECT INVESTMENT ON TAX REVENUE 99
Virgilijus Dirma, Žaneta Simanavičienė ENERGY AND ECONOMIC RELATIONS IN A POSITIVE ECONOMY 113
J.Lape, S.Preidys, I.Zilinskiene TRADITIONAL AND MACHINE LEARNING-BASED METHODS FOR FINANCIAL INSTRUMENT PRICE FORECASTING: A THEORETICAL APPROACH
124
George Abuselidze, Jilda Abashidze, Anna Slobodianyk THE PROBLEM OF INEQUALITY IN INCOME IN GEORGIA AND THE ROLE OF PROGRESSIVE TAXATION IN ITS ELIMINATION
136
O. G. Rakauskienė, V. Velikorosov, D. Balachanova, THE EMPLOYMENT AND BUSINESS DIRECTIONS OF CULTURAL ORGANIZATIONS IN THE EUROPEAN UNION MEMBER STATES
148
4
NEW BANKING REGULATIONS AND THEIR EFFECT ON ECONOMIC ACTIVITIES IN
GEORGIA
George ABUSELIDZE
Batumi Shota Rustaveli State University1
Ivane Javakhishvili Tbilisi State University2
Georgia
Lela MAMALADZE
Batumi Shota Rustaveli State University1
Ivane Javakhishvili Tbilisi State University2
Georgia
Abstract: The functioning of the commercial banking system plays an important role in the
development of the state under the transformed economy. Also, the economic growth rate of the country
stands for the stability of the banking sector. The problems of commercial banks and other subjects of
financial sector are instantaneously affecting other areas of economy.
The modern Georgian banking system was founded after the collapse of the USSR and the independence
of the country. By 1995 the number of banks was more than 100 that was reduced to 16 in 2017. At
present, the National Bank of Georgia is supervising the financial sector, which is obliged to analyze and
elaborate policies to reduce risks and stability to the minimum.
The purpose of the work is to study new banking regulations in Georgia, which entered into force from
January 1, 2019 and radically changed the existing lending policy. The article analyzes the regulatory
situation as a bank, as well as for business sector and consumers.
The work is based on both qualitative as well as quantitative methods of research, in particular analyzing
official documents and legal documents and statistical data to make clear the distinction between before
and after the regulation period.
The recommendations developed based on the results of the research have a practical purpose that can
be used when developing various state documents for the regulation of the financial sector.
Key words: Banking system, banking regulations, economic activities, Georgia
5
Introduction
Excessive credit is one of the most serious problems of the population. According to the International
Monetary Fund, Georgia has the leading position among the world's countries with the debt in
commercial banks [20]. According to the International Monetary Fund's data of 2013, every third
borrower in Georgia has been paying more than 50% of its income on bank liabilities [28]. In order to
regulate this problem, significant changes have been made in the lending policy of 2017-2019. Namely:
➢ Approved in 2017 and already enacted on the Basel 3 framework, "Rules for Disclosure of
Information by Commercial Banks", according to which banks are obliged to publish
quantitative and qualitative information about capital, risk-weighted assets, management
compensation and other important issues.
➢ Amendments were made to the Organic Law on "National Bank of Georgia", according to which
the National Bank has been given full authority to supervise the entire financial sector (banks,
microfinance organizations, non-bank institutions, etc.) From May 2018, NBG has restricted
unsecured consumer loans to more than 25% of the bank's capital.
➢ Effective interest rate up to 50% [14].
And since January 1, 2019, the "Framework for Responsible Credit" of the National Bank has been
elaborated, in which the borrower is obliged to assess the user's income, the cost of securing the loan,
which aims to protect the borrowers and the financial system from risks caused by the fluctuation of the
foreign currency exchange rate [15].
These changes have caused a number of reactions. According to one part of the specialists, the
amendments will strengthen the sustainability of the financial sector. In the short term this will bring
down the retail lending, increase in expenditure in the sector, increase demand on capital and decrease
profitability, and in the long term the qualitative improvement of the Georgian financial system [14].
Others (Kipiani, 2019 [29]; Zambakhidze, 2019 [30]; Kepuladze, 2019 [31]) believe that is expected to
increase the credit rating, which will further increase the financial burden of consumers and cause a
systemic problem.
The goal of the research is to analyze existing data before the change of lending policy and after it and to
review
➢ Commercial Banks' Income
➢ Small Business
6
➢ Building and Real Estate Sector
➢ Customers' Perspective;
The research has the greatest practical and theoretical significance, since the analysis of the changes in
the short term as a result of the above regulations creates a clear representation of the impact of changes
and allows us to get the most objective prognosis of the long term and work out relevant
recommendations.
A theoretical framework of the thesis is based on traditional and non-traditional monetary policy.
Literature review
At the modern stage, radical transformation of monetary policy has been made and new methods of
interaction between money and credit and fiscal policy have been developed.
Using monetary policy when using traditional monetary credit methods stimulates the economy through
two channels:
➢ Interest rate channel. It is a mechanism of monetary policy, whereby a policy-induced change in
the short-term nominal interest rate by the central bank affects the price level, and subsequently
output and employment [16].
➢ Bank lending channel. It represents the credit view of this mechanism. According to this view,
monetary policy works by affecting bank assets (loans) as well as banks’ liabilities (deposits).
The key point is that monetary policy besides shifting the supply of deposits also shifts the
supply of bank loans [17].
The most traditional approach to identify and estimate the effects of monetary policy shocks remains the
timing assumption that a current innovation to the instrument used by monetary policymakers [7]. The
traditional approach, which identifies changes in monetary policy with changes in the stock of money, is
not adequate, since in practice the growth rates of monetary aggregates depend on a variety of variables
[1].
The forms of monetary policy, particularly used when interest rates are at or near 0% and there are
concerns about deflation or deflation is occurring, are referred to as unconventional monetary policy.
These include credit easing, quantitative easing, forward guidance, and signaling [18].
7
There is a widespread notion that unconventional monetary policy actions undertaken by central banks
over the past years have helped to alleviate some of the immediate downside risks to financial markets
and the global economy [13].
John Maynard Keynes pointed to an increase in the role of the state to eradicate the crisis and suggested
that the increase of money in circulation would improve the investment environment and stimulate
consumption, but ignored the risk of rising inflation [9].
The instrument of non-traditional monetary policy implies support of the state when financial
institutions cannot afford the crisis using only standard market methods.
Unconventional financial methods include:
➢ Negative interest rate policy stimulates bank net worth. Significant capital gains and a more
favourable economic environment, that induces banks to increase credit supply to the real
economy, outweigh the costs of negative rates. A simple welfare analysis yields that under
reasonable conditions, negative interest rates might constitute an optimal crisis response [2].
➢ Zero interest rate policy is a macroeconomic concept describing conditions with a very low
nominal interest rate. Can be associated with slow economic growth, deflation, and deleverage.
➢ Forward guidance is when the Central Bank announces to markets that it intends to keep interest
rates at a certain level until a fixed point in the future. The aim of forward guidance is to
influence long-term interest rates and market expectations [19].
However unconventional monetary policy takes many forms. In some cases (for instance Denmark), it
involves the use of negative interest rates. Some commentators advocate suspension or changes to
inflation targets. The more common forms of unconventional monetary policy involve massive
expansion of central banks' balance sheets and attempts at influencing interest rates other than the usual
short-term official rates [12]. So, unconventional monetary policy helped to stabilize some sectors and
provoked modest additional risk taking in others [6].
In sum, we can say that the main difference between traditional and non-traditional monetary policy is
that through existing instruments. In the case of traditional monetary policy, the central bank sells or
buys state bonds to maintain the current interest rate.
While qauantative easing during non-traditional monetary policy, the Central Bank buys or provides
financial assets to maximize the money on the market and to stimulate the economy [16].
The main difference between the traditional non-standard monetary policy is the traditional (standard)
monetary policy of how the neoconservative principle of non-interference of the state of the market in
8
the market changes, how does the central bank change its policy and sanitizing measures to overcome
the crisis (Borio & Disyatat [3]; Furceri, Loungani & Zdzienicka [8]; Kuroda [10]; Ueda [11];
Carpenter & Demiralp [4]; Cecioni, Ferrero & Secchi [5]; etc.).
Therefore, the main driving mechanism in monetary policy is to operate the money. This was the old
debate between the neo keynesians and monetarists. More important is the combination of Keynesianism
and monetarism and non-traditional cash-credit methods in the modern economy. The International
Monetary Fund economists created the term MP-plus ("Monetary Policy Plus"), which means a very
large set of financial instruments, including traditional cash-credit methods and non-traditional methods.
Using monetary policy when using traditional monetary credit methods stimulates the economy through
two channels. These include: interest rate channel (interest rate channel) and bank lending channel (bank
lending channel).
Methods survey and results
According to the National Bank of Georgia, the purpose of the Framework for responsible credit is to
support stable functioning of the financial system and encourage healthy lending, which in turn will
contribute to sustainable development of the country's economy. According to the basic principle of
regulation, the financial institution should not impose a loan or other obligation without studying the
person's solvency. This principle is one of the requirements of European Union.
The regulation applies to all loan issuing entities under the supervision of the National Bank. The loan
issuer shall be obliged to assess the user's income, the cost of the loan and the loan, whose monthly
payments (LTV) and the collateral value (LTV) do not exceed the limits set by this regulation. The
provision for loan to all organizations simultaneously came into effect on 1 January 2019 [15].
The reform was based on two goals:
1) Gradual reduction in the level of felicity, which will enable us to get some type of economic
advantage in Georgia. This will enable us to macroeconomic policy and macroeconomic status in
Georgia to become more stable and predictable in the long term.
2) The rights of users in the financial sector are protected [20].
The subsequent sections of the paper examines the results of changes in the Framework for responsible
credit enacted on 1 January 2019, in the short term of time, as well as the prospect of different segment
9
of the financial market. Analysis gives an opportunity to assess the current situation, to identify the
advantages, risks and predictions.
Responsible Credit Framework
Within the framework of Framework for responsible credit the National Bank regulates the average
weighted interest rate that aims to estimate average annual weighted interest rates on loans issued in
different national / foreign currencies issued by commercial banks. Namely:
• Different maturities loans
• Loans to various categories of debaters
• Consumer loans
• Loans provided by real estate.
Also, average annual weighted interest rates are determined by the flow of loans and balances (National
Bank, 2019).
The average annual weighted interest rate can be calculated using the formula:
𝑃 =∑𝑃𝑉
∑𝑉,
The average annual weighted interest rate for loans is P, the nominal annual interest rate, the V - loan
volume according to the Agreement.
Based on the monthly statistical reporting of commercial banks operating in the territory of Georgia
(including non-resident banks on the territory of Georgia), we get the following picture (diagram)
10
Source: National Bank of Georgia, 2019
The results of banking regulations from the perspective of commercial banks
The net profit of 15 commercial banks operating in Georgia in January 2019 amounted to 67,755 million
GEL, which is 792 thousand less compared to the same period of the previous year [21].
After enabling bank regulation, the volume of loans issued to banks in January decreased by 170.2
million lari compared to December. According to the National Bank statement, the volume of loans
issued in national currency decreased by 118,7 million lari (1%) and the volume of loans in foreign
currency decreased by 51.5 million lari (3%). The volume of lending to resident individuals decreased
by 1% or 265.8 million lari in January of the year 2019 and amounted to 13,9 billion lari [22].
Building sector and real estate market
The tightening of the loan for the banking sector has had a direct impact on the building and real estate
sector, whose share of the country's economy exceeded 9% in 2017-2018. Some of the developers say
that after the regulation, their sales were halved, and some were stopped at all. According to them,
business in such conditions ceases to develop. In January 2019, the sale of real estate companies
decreased by 50% compared with the same period last year [21].
11
Small Business
Enactment of new regulations has significantly reduced trade equipment stores. It was complicated and
in many cases it was impossible to use the installment service, which inflicts damages to the share of
installments. According to local media, "Regions, where citizens are self-employed, cannot afford the
revenues and they are practically prohibited from using new banking regulations." In some cases, there
is the risk of closing the business, after which a lot of unemployed people will be found [24].
Conclusion
In conclusion, it can be said that for a large part of the population who bought household techniques
with the installment and real estate secured bank loans, availability on consumer loans as well.
In addition, the research, based on the data of the National Statistics Office of Georgia, "World
Experience for Georgia", considering that 40% of the population pay communal taxes with borrowed
money and 50% for loan for food [25].
If we agree that the population will be limited to access to finance for basic living conditions, it is likely
to reduce the demand for the loan but not the amount of money that the National Bank and Regulation
do not restrict. It should be noted that private creditors have a radically high interest rate than
commercial banks. Therefore, the necessity of putting the segment into the legal framework is not the
result of a longer period of impairment than before the regulation.
It is important that the Borrower's Payment Assessment Requirement is also defined by the EU Directive
(Directive 2008/48 / EC of Customer Credit Agreements), which, in agreement with the Association
Agreement, must adopt until September 2019 [26].
Also, the Central Bank's attempt to protect consumers' rights and gradually regulate the financial market
may be assessed positively. However, the scope of lending to lower income levels of population may be
more stimulating and promoting economic growth.
12
References:
Bernanke, B., & Mihov, I. (1998). Measuring Monetary Policy. The Quarterly Journal of
Economics, 113(3), 869-902. https://doi.org/10.1162/003355398555775.
Bernoth, K., & Haas, A. (2018). Negative interest rates and the signalling channel (No. 130). DIW
Berlin: Politikberatung kompakt. http://hdl.handle.net/10419/187510.
Borio, C., & Disyatat, P. (2010). Unconventional monetary policies: an appraisal. The Manchester
School, 78, 53-89. https://doi.org/10.1111/j.1467-9957.2010.02199.x.
Carpenter, S., & Demiralp, S. (2012). Money, reserves, and the transmission of monetary policy:
Does the money multiplier exist?. Journal of macroeconomics, 34(1), 59-75.
https://doi.org/10.1016/j.jmacro.2011.09.009.
Cecioni, M., Ferrero, G., & Secchi, A. (2011). Unconventional monetary policy in theory and in
practice. http://doi.org/10.2139/ssrn.1998755.
Chodorow-Reich, G. (2014). Effects of unconventional monetary policy on financial institutions
(No. w20230). National Bureau of Economic Research. https://doi.org/10.3386/w20230 .
Coibion, O. (2012). Are the Effects of Monetary Policy Shocks Big or Small? American Economic
Journal: Macroeconomics, 4(2), 1-32. https://doi.org/10.1257/mac.4.2.1 .
Furceri, D., Loungani, P., & Zdzienicka, A. (2018). The effects of monetary policy shocks on
inequality. Journal of International Money and Finance, 85, 168-186.
https://doi.org/10.1016/j.jimonfin.2017.11.004.
Keynes, J. M. (2018). The general theory of employment, interest, and money. Springer. Retrieved
from
https://cas2.umkc.edu/economics/people/facultypages/kregel/courses/econ645/winter2011/generalth
eory.pdf
Kuroda, H. (2016). The practice and theory of unconventional monetary policy. In Contemporary
Issues in Macroeconomics (pp. 7-14). Palgrave Macmillan, London.
https://doi.org/10.1057/9781137529589_2.
Ueda, K. (2012). The effectiveness of non‐traditional monetary policy measures: The case of the
Bank of Japan. The Japanese Economic Review, 63(1), 1-22. https://doi.org/10.1111/j.1468-
5876.2011.00547.x.
Joyce, M., Miles, D., Scott, A., & Vayanos, D. (2012). Quantitative easing and unconventional
monetary policy-an introduction. The Economic Journal, 122(564), F271-F288.
https://doi.org/10.1111/j.1468-0297.2012.02551.x .
13
Hattori, M., Schrimpf, A., & Sushko, V. (2016). The Response of Tail Risk Perceptions to
Unconventional Monetary Policy. American Economic Journal: Macroeconomics, 8(2), 111-136.
https://doi.org/10.1257/mac.20140016 .
Gvalia, T. (2018). New banking regulations. Retrieved from https://forbes.ge/news/4387/axali-
sabanko-regulaciebi.
National Bank Georgia, (2018). Retrieved from
https://www.nbg.gov.ge/index.php?m=340&newsid=3545.
Gvalia, T. (2016). What does unconventional monetary policy mean? Retrieved from
http://european.ge/ra-aris-aratradiciuli-monetaruli-politika/ .
What is the Bank Lending Channel of Monetary Policy Transmission? Retrieved from
https://www.cbn.gov.ng/Out/EduSeries/Series16.pdf.
Roubini, Nouriel (January 14, 2016). "Troubled Global Economy". Time Magazine. time.com.
Pettinger, T. (2013). Forward guidance in monetary policy. Retrieved from
https://www.economicshelp.org/blog/7732/economics/forward-guidance-in-monetary-policy/.
Bakhtadze, M. (2018). What will change the new financial reform for citizens?. Retrieved from
https://imedinews.ge/ge/interview/210/ras-shetsvlis-akhali-pinansuri-reporma-moqalaqeebisatvis.
Georgian commercial banks have reduced their profit. Retrieved from
https://imedinews.ge/ge/ekonomika/100241/saqartveloshi-komertsiul-bankebs-mogeba-sheumtsirdat.
https://on.ge/story/34246 .
Ioseliani, T. (2019). The impact of regulations on important sectors of economy. Retrieved from
https://sputnik-georgia.com/analytics/20190222/244463207/charbvalianobastan-brdzolis-sapirispiro-
shedegi.html.
https://commersant.ge/ge/post/biznesombudsmeni-axali-sabanko-regulaciebi-teqnikis-da-udzravi-
qonebis-bazrebs-azaralebs.
http://bfm.ge/akhali-regulaciebis-shedegad-ganvadebebi-ganakhevrda-teqnikis-maghaziebi-
gamosavals-ver-pouloben/.
National Bank of Georgia, (2018). Retrieved from
https://www.nbg.gov.ge/index.php?m=340&newsid=3333.
http://bfm.ge/ramdenad-moagvarebs-charbvalianobis-problemas-erovnuli-bankis-akhali-regulaciebi/.
http://resonancedaily.com/index.php?id_rub=3&id_artc=56756.
14
http://netgazeti.ge/news/285715/.
https://commersant.ge/ge/post/rodesac-fermeri-gatavisuflebulia-sashemosavlo-gadasaxadisagan-da-
am-dros-mas-shemosavlis-dadasturebas-stxov-es-nishnavs-imas-rom-mtavrobashi-komunikaciis-
problemaa.
https://fortuna.ge/fortuna/post/akrdzalva-motkhovnas-ver-amcirebs-safinanso-seqtoris-regulaciebis-
shedegebi-da-mosalodneli-riskebi.
15
IMPROVEMENT OF THE METHODOLOGY FOR INTEGRATED ASSESSMENT OF
ENTERPRISE INVESTMENT ATTRACTIVENESS
Yaroslava LEVCHENKO
Kharkiv (Ukraine), Kharkiv National
Automobile and Highway University 25,
Yaroslava Mudroho st.,
Asta VASILIAUSKAITE
Vilnius (Lithuania), Mykolas Romeris
University, 20, Ateities st., [email protected]
Abstract. The article presents an improved methodology for integrated assessment of investment
attractiveness (IA) of an enterprise. The main scientific approaches and methods for assessing
enterprise IA are considered. The accumulated experience of researchers in the field of assessing
enterprise IA is integrated and an improved mathematical model for assessing enterprise IA is
proposed. The aim of the study is to improve the methodological approach to assessing enterprise
IA. This can be achieved by combining internal quantitative and qualitative indicators of activity of
an enterprise and the indicators of attractiveness of the sector and region in which it operates into a
single integrated assessment model. Results of the research: It is found that the available models
and methods for assessing enterprise IA are limited to using either qualitative indicators or
quantitative ones. Moreover, the territory (region) and sector in which an enterprise operates have a
direct influence on attractiveness of the enterprise. Assessment of attractiveness of the sector and
region is carried out by means of individual methods and models. Within the framework of the study
there revealed the absence of a single mathematical model that would take into account all the factors
that influence enterprise attractiveness. The fact that numerically presented information is easier to
perceive determined the construction of an improved mathematical model for assessing enterprise
IA.
Keywords. Investment attractiveness, assessment model, integrated assessment, integration,
16
Introduction
Recently a number of studies have been conducted to assess attractiveness of individual
enterprises, regions, and countries. To date, not only countries but also regions, cities, and individual
enterprises are striving to improve their investment attractiveness (IA). It should be noted that
enterprise IA is determined not only by indicators of economic activity but by factors that influence
IA of the city, region, or sector in which it operates as well [1]. Research projects focusing on
inflows of foreign direct investment into specific areas are carried out by both academic economists
and world leading consulting companies, such as Ernst & Young (the United Kingdom). Some
problems associated with IA are considered in [2]. Researchers speak about the importance of IA of
an individual enterprise as the key component of IA of each region and country. Competitiveness of
a particular sector as well as the entire country depends on IA of the enterprises operating in them. It
is the basis of the European Union’s economy [3]. Fluctuations in the economy have forced
businesses to change traditional methods of organization and management and search for new tools,
knowledge, resources, and competencies in order to strengthen their positions and ensure their
competitiveness. It is not enough to pay attention only to IA of an enterprise, since its
competitiveness also depends on attractiveness of the sector and the region in which it operates [3].
Since the scope of research in the field of enterprise IA is expanding, the methods and models for its
assessment should also be presented more extensively. In this regard, scientists pay special attention
to the need to improve the methodological approaches for assessing enterprise IA. As far as
discussions in the field of studying the methodological principles of assessing IA continue, this
problem remains relevant. The struggle for investment is a kind of a beauty competition among
applicants for investment. The selection criteria in this competition are different indicators. The task
of science is to answer the question of how these indicators can be combined into one integrated
model for assessing enterprise IA. To achieve the task, at the first stage of the study, the
methodological approaches developed by academic economists are generalized and their views on
the solution of this problem are present.
The aim of the research is to improve the methodological approach to assessing enterprise
IA and develop an integrated mathematical model.
Related Work
To achieve this aim, the following tasks are set:
- analyzing the main scientific approaches and methods for assessing enterprise IA,
summarizing the accumulated experience of researchers in this area;
17
- proposing a calculation method that would allow for combining quantitative and qualitative
indicators (the method of data normalization);
- introducing into the methodology for integrated assessment of IA of an enterprise the
indicators of attractiveness of the sector and region in which it operates.
To achieve the aim, the following general scientific and special research methods and
techniques are used:
- theoretical generalization, analysis and synthesis — to study the theoretical foundations of
and approaches to assessing enterprise IA;
- data normalization – to bring all values of the indicators into the same region of variation to
further combine them into an integrated model;
- the methodology for integrated assessment – to develop an integrated model for assessing
enterprise IA through combining internal quantitative and qualitative indicators with the indicators of
attractiveness of the sector and region in which the enterprise operates;
- abstract-analytical method – for generalization and formulation of conclusions.
In [4], to determine the IA of an enterprise, it is proposed to use a systems approach and
analyze the entire set of factors influencing the state of financial and economic activities (FEA) of
the enterprise. This approach is quite common. It implies assessing IA of an enterprise with the use
of economic and mathematical analysis, which considers a set of indicators characterizing
performance of the enterprise. But the issue of applying integrated assessment is unresolved in the
work. The most objective assessment of attractiveness can be achieved rather by using integrated
assessment than by analyzing a large number of individual indicators that can be interpreted in
different ways. Determining enterprise attractiveness without applying integrated assessment is
suggested in the group of studies [5–7].
In work [5], it is proposed to abandon the analysis of FEA and focus on investment funds.
The findings of the study concern “investment efficiency” as the end result of implementing capital
investments to be used. Work [6] supports the findings of study [5]. It summarizes the main 5
traditional methods for assessing IA. A similar opinion is presented in study [7], the result of which
is the development of an approach that is closer to the European one. As the most versatile method
for assessing enterprise IA there proposed considering enterprise IA in terms of efficiency and
feasibility of investing with the use of traditional methods. Among such methods study [7] mentions
discounted cash flow (DCF), namely, net present value (NPV), and internal rate of return (IRR). The
researchers of this group consider assessment of IA from the standpoint of profitability of investing a
18
certain amount of money, which is not always relevant. Therefore, the issue of integration of the
entire set of indicators that determine enterprise IA remains unresolved.
In contrast to studies [5-7], the author of work [8] claims that for investors the innovation
component is of importance but not the sum of investments or the current state of the enterprise’s
FEA. It is justified that it is reasonable to assess the importance of innovation and only then speak
about the amount of investment. In this regard, the study suggests carrying out, first, quantitative
assessment of the innovation component of an enterprise and then comprehensive assessment in
order to objectively determine its IA. To carry out comprehensive assessment of the innovation
component, it is proposed to use the concept of innovation potential. But even though the study is
comprehensive, investments are not always made into innovative enterprises and not always
enterprise IA is considered only in terms of innovations. When developing a versatile methodology
for assessing enterprise IA, this approach is inexpedient.
Research [9] proves that the presence of the innovation component is absolutely not
important. The author states that the main indicators that investors pay attention to are those of
business activity, since they reflect real capabilities of an enterprise, and offers the integrated “Model
for business activity assessment”. This model is quite simple to use, but it has a drawback – it
considers enterprise IA only in terms of business activity. As part of assessment of enterprise IA, the
integration of this set of indicators is insufficient. For an adequate assessment, the model must
contain a sufficient number of indicators that comprehensively describe attractiveness [10]. It is this
approach that is used in studies [11-12]. In [11], to provide comprehensive assessment, it is proposed
applying factor analysis combined with an individual approach, since each consumer, and,
accordingly, investor has an individual taste and idea of attractiveness. The research results of [11]
got further development in [12]. The study proves that it is necessary to develop a model for
determining the level of enterprise IA based on factor analysis with the use of forecast estimates.
These approaches are interesting but their using is to some extend inexpedient. First, within the
framework of factor analysis it is impossible to combine quantitative and qualitative indicators into a
single integrated model. Secondly, to obtain a reliable forecast estimate, there required a sample of
data which are not always available within the required time range.
Based on the analysis of the methods for determining enterprise IA, we can conclude that
scientists have different views on this problem. Some of them emphasize the importance of
economic indicators. Others believe that each consumer, and, accordingly, investor, has an individual
taste and idea of attractiveness. And some researchers focus on forecasts for the future. Attempts to
carry out integrated assessment of enterprise IA are limited to either quantitative or qualitative
19
indicators of enterprise activity. It is a question of the influence of external factors [10], but no single
integrated mathematical model within the framework of the methodological approach for assessing
enterprise IA has been proposed so far. Considering the methodological approaches to assessing an
investee’s IA, it is possible to formulate the following disadvantages:
- a primary focus is on portfolio investment;
- assessment is carried out based on qualitative and quantitative factors separately;
- attractiveness of the external environment beyond the framework of the integrated model is
taken into account;
- the lack of a single approach to determining enterprise IA on the basis of economic and
other indicators.
All the above mentioned suggests that it is expedient to conduct a study aimed at improving
the methodological approach to assessing enterprise IA. Such improvement is possible by combining
internal quantitative and qualitative indicators with the indicators of attractiveness of the sector and
region in which the enterprise operates into a single integrated mathematical assessment model.
Description of the experiment and analysis of the results
Integration of scientific approaches to assessing enterprise IA
The elaboration of models for assessing IA contributes to scientific development. It is easier
to understand the result when it is presented quantitatively, and data obtained by integrated
assessment are especially easy to perceive.
In this study, a number of methods and models for assessing and analyzing IA of business
entities with the use of financial indicators are considered. It should be noted that their main features
are as follows:
- they are based on a large number of indexes united in certain groups by areas of analysis;
- indexes characterizing profitability, property and financial status of an investee are taken
into consideration;
- a lot of methods include analysis of indexes of investment risk and bringing the value of
different economic indicators to their present value by means of the system of discounting;
- determination of relative significance of certain indexes by means of ranking or
determination of their share;
- combination of various indexes into a single system for assessing IA through
20
determination of one or several integral indexes [1].
All of them are based on integrating indicators, but none of the presented methods consider
enterprise IA based on integration of quantitative and qualitative indicators with the indicators of
attractiveness of the sector and the region in which the enterprise operates.
The study takes into account all the shortcomings and gaps in this area and proposes an
improved methodological approach to assessing enterprise IA (Fig. 1).
Introduction of the indicators of attractiveness of the sector and region to improve the
methodology for assessing enterprise IA
Further, the study considers step by step the improved methodology for assessing enterprise
IA (Fig. 1) based on integration of quantitative and qualitative indicators with the indicators of
attractiveness of the sector and the region in which the enterprise operates.
Preparatory stage. For a successful implementation of any project within the framework of
enterprise management, information support is necessary [13]. At this stage the information on both
quantitative and qualitative indicators of enterprise activity can be collected. The basis for obtaining
such data can be open information resources and statistical information received from the enterprise.
Then the internal indicators of activity of enterprises and organizations are calculated on the
basis of the data collected at the first stage (quantitative and qualitative indicators). An important
condition is that the increase in each of the indicators should suggest a positive trend. The greater its
value is, the better condition it indicates.
Investors are looking for a relatively cheap, geographically attractive region or city [14] with
adequate resources (logistics, human resources, market size, economic and political stability, and
operating expenses) [15].
Based on the availability of public information on activities of enterprises [16] and publicly
available statistical data [17], it is possible to determine the entire set of indicators necessary for
integrated assessment. These are indicators which form the factors:
1) property status;
2) financial independence;
3) financial stability (solvency);
4) liquidity of assets;
5) profitability;
6) business activity [1];
7) attractiveness of the economic sector;
8) attractiveness of the region (territory).
21
All the indicators can be calculated based on available reliable data.
22
Fig. 1. Methodology for integrated assessment of enterprise IA (developed by the authors)
Calculation stage. The final list of quantitative and qualitative indicators of enterprise
activity used to form the model of enterprise internal IA is formed based on the results of the
correlation analysis of the initial data set [18].
As mentioned above, each person has his/her own idea of attractiveness, and, accordingly, of
the weight of each component of the model for assessing enterprise IA.
To determine the weighting coefficient of the above mentioned factors, it is proposed to break
them down into primary and secondary ones. The main factors include those that have a decisive
influence on IA. The breakdown of factors into primary and secondary occurs in accordance with
their significance or degree of influence. The choice of these indicators is proposed on the basis of an
audience survey conducted among industry experts and highly qualified researchers in this field.
Condition 1 “Striving for the maximum”. An increase in each of the indicators
should suggest a positive trend. The greater its value is, the better condition it
indicates
Formation of the preliminary array of input data for calculations
Pre
pa
rato
ry S
tag
e
Formation of the initial list of indicators of enterprise IA
Formation of the intermediate list of indicators of enterprise IA
Condition 1 “Reasonableness”. Application of content analysis for the formation of a list
of indicators
Condition 2 “Availability”. Information about an
indicator should be publicly available.
Condition 1 "Professionalism". The expert group is
formed from qualified professionals
Condition 2 “Optimality”. According to the
methodology presented by the SurveyMonkey Help
Centre, the number of respondents comprises 21 expert,
Data normalization:
xi
i
i
xx
=*
, where *
ix is the normalized indicator,
ix – indicator value in the group,
xi -
mean square deviation.
Ca
lcu
lati
on
Sta
ge
Correlation analysis of the array of input data
Formation of the final list of indicators of enterprise IA and the data array
Condition 1 “Objectivity”. The data array is
adjusted to prevent multicollinearity.
Condition 2 “Credibility”. The results of the
correlation analysis are checked using the t-test.
Calculation of the integral index of EIA: ІІIA = ∑Кj ∙ j , where ІІIA is the integral index of enterprise IA; Кj — the
synthetic (intermediate) indicator of the jth component of IA;, j — the weighting coefficient of the jth component of
enterprise IA (∑=1).
23
The audience survey was conducted on a sample of 21 experts. This number was determined
based on the Calculate the Number of Required Respondents You NEED methodology, presented by
the SurveyMonkey Help Centre. The structure of the experts is presented in Table 1.
Table 1. A sample structure of the questionnaire-based survey (%)
Area Percentage of respondents (N = 21)
Science 57
Business 24
Public administration 19
The coefficient of concordance varies from 0 to 1. Since the table value of the Pearson
criterion at the corresponding values of the degrees of freedom of each group does not exceed the
calculated value, and the concordance coefficient approaches to 1 (much greater than zero), the
consensus of expert opinions on the rank of factors of investment attractiveness is not coincidental.
The following criteria were chosen by the experts:
− Balance sheet total, which is the sum of all assets or all liabilities reflected in the
balance sheet. The importance of this indicator is determined by a fairly broad area of its application
in financial analysis. In addition, the balance sheet total determines whether the enterprise is subject
to audit.
− Coefficient of renovation of fixed assets, which shows the share of new fixed assets in
those available at the end of the reporting period. The higher the coefficient of renovation of fixed
assets, the higher the technical potential is.
− Coefficient of concentration of equity capital, an indicator to the value of which
investors and banks that issue loans pay special attention.
− Coefficient of independence form borrowed funds. The higher the value of this
indicator, the more attractive the enterprise is for investors. It is also an indicator to the value of
which investors and banks that issue loans pay special attention.
− Current or total coverage ratio, which allows investors to assess the ability of an
enterprise to pay off its debts by using available funds.
− Coefficient of financial stability. This indicator is important for investors, since it shows
the share of the sources of financing that the organization uses in its activity for more than a year.
− Coefficient of absolute liquidity. The importance of this indicator for investors is
determined by the fact that it indicates enterprise solvency.
24
− Working capital, which gives investors an idea of the corresponding operating
efficiency.
− Coefficient of return on equity. This indicator demonstrates the activity of money
resources and is taken into account by the investor in determining the risk level.
− Operating return on sales, which shows investors the efficiency of the enterprise.
− Coefficient of asset turnover. This indicator is used by investors to assess the
effectiveness of capital investments;
− Turnover of working capital, which is important for investors, since it shows how
effectively the enterprise uses investments in working capital.
To determine IA of the sector, the experts selected for the analysis the following indicators:
- structure of production;
- trends in capital investment;
- foreign direct investment;
- financial performance of enterprises which work in it.
To determine IA of the region, the experts selected:
- production volume;
- trends in capital investment;
- foreign direct investments;
- financial performance of enterprises which work in it.
The subsequent step of the study is determining which of the presented set of quantitative and
qualitative indicators is the most significant in assessment of enterprise IA (Tbl. 2).
Table 2. The results of implementing the hierarchy analysis method to determine the
weighting coefficients for assessing enterprise IA
№ Factors influencing enterprise IA Weighting coefficient
1 property status 0.1128
2 financial independence 0.1347
3 financial stability (solvency) 0.0597
4 asset liquidity 0.0749
5 profitability 0.0973
6 business activity 0.0802
7 attractiveness of the economy sector 0.2527
8 attractiveness of the region (territory) 0.1873
25
The introduction of data normalization to improve the method for assessing enterprise
IA
The need for data normalization is due to the nature of the factors influencing enterprise
attractiveness: they can vary greatly in absolute values (some indicators are qualitative, some
quantitative, or individual indicators are measured in thousands while others – in hundreds). Data
normalization allows to bring all the values of variables used into the same region of variation, so
that it could be possible to combine them into one model. At this stage, we propose to normalize the
data previously obtained by calculating the standard deviation of each indicator using descriptive
statistics. The normalization is performed by dividing the value of the statistical indicator by the
mean square deviation of the studied group.
xi
i
i
xx
=* , (1)
where *
ix is the normalized indicator,
ix – indicator value in the group,
xi – mean square deviation.
When all the indicators are brought to a common unit of measurement, it becomes possible to
combine them all into a single model.
The model for determining the integral index of the enterprise IA is:
ІІIA = ∑Кj ∙ j , (2)
where Кj — the synthetic (intermediate) indicator of the jth component of IA;
j — the weighting coefficient of the jth component of enterprise IA;
ІІIA = 0,1128*КGI + 0,1347*КGII + 0,0597*КGIII + 0,0749*КGIV +
+ 0,0973*КGV +0,0802*КGVI + 0,2527*КGVII + 0,1873*КGVIII, (3)
where ІІIA is the integral index of enterprise IA;
КGI — factor І;
КGII – factor IІ;
КGIII – factor IIІ;
26
КGIV — factor IV;
КGV — factor V;
К GVI — factor VІ;
К GVII — factor VІI;
К GVIII — factor VІII.
On the basis of the improved methodology for integrated assessment of enterprise IA, it is
possible to measure the share (significance) of the components that determine such attractiveness.
The hypothesis about the importance of introducing the indicators of sector-region attractiveness into
integrated assessment of enterprise IA is justified. It is determined that the main indicators of
enterprise IA are quantitative and qualitative internal indicators of activity of an enterprise and the
indicators of attractiveness of the economic sector and region in which it operates.
Conclusions and Outlook
1. In the course of the study, the main scientific approaches and methods for assessing
enterprise IA are analyzed, the accumulated experience of scientists is summarized. The analysis
reveals that the existing methods and models for assessing enterprise IA imply the assessment of
either quantitative or qualitative indicators of an enterprise; to assess attractiveness of the region and
sector in which the enterprise operates, there used individual methods. It is established that the
existing methods for assessing enterprise IA are rather labor intensive, time- and material-
consuming.
2. The need to use a calculation method which can allow combining quantitative and
qualitative indicators is proven. The application of data normalization, which makes it possible to
combine such indicators into one integrated model, is proposed. The attention is focused on the fact
that integrated assessment suggests a positive trend in its components (The greater its value of an
indicator is, the better condition it indicates).
3. The introduction of the indicators of attractiveness of the sector and the region into the
methodology for integrated assessment of enterprise IA is justified. This approach makes it possible
to comprehensively assess enterprise IA. The expediency of carrying out the calculation with
consideration for attractiveness of the sector and region in which the enterprise operates is proven.
The quantitative, qualitative indicators and indicators of attractiveness of the sector and region in
which the enterprise operates are combined into a single integrated mathematical model.
27
The improved methodological approach to assessing enterprise IA integrates the existing
approaches and takes into account the best practices of researchers in this area. Since each investor
has an individual vision of attractiveness, using this method is rather advisory than mandatory. The
existing methods for assessing enterprise IA are time- and material-consuming. The issue of
integrated assessment of enterprise IA is particularly important when it comes to evaluating a large
number of applicants for investment. Which company is the most attractive for investment? An
unequivocal answer to this question is provided by the improved method for assessing enterprise IA
based on the integration of internal quantitative and qualitative indicators with the indicators of
attractiveness of the sector and region in which the enterprise operates.
In the course of this study, attention is not paid to the influence of the factor of country
attractiveness on enterprise IA, which is the main limitation of this study. To date, there exist a
number of methods for determining attractiveness of a country. Researchers all around the world pay
enough attention to this aspect. However, within the framework of this study it is impossible to
complement the proposed integrated mathematical model with the indicator of country attractiveness.
The presented methodology integrates coefficient indicators. The possibility to supplement the
integrated model will arise in the event of developing a method for determining country IA with an
effective coefficient indicator.
A prospect for further research in this area is the receiving of practical approval of the
proposed methodology in enterprises of various sizes in different economic sectors. The presented
methodology ensures an accurate assessment but it is labor intensive, thus the development of
software to ensure assessing enterprise IA is quite acute and relevant.
28
References
Levchenko, Ya. (2018). Assessing and ensuring enterprise investment attractiveness in the context or
regional development. Doctoral dissertation. - Vilnius: Mykolas Romeris University, 2018. 216 р.
ISBN 978-9955-19-910-6 (internet) ISBN 978-9955-19-911-3 (printed)
The BPCC economic debate on the investment attractiveness of Podkarpacie in Rzeszow. –
Avaliable at: http://bpcc.org.pl/en/event-coverages/the-bpcc-economic-debate-on-the-investment-
attractiveness-of-podkarpacie-in-rzeszow
European Commission (2016). Growth. Industry. Competitiveness. – Avaliable at:
http://ec.europa.eu/growth/industry/competitiveness/index_en.htm
L.S. Valinurova, O.B. Kazakova, N.A. Kuzminykh, 2016. Management of investment attractiveness
of municipalities: theory and practice: monograph. Upravlenie investitsionnoy privlekatelnostyu
munitsipalnyih obrazovaniy: teoriya i praktika: monografiya. Moscow: Rusains Publ.
Assessment of economic efficiency of investments. Otsenka ekonomicheskoy effektivnosti
investitsiy. [online] Available at:
<https://books.google.com.ua/books?id=FSwJEZ_AFCMC&printsec=frontcover&hl=ru#v=onepage
&q&f=false>. [Accessed 12 December 2016].
Kalinesku T.V., 2013. The provision of investment attractiveness of the enterprise: monograph.
Obespechenie investitsionnoy privlekatelnosti predpriyatiya: monografiya. Lugansk: SNU im. V.
Dalya Publ.
Stefan Anamari-Beatrice, 2014. Real options a solution for evaluating Public Private Partnerships,
Emerging Markets Queries in Finance and Business, Procedia Economics and Finance 15, pp. 583 –
586.
Rolik Y. A., 2013. A Complex Approach to Evaluating the Innovation Strategy of a Company to
Determine its Investment Attractiveness, 9th International Strategic Management Conference,
Procedia - Social and Behavioral Sciences 99, pp. 562 – 571.
Nizielska, A. (2012). The criteria, instruments, and determinants of investment attractiveness of
Silesia: experts opinions. Journal of economics & management, 8, 53–70.
Dr. Amit Mittala, Dr. Deepika Jhamb, 2016. Determinants of Shopping Mall Attractiveness: The
Indian Context, Fifth international conference on marketing and retailing (5TH INCOMaR) 2015,
Procedia Economics and Finance 37, pp. 386 – 390.
Bogdan Rębiasz, Andrzej Macioł, 2014. Hybrid data in the multiobjective evaluation of investments,
18th International Conference on Knowledge-Based and Intelligent Information & Engineering
Systems - KES2014, Procedia Computer Science 35, pp. 624 – 633.
Levchenko, Ya. S. (2018). Overview the factors influencing investment attractiveness. International
Scientific Conference. From the Baltic to the Black Sea: the Formation of Modern Economic Area:
Conference Proceedings, August 24th, 2018. Riga, Latvia: Baltija Publishing, 48-51
29
Grinko А. Forming concept of intellectualization information provision of managing an enterprise /
Alla Grinko, Tetiana Bochulia, Paul Grynko, Iryna Yasinetska, Iaroslava Levchenko // Eastern-
European Journal of Enterprise Technologies . – 2017. – Vol. 5, № 3 (89) - P. 4-14. – Avaliable at:
http://journals.uran.ua/eejet/article/view/111859
Bruneckienė, J., Zykienė, I., Stankevičius, V. (2016). Critical analysis of city attractiveness factors in
Lithuania – Poland cross-border regions: the viewpoints of businessmen and youth. Journal of
Geography, Politics and Society, 6 (2), 45–58.
Rębiasz, B., Macioł, A. (2014). Hybrid Data in the Multiobjective Evaluation of Investments.
Procedia Computer Science, 35, 624–633. doi: 10.1016/j.procs.2014.08.144
Stock market infrastructure development agency of Ukraine(SMIDA). – Avaliable at:
https://smida.gov.ua
Bahatohaluzeva statystychna informatsiia / Rehionalna statystyka. – Avaliable at:
http://www.ukrstat.gov.ua
Andriushchenko К. Process of resources provision management of the enterprise's activity with
consideration of gender factor / Kateryna Andriushchenko, Dmytro Stefanyshyn, Mykhailo
Sahaidak, Mariia Tepliuk, Olena Buchynska, Elena Rozmetova, Tetyana Marusei, Iaroslava
Levchenko, Iryna Smyrnova, Tetiana Zhytomyrska // Eastern-European Journal of Enterprise
Technologies . – 2018. – Vol. 6, № 3 (96) - P. 6-19. – Avaliable at:
http://journals.uran.ua/eejet/article/view/150799
30
EVALUATION OF THE UNEMPLOYMENT RATE ANNOUNCEMENT IMPACT ON EURO
STOXX 50 INDEX RETURNS BASED ON SEMI-STRONG EFFICIENT MARKET
HYPOTHESIS
Aleksejus SOSIDKO
Faculty of Economics and Business,
Mykolas Romeris University, Ateities str. 20,
Vilnius, Lithuania
E-mail: [email protected]
Ligita GASPARENIENE
Faculty of Economics and Business,
Mykolas Romeris University, Ateities str. 20,
Vilnius, Lithuania
E-mail: [email protected]
Abstract: In this paper are evaluating the impact of unemployment rate announcement on the Euro Stoxx
50 index returns on the basis of a semi-strong efficient market hypothesis. Analyzing and summarizing
previous researches of semi-strong market efficiency find, that there are various studies analyzing the
returns of stock market depending on corporate financial statements announcement, but there are only a
few that analyze the returns of stock market of macroeconomic announcement, and especially
announcement about the unemployment rate. In this study the semi-strong effective market hypothesis is
determined in a very short time interval of 5 minutes from 11:00 to 11:04, therefore the MKAR, RAR
methods to determine the Euro Stoxx 50 index returns are used. The originality of the study is that the
values of the models were calculated on the basis of high frequency data, not daily data. In this empirical
research non-zero values of MKAR, RAR models were obtained, which indicate that in the analyzed
4/4/2018 - 1/4/2019 period the Euro Stoxx 50 index is not a semi-strong effective based on of
unemployment rate announcement. It was also found that Euro Stoxx 50 index returns on the fifth minute
(11:04) after the unemployment rate announcement realize (11:00) can earn more, or less than the
comparable stock market indexes returns.
Keywords. Unemployment rate, Euro Stoxx 50, abnormal returns, stock, semi-strong efficient market
hypothesis.
31
Introduction
All researches on the topic of an effective market hypothesis (EMH) until 1965 can be called the
theory of random walk. The first empirical studies in this direction were performed by Regnault (1863),
Rayleigh (1880), Venn (1888), Bachelier (1900), Barriol (1908), Dibblee (1912), Keynes (1923), Cowles
(1933), Working (1934). ), Osborne (1959), Cootner (1964), Fama (1965), who tested the theory of
random walk and found that stock price changes were independent. The above-mentioned studies led to
the defined of an EMH theory. The first scientist to did that was Eugene Fama in year 1965. Based on his
study in "The Behavior of Stock Market Prices", scientist says that stock prices reflect all the information,
and trading on the basis of this information will not produce abnormal returns.
In year 1970 Fama in his work "Efficient capital markets: a theoretical and empirical review of
work" according to critic’s remarks, EMH has divided into three forms of efficiency: weak, semi-strong
and strong. According to Klimašauskienė and Moščinskienė (1998) these forms differ from each other in
terms of the amount of information and possibilities to get it.
In this work the semi-strong efficiency in Euro Stoxx 50 market will be studied. This semi - strong
hypothesis says that stock prices already reflect all historical data along with all publicly available
information. According to Klimašauskienė and Moščinskienė (1998), if the market is semi-strong
efficient, its participants will not be able to earn abnormal returns by using all historical data and publicly
available information (information on dividends, income, stock shedding, balance sheet items, etc.).
Recently reasearches (Logeswary, Thirunavukkarasu (2019), Alekneviciene, Kviedaraitiene,
Alekneviciute (2018), Andrade, Santos (2017), Mackey, Macon (2017), Woodard, Bacon (2015),
Mallikarjunappa, Dsouza (2014), Ferrara, Bacon (2014), Westfall (2010), Sharma (2009),
Mallikarjunappa, Manjunatha (2009)) has shown that there are various studies analyzing the returns of
stock market depending on corporate financial statements announcement, but there are only a few that
analyze the returns of stock market of macroeconomic announcement, and especially announcement about
the unemployment rate.
The problem of the research. What kind of models can be used to determine semi - strong efficient
market and what is the abnormal returns of the Euro Stoxx 50 after 5 minutes when unemployment rate
announcements occurs?
The purpose of the research. Determine whether Euro Stoxx 50 index is semi - strong efficient
when unemployment rate announcements occurs and calculate its abnormal returns after 5 minutes
32
The objectives of the research:
1. Determine which models can be used to check whether the stock market is semi - strong
efficient.
2. Determine whether Euro Stoxx 50 index is semi - strong efficient when unemployment rate
announcements occurs.
3. Calculate Euro Stoxx 50 abnormal returns after 5 minutes when unemployment rate
announcements occur.
The methods of the research:
1. Systematic analysis of the scientific literature. 2. Comparative analysis. 3. The method of
multicriteria evaluation. 4. Correlation analysis. 5. Analytical-logical method. 6. High frequency data
analysis
1. Analysis of scientific literature of the semi-strong efficient market hypothesis
According to Fama (1991) semi – strong efficient hypotheses can be divided into two groups:
1. Research to predict future stock price changes using historical and publicly available information.
The most common method of this analysis is time series analysis.
2. Event studies that analyze how quickly stock prices adjust to new information about important
economic events in the selected market. Cleary, Atkinson and Drake (2013) are saying that event research
is conducted by comparing the expected profitability model with the real return on assets after the
company announces new information. If the difference between expected and actual profitability is
statistically significant, it will be considered that the market responds quickly to new information and
investment decisions based on this information after it has entered the market.
Below is an overview of event studies.
Table 1. Semi-strong market efficiency recent researches
33
Years Authors Research
stock
market
Research Object Is market
semi –strong
efficiency?
2019 Logeswary, Thirunavukkarasu Sri Lanka FS* of companies No
2018 Alekneviciene, Kviedaraitiene,
Alekneviciute
Baltic
States
FS* of companies No
2017 Andrade, Santos Brazil FS* of companies Yes
2017 Mackey, Macon USA Stock repurchase & issue Yes
2015 Woodard, Bacon USA Unemployment rate No
2014 Mallikarjunappa, Dsouza India FS* of companies No
2014 Ferrara, Bacon USA Merger of companies Yes
2010 Westfall USA Stock split of companies Yes
2009 Sharma India Consolidation, change of
managers, acquisitions
No
2009 Mallikarjunappa, Manjunatha India Dividends of companies No
*Financial statements
Source: compiled by the author based on the above sources
In these researches the semi-strong efficiency market hypothesis is both confirmed and rejected
(see Table 1). In some markets, stock prices move according by random walk theory (YES), so the
financial market participants will not earn abnormal returns. In other markets stock prices are moving
according to certain trends that can generate abnormal returns (NO).
In order to confirm or deny the existence of a semi-strong efficiency hypothesis in a stock markets,
it is necessary to select research methods that can prove empirically whether financial market
participants can earn abnormal returns in the stock market. It is therefore appropriate to learn the
methods used by scientists in their recent research.
34
Table 2. Methods for determining the semi-strong efficiency market in recent researches
Years Authors Models in the research
2019 Logeswary, Thirunavukkarasu - Average abnormal returns model (AAR)
- Cumulative average abnormal returns model (CAAR)
- Parametric T test
2018 Alekneviciene, Kviedaraitiene,
Alekneviciute
- Average abnormal returns model (AAR)
- Cumulative average abnormal returns model (CAAR)
- Patell’s, BMP tests
2017 Andrade, Santos - Regression model
2017 Mackey, Macon - Risk adjusted model (RAR)
- Parametric T test
2015 Woodard, Bacon - Risk adjusted model (RAR)
2014 Mallikarjunappa, Dsouza - Market adjusted model (MAR)
- Parametric T test
2014 Ferrara, Bacon - Risk adjusted model (RAR)
2010 Westfall - Risk adjusted model (RAR)
2009 Sharma - Market-adjusted model (MAR)
2009 Mallikarjunappa, Manjunatha - Average abnormal returns model (AAR)
- Cumulative average abnormal returns model (CAAR)
Source: compiled by the author based on the above sources
A comprehensive scientific literature review found that average abnormal returns (AAR),
cumulative average abnormal returns (CAAR), market adjusted returns (MKAR), risk adjusted returns
(RAR), regression models are the most commonly used in the stock markets to check the existence of a
semi-strong market efficiency hypothesis. The Student's T-test is used to check the statistical significance
of the results obtained. Since in this study the semi-strong effective market hypothesis is determined in a
very short time interval of 5 minutes from 11:00 to 11:04 therefore the MKAR, RAR methods were
chosen. Student's T-test was chosen for statistical significance.
35
2. Evaluation of the unemployment rate announcement impact on Euro Stoxx 50 index returns
based on semi-strong efficient market hypothesis methodology
The Euro Stoxx 50 index was chosen for this study. It is one of the most liquid indices in the
world, consisting of shares in the 50 largest capitalized euro area companies from Australia, Belgium,
Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal and Spain.
Determining whether the Euro Stoxx 50 index is a semi-strong effective was chosen the Euro
area unemployment rate announcements.
In this research unemployment rate data was obtained from Bloomberg terminal. Data period from
4 April 2018 to April 4 2019 (13 Month).
Table 3: Distribution of the statistical data intervals for the analyzed determinants
Variable Data frequency (GMT +2) Number of data
Unemployment rate (%) 11:00 26
Euro Stoxx 50 (€) Open 11:00 and 11:04 Close 512
Euro Stoxx Total market (€) Open 11:00 and 11:04 Close 512
Source: compiled by the author
In the next step, zero and alternative hypotheses are raised. Hypothesis 𝐻0 states that there are no
abnormal returns in the event window, while alternative 𝐻1 indicates that there is an abnormal return in
the event window.
𝐻0:𝑀𝐾𝐴𝑅𝑖𝑡 = 0
𝐻1:𝑀𝐾𝐴𝑅𝑖𝑡 ≠ 0
𝐻0: 𝑅𝐴𝑅𝑖𝑡 = 0
𝐻1: 𝑅𝐴𝑅𝑖𝑡 ≠ 0
36
Event window starts at 11:00 and ends at 11:04 (5-minute time interval).
Then we process the resulting data from Table 3 and calculate the MKAR and RAR values for each
event window according to the formulas in Table 4. All calculations are performed using Excel platform.
Table 4: Types and explanation of semi - strong EHR determination models
Model Purpose of the model according
to scientific literature
The purpose of the model in this study
𝑀𝐾𝐴𝑅𝑖𝑡 = 𝐿𝑂𝐺(𝑅𝑖𝑡) −
𝐿𝑂𝐺(𝑅𝑚𝑡)
The purpose is to determine
whether the change in the return
on securities on the day of the
event is higher / lower than the
change in the market (index)
return on the day of the event.
Determines whether the Euro Stoxx
50 index returns after 5 min when the
news of the unemployment rate
appears to be higher / lower than the
Euro Stoxx Total market index
returns in the previous days within the
same 5 min interval.
𝑅𝐴𝑅𝑖𝑡
= 𝐿𝑂𝐺(𝑅𝑖𝑡) − 𝐿𝑂𝐺(𝛼𝑖
+ 𝛽𝑖 × 𝑅𝑚𝑡)
The purpose is to determine
whether the change in the return
on securities on the day of the
event is higher / lower than the
change in the market (index)
return on the day of the event
with additional elements in the
regression model.
Determines whether the Euro Stoxx
50 index returns after 5 min when the
news of the unemployment rate
appears is higher / lower than the
return of the Euro Stoxx Total market
index in the previous days, taking into
account the regression model
elements within the same 5 min
interval.
(𝑅𝑖𝑡) - Close 11:04/open 11:00 of Euro Stoxx 50 index prices changes (%) event day
(𝑅𝑚𝑡) - Mean (Close 11:04/open 11:00) of Euro Stoxx Total Market index prices changes (%) starts after next
day and end till next unemployment event day
𝛼𝑖 - Values obtained between Euro Stoxx 50 and Euro Stoxx Total Market starts after next day and end till
next unemployment event day
𝛽𝑖 - Values obtained between Euro Stoxx 50 and Euro Stoxx Total Market starts after next day and end till
next unemployment event day
Source: compiled by the author according table 2
37
Models values are calculated by logarithmic returns on the Euro Stoxx 50 and Euro Stoxx Total
Market indices.
The statistical significance of the obtained model values is checked using the Student's t-test. If
Student’s t-test with 95% confidence intervals greater than 2.093 (19 df) or less than -2.093 (19 df) are
obtained, then the null hypothesis is rejected and the alternative is accepted. This means that the MKARs
and RARs values received in the event window are unequal to zero and are statistically significant.
At the end of this empirical study, it was assumed that the short-term returns volatility of the Euro
Stoxx 50 index is driven by unemployment rate announcements surprises. This assumption is tested by
calculating the correlation coefficient between the returns after 5 minutes on the day then event occurred
and the difference between the actual and forecasted unemployment rate values.
3. Results of empirical research
The Euro Stoxx 50 abnormal return analysis (see Table 5) shows that none of the MKAR, RAR
values are zero during the analysis period. This means that market participants could earn more or less
than the market average. However, checking the statistical significance of these values revealed that the
values of the models on the event days - 02.05.2018, 31.05.2018, 2018.11.11 are not statistically
significant. Therefore, it can be argued that on these days, the semi-strong market hypothesis for the Euro
Stoxx 50 market when the unemployment rate appears is valid, so market participants could not earn more
or less than the market average.
Table 5: MKAR, RAR and Student's t-test values
Announcement date Times open Times close MKAR T-test RAR T-test
4/4/2018 11:00 11:04 -0,071% -6,44 -0,074% -6,67
2/5/2018 11:00 11:04 0,004% 0,46 0,003% 0,37
31/5/2018 11:00 11:04 -0,011% -0,94 -0,009% -0,79
2/7/2018 11:00 11:04 0,033% 3,33 0,028% 2,85
31/7/2018 11:00 11:04 0,077% 10,90 0,077% 10,88
31/8/2018 11:00 11:04 -0,033% -5,11 -0,036% -5,56
1/10/2018 11:00 11:04 0,077% 9,56 0,080% 9,98
31/10/2018 11:00 11:04 0,104% 7,10 0,102% 6,97
30/11/2018 11:00 11:04 0,019% 1,42 0,019% 1,47
38
9/1/2019 11:00 11:04 -0,099% -7,08 -0,098% -6,97
31/1/2019 11:00 11:04 -0,142% -13,14 -0,147% -13,60
1/3/2019 11:00 11:04 0,033% 3,87 0,032% 3,77
1/4/2019 11:00 11:04 -0,026% -3,58 -0,028% -3,85
Index price data is distributed by normal distribution.
Source: compiled by the author.
The contents of Table 5 can also be represented by figure 1.
Figure 1: MKAR, RAR values
Source: compiled by the author.
From the figure 1 it is more evident that on days when MKAR, RAR values are very low, their
statistical significance is accordingly insignificant. Thus, on these dates 02/05/2018, 31/05/2018,
30/11/2018, MKAR, RAR null hypothesis is not rejected, and on other days null hypothesis is rejected
and the alternative hypothesis is rejected
Figure 2 shows the returns of the Euro Stoxx 50 index on the day of the event, 5 minutes after the
news of the unemployment rate appears, and the average returns of the Euro Stoxx 50 and Euro Stoxx
Total market indices until the next news release of the unemployment rate.
Figure 2: Comparison of indexes LOG returns
39
Source: compiled by the author.
Figure 2 shows that on the day of the event, 5 minutes after the news of the unemployment rate
appears, trading on the Euro Stoxx 50 index can earn more or less than the comparable market indices.
From the author's point of view, according to the results of previously named scientists, such differences
in returns may be driven by market expectations, with the news of the unemployment rate appearing at
the time. For example, economists and analysts could predict the unemployment rates values before the
news appeared. Published forecasts shape the perception of other financial market participants about the
significance of unemployment. Therefore, when the actual value of the news comes out, which does not
fully meet the expectations of the market participants, one can expect larger or smaller fluctuations in the
price of the Euro Stoxx 50 index than usual.
In order to confirm or reject this approach, the calculations referred to in appendix I and II have
been carried out. The results are shown in Figure 3.
40
Figure 3: Comparison between indexes LOG returns and unemployment rate announcements surprises
In this figure, the yellow curve shows the difference between the actual and the forecast value of the
unemployment rate. It is not clear from the figure how much unemployment rate surprises can explain the
price returns of the Euro Stoxx 50 index, so it is appropriate to determine the correlation coefficient of
these variables. The resulting correlation coefficient is 9.76% (calculation on Appendix 2), which means
that the surprises analysis performed during the analyzed period explains about ~ 10% movement of Euro
Stoxx 50 returns. The significance of the correlation coefficient is low, hence the view that surprises in
this paper can explain why Euro Stoxx 50 returns are higher or lower than market averages can be ruled
out.
41
Conclusions and recommendations
1. A comprehensive scientific literature review found that average abnormal returns (AAR),
cumulative average abnormal returns (CAAR), market adjusted returns (MKAR), risk adjusted returns
(RAR), regression models are the most commonly used in the stock markets to check the existence of a
semi-strong market efficiency hypothesis. The purpose of these models is to determine whether the change
in the stock return on the day of the event is greater or less than the day of the market return on the event
day. In this study the semi-strong effective market hypothesis is determined in a very short time interval
of 5 minutes from 11:00 to 11:04, therefore the MKAR, RAR methods to determine the Euro Stoxx 50
index returns are used. The Student's T-test was selected for statistical significance
2. In the empirical research we show that none of the MKAR, RAR values are zero during the
analysis period. This means that market participants could earn more or less than the market average.
However, checking the statistical significance of these values revealed that the values of the models on
the event days - 02.05.2018, 31.05.2018, 2018.11.11 are not statistically significant. Therefore, it can be
argued that on these days, the semi-strong market hypothesis for the Euro Stoxx 50 market when the
unemployment rate appears is valid, so market participants could not earn more or less than the market
average.
3. The analysis of Euro Stoxx 50 index returns shows that the average return of the index in the fifth
minute (11:04) is 0.004% lower than on other days in same index or about on average 0.003% lower than
the selected Euro Stoxx Total Market index. However, analyzing each event separately, one can see a
large gap between the Euro Stoxx 50 and the Euro Stoxx Total Market index returns. From the author's
point of view such differences in returns can be driven by market surprises with the news of the
unemployment rate appearing at the time. Calculations showed that the unemployment rate surprises
explain only ~ 10% of the Euro Stoxx 50 index return movement. Hence the approach that surprises may
explain why the Euro Stoxx 50 returns are higher or lower than the market averages in this study is
rejected. It is recommended continue research on a similar topic by expanding sample of the year to
include other economic news or capturing index returns within 10 or 15 minutes.
42
References
Alekneviciene, V., Kviedaraitiene, L., Alekneviciute, E. Semi-Strong Form Efficiency in the Baltic Stock
Markets under Changing Economic Situation. Engineering Economics, 2018, 29(5), p. 495–506
Andrade, L.L., Santos, N.S.S. Accounting information and stock returns: evidences from Brazil.
International journal of management, accounting and economics, 2017, Vol. 4, No. 5.
Bachelier, L. Theorie de la speculation, Annales Scientifiques de l’Ecole, 1990, Normale Supérieure 17:
21–86.
Barriol, A. Theorie et Pratique des Operations Financie`res, O. Doin, 1908, Paris
Cleary, W. S., Atkinson, H. J. Drake, P. P. Market Efficiency. Equity and Fixed Income, 2013, p. 119-
151.
Cootner, P. H. The Random Character of Stock Market Prices. Cambridge, MA, 1964, The MIT Press.
Cowles, A. Can stock market forecasters forecast? Econometrical 1(3), 1933, 309–324.
Dibblee, G. B. The Laws of Supply and Demand, in The Laws of Supply and Demand, 1912, London:
Constable.
Fama, E. F. Efficient Capital Markets. Journal of Finance, nr. 1991, 46 (5), p. 1575–1617.
Fama, E. F. Efficient capital markets: A review of theory and empirical work, The Journal of Finance,
1970, 25(2): 383–417.
Fama, E. F. The behavior of stock-market prices, Journal of Business, 1965, 38(1): 34–105.
Ferrara, D. Bacon, F. Merger and acquisition announcements effect on acquiring company’s stock price:
a test of market efficiency. Proceedings of the Academy of Accounting and Financial Studies, 2014,
Volume 19, Number 1
Keynes, J. M. Some aspects of commodity markets, Manchester Guardian Commercial: European
Reconstruction Series 13, 1923, pp.784–786.29.
Klimašauskienė, D., Moščinskienė, V. Lietuvos kapitalo rinkos efektyvumo problema // Pinigų studijos,
1998, Nr. 2, p. 25-34
Logeswary, S., Thirunavukkarasu, V. Stock Market Reaction to Impairment Announcement of Listed
Manufacturing Companies in Sri Lanka. Journal of Accounting & Marketing. J Account Mark 2018, 7:4
Mackey, A.M., Bacon, F.W. Signaling with issues and repurchases: a test of semi-strong form market
efficiency. Journal of Business and Behavioral Sciences, 2017, Vol 29, No 1.
Mallikarjunappa, T., Dsouza, J.J. A Study of Quarterly Earnings Announcement and Stock Price
Reactions. IUP Journal of Applied Finance, 2014, (95-106). India.
Mallikarjunappa, T., Manjunatha, T. Stock price reactions to dividend announcements. Journal of
management & public policy, 2009, Vol. 1 No. 1 pp 43- 56
Osborne, M. F. M. Brownian motion in the stock market, Operations Re-search, 1959, 7(2): 145–73.
Rayleigh, L. On the resultant of a large number of vibrations of the same pitch and of arbitrary phase,
1880, Philosophical Magazine 10: 73–78.
Regnault, J. Calcul des Chances et Philosophie de la Bourse, Mallet-Bachelier et, in Calcul des Chances
et Philosophie de la Bourse, Mallet-Bachelier et. Castel, 1863, Paris.
Sharma, A. Impact of public announcement of open offer on shareholders return: an empirical test for
efficient market hypothesis. The IUP Journal of applied finance, 2009, Vol. 15, No. 11
43
Venn, J. The Logic of Chance, an Essay on the Foundations and Province of the Theory of Probability
with Special References to its Logical Bearings and its Application to Moral and Social Sciences, and to
Statistics, third edition, 1888, London: MacMillan
Westfall, T. Stock split announcements: a test of market efficiency. Allied academies international
conference. 2010
Woodard, J.H., Bacon, F.W. Government shutdown: a test of market efficiency. Journal of business and
behavioral sciences, 2015, Vol 27, No 2.
Working, H. A random-difference series for use in the analysis of time series, Journal of the American
Statistical Association, 1934, 29(185): 11–24.
44
Appendix 1
Unemployment rate values
Date Unemployment rate (%)
Actual
Unemployment rate (%) Forecast
(median)
Surprises (Actual –
Forecast)
4/4/2018 8,50% 8,50% 0,00%
2/5/2018 8,50% 8,50% 0,00%
31/5/2018 8,50% 8,40% 0,10%
2/7/2018 8,40% 8,50% -0,10%
31/7/2018 8,30% 8,30% 0,00%
31/8/2018 8,20% 8,20% 0,00%
1/10/2018 8,10% 8,10% 0,00%
31/10/2018 8,10% 8,10% 0,00%
30/11/2018 8,10% 8,00% 0,10%
9/1/2019 7,90% 8,10% -0,20%
31/1/2019 7,90% 7,90% 0,00%
1/3/2019 7,80% 7,90% -0,10%
1/4/2019 7,80% 7,80% 0,00%
Source: compiled by the author.
45
Appendix 2
Correlation between Euro Stoxx 50 returns and unemployment rate surprises
Date LOG (Close 11:04/open 11:00) of
Euro Stoxx 50 index returns (%) event
day
Unemployment rate Surprises (Actual –
Forecast)
4/4/2018 -0,061% 0,00%
2/7/2018 0,056% 0,10%
31/7/2018 0,084% -0,10%
31/8/2018 -0,030% 0,00%
1/10/2018 0,056% 0,00%
31/10/2018 0,085% 0,00%
9/1/2019 -0,072% -0,20%
31/1/2019 -0,122% 0,00%
1/3/2019 0,035% -0,10%
1/4/2019 -0,048% 0,00%
Correlation * 9,76%
* Correlation is calculated only for statistically significant MKAR, RAR values.
Source: compiled by the author.
46
INTELLECTUAL CAPITAL IN THE LISTED COMPANIES OF THE BALTIC STATES
Irena MAČERINSKIENĖ
Mykolas Romeris University, Ateities str. 20,
LT-08303, Vilnius, Lithuania
Simona SURVILAITĖ
Vilnius University, Faculty of Economics &
Business Administration
Saulėtekio ave. 9, LT-10222, Vilnius,
Lithuania
Abstract: The theory of intellectual capital is vast and there are various angles and approaches to
investigate it. Nowadays scientific publications about the intellectual capital have flooded academic
journals, so this stage of the development of intellectual capital can be called “The intellectual capital
era”. Nevertheless, there are so many opinions about it that it becomes difficult to investigate
intellectual capital in a consistent and thorough way. The main issue arises in the evaluation of
intellectual capital. This article is designed in order to provide a unified intellectual capital
description, its structure and the methodology of intellectual capital evaluation. The main approach
of this scientific research is regarding the listed companies of the Baltic States. The aggregated
intellectual capital index is calculated for the listed companies of the Baltic States and the main
tendency of its influence on market value. In addition to this, main factors that have an impact on the
market value of the listed companies of the Baltic States are revealed.
Keywords: intellectual capital; human capital; structural capital; juridical capital; relational capital;
market value
47
Introduction
Nowadays the most frequent goal of successful leaders of various companies is to gain a
competitive advantage and to use as little funds as possible. Although the concept of intellectual
capital has been widely investigated for the last decade, but it still does not have a common widely
used description nor structure or assessment method. The concept of intellectual capital is popular
amongst scientists as it is a complex, dynamic, multi-criteria object that definitely adds value and
positive results are observed in the majority of studies (Roos & Roos, 1997; Stewart, 1997;
Edvinsson & Malone, 1997; Sullivan, 1998; Taylor, 1998; Bontis, 1998; Nahapiet & Ghoshal, 1998;
Bukh & Johanson, 2003; Bozzolan et al., 2003; Firer & Williams, 2003; Riahi-Belkaoui, 2003;
Guthrie et al., 2004; Chen et al., 2005; Sanchez & Elena, 2006; Shiu, 2006; Zhang et al., 2006; Peng
et al., 2007; Sonnier et al., 2007; Boesso & Kumar, 2007; Sonnier, 2008; Whiting & Miller, 2008;
Ting & Lean, 2009; Yi & Davey, 2010; Zéghal & Maaloul, 2010, 2011; Alcaniz et al., 2011; Rashid
et al., 2012; Rahman, 2012; Branswijck & Everaert, 2012; Molodchik et al., 2012; Cricelli, Greco &
Grimaldi, 2013; Beattie & Smith, 2013; Bontis et al. 2015; Nimtrakoon, 2015; Bini et al., 2016;
Sachpazidu-Wojcicka, 2017; Vlacsekova & Mura, 2017). In the modern world, intellectual capital
has become one of the most valuable assets of an organization, region or state (Markhaichuk &
Zhuckovskaya, 2019, p. 90). There are various approaches regarding company’s intellectual capital,
its main structure, features. Many researchers use multiple methods to assess intellectual capital and
the impact it has on a firm’s market value. That is why the research problem of this article is how to
assess company’s intellectual capital and what impact it has on its market value. The object of the
research is the impact of a respective company’s intellectual capital on its market value. The purpose
of the article is as follows: to examine various scientific approaches of the company’s intellectual
capital and to assess its impact on the market value. What is more, the aim of this article is to check
and test the effectiveness of the abovementioned model using an example of the Baltic States listed
companies. Methods used for the research are as follows: expert evaluation, exploratory factor
analysis, a pair-wise multiple correlation, and regression analysis.
Theory of intellectual capital and its influence on market value
48
Nowadays there are numerous concepts related to the word “intellectual”: intellectual property,
intellectual tools, intellectual approach, intellectual challenges, intellectual debates, intellectual
enquiries, and even intellectual jokes. The main goal of company owners is to concentrate on
intellectual capital, but the concept itself is not yet developed in a unified and harmonised way. It can
be noted that the theory of intellectual capital can be divided into four main stages according to the
features and research methodologies being used by various scientists (Figure 1).
Source: Mačerinskienė and Survilaitė, 2019
Figure 1. The development stages of the Intellectual Capital concept
The figure demonstrates the grouping of scientific literature regarding intellectual capital and
main criteria of its formation. It was build on own assumptions based on Tuckman’s (1965)
group/team formation theory stages. The initial stage can be considered as the notion of intellectual
capital, the appearance of the concept itself in 1969 when Galbraith mentioned it for the first time in
an email to Michael Kalecki. A couple of years later, Cooper and Sherer (1984) started to discuss the
criteria of a concept that is invisible, but provides visible benefits. The figure shows how the research
regarding intellectual capital changed during the years and the major findings that researchers were
able to publish.
The main difficulty lies in defining intellectual capital in an appropriate and thorough way.
Many researchers accentuate different features of intellectual capital and there is a vast theory about
this matter. Depending on the type of research, intellectual capital can be investigated using the
49
accounting approach, where intellectual capital of a company is captured in balance sheets and other
financial statements (Dumay & Cuganesan, 2011; Dameri & Ricciardi, 2015; Dumay, 2016;
Rahman, 2012; Nimtrakoon, 2015). Other authors state that intellectual capital is a strategic part of
every company as it is the total amount of knowledge that exists in a company (Ulrich, 1998; Kaplan
& Norton, 2001). However, another group of authors (Vaičekauskaitė, 2014; Low et al., 2015)
suggest that intellectual capital is produced in universities and their main goal is to teach society and
managers to use it in an effective and efficient way. Economists (Stewart, 1997; Edvinsson &
Malone, 1997; Daum, 2003; Cezair, 2008; Wang, 2008; Nam & Pardo, 2011; Rahman, 2012;
Mention & Bontis, 2013; Strong, 2014; Adams, 2015; Flower, 2015; Nimtrakoon, 2015; Dumay,
2016) in turn accentuate that intellectual capital is the tool of asset creation, knowledge, information,
intellectual property, experience and other elements that are used in a company to have a competitive
advantage.
It is proposed (Mačerinskienė & Survilaitė, 2019) that company’s intellectual capital can be
described as the aggregate of intangible resources a company has at its disposal that enables a
company to operate at its best, creates a competitive advantage and increases market value. In this
scientific paper it is suggested that intellectual capital of the company consists of four main structural
parts: human capital, structural capital, juridical capital, and relational capital. The main elements
are presented in the figure below (Figure 1).
Intellectual capital of a company
Human capital
Employees
Employee education
Investments in employees
Structural capital
Corporate identity
Financial leverage
Selling, General & Administrative
Costs (SG&A)
Juridical capital
Intangible assets
Legally protected
information
Relational capital
Social characteristics of a company
Relations
Relational expenses
50
Source: Mačerinskienė and Survilaitė, 2019
Figure 2. The structure of the intellectual capital of a company
To sum up, it depends what type of intellectual capital structure is being chosen for the
research. Many authors investigate intellectual capital concept in various ways and there is no
unified description, main structure, and features. Depending on the sector, type, size of the company
different approaches can be used to investigate company’s intellectual capital. The following
sections of this article provide the methodology on how to investigate company’s intellectual capital
and how to assess the impact of intellectual capital on a company’s market value.
Methodological approach of the evaluation of intellectual capital
Intellectual capital of any respective company is a complex, dynamic, and diverse multi-
criteria variable that does not have any widely accepted and unified assessment methodology. In this
scientific article it is proposed to use intellectual capital structural parts’ approach by composing an
aggregated intellectual capital index. In order to do that, an aggregated index of every structural part
should be calculated: human capital, structural capital, juridical capital, and relational capital. The
question arises: how to evaluate each specific intellectual capital structural part? Scientific literature
review revealed that different authors suggest various indicators for each intellectual capital
structural part. In order to choose the most appropriate ones the following principles were applied
(Pakalniškienė, 2012; Užienė, 2014): frequency, objectivity, comparability, publicity, same
methodology of calculation, and repeatability. Indicators that are confidential, any subjective criteria
were not included in the research (for instance: emotional intelligence, employee satisfaction,
motivation, abilities, competences and skills, ability to deal with crises, reputation, personal
relationships of top management, etc.).
Human capital indicators that were selected for further research are as follows: number of
employees (Hall, 1992; Edvinsson & Malone, Skandia, 1997; Roos et al., 1998; Sveiby, 1997; Luthy,
1998; Andriessen, 2004; Huang & Liu, 2005; Dalkir et al., 2007; Peng et al., 2007; Huang & Wang,
2008; Baiburina & Golovko, 2008; Nogueira et al., 2010; Huang & Wu, 2010; Shakina & Barajas,
2012, 2014; Branswijck & Everaert, 2012; Molodchik et al., 2012); education of employees
(Edvinsson & Malone, Skandia, 1997); productivity of employees (Edvinsson & Malone, 1997; Fox
51
et al., 2001; Peng et al., 2007; Ahangar, 2010; Shakina & Barajas, 2012, 2014; Molodchik et al.,
2012; Branswijck & Everaert, 2012; Liang et al., 2013; Cricelli, Greco & Grimaldi, 2013); and
personnel costs that consists of two main indicators: expenses per employee and proportional costs
of employee (Edvinsson & Malone, Skandia, 1997).
Structural capital indicators that were selected for further research are as follows: financial
leverage – Nasdaq OMX Baltic indicator calculated for all Baltic States’ companies according to the
same methodology (Edvinsson & Malone, Skandia, 1997; Poletti, 2003; Riahi-Belkaoui, 2003;
Huang & Liu, 2005; Liang et al., 2011; Branswijck & Everaert, 2012; Molodchik et al., 2012;
Shakina & Barajas, 2014); Selling, General & Administrative (SG&A) – Nasdaq OMX Baltic
indicator calculated for all Baltic States’ companies according to the same methodology (Edvinsson
& Malone, Skandia, 1997; Peng et al., 2007; Branswijck & Everaert, 2012; Molodchik et al., 2012;
Cricelli, Greco & Grimaldi, 2013); company’s identity that consists of two main indicators:
company’s age (Edvinsson & Malone, Skandia, 1997; Huang & Wang, 2008; Huang & Wu, 2010;
Branswijck & Everaert, 2012; Molodchik et al., 2012; Shakina & Barajas, 2014) and strategy
implementation (Edvinsson & Malone, Skandia, 1997; Tseng & Goo, 2005; Peng et al., 2007;
Kamukama et al., 2010; Shakina & Barajas, 2012, 2014; Branswijck & Everaert, 2012; Molodchik et
al., 2012; Cricelli, Greco & Grimaldi, 2013; Chang et al., 2014).
Juridical capital indicators that were selected for further research are as follows: intangible
assets (Edvinsson & Malone, Skandia, 1997; Sellers-Rubio & ’lbez, 2007; Shakina & Barajas, 2012,
2014; Branswijck & Everaert, 2012; Molodchik et al., 2012; Cricelli, Greco & Grimaldi, 2013);
legally protected information that was treated as the number of patents, licences, trademarks –
information was used from the European Patent Office, patent information from the website:
https://worldwide.espacenet.com/ and trademark information from the website:
https://euipo.europa.eu/eSearch/ (Edvinsson & Malone, Skandia, 1997; Tseng & Goo, 2005; Sellers-
Rubio & ’lbez, 2007; Shakina & Barajas, 2012, 2014; Branswijck & Everaert, 2012; Molodchik et
al., 2012; Cricelli, Greco & Grimaldi, 2013); and characteristics of the company that consists of two
main indicators: location in the capital (Shakina & Bykova, 2011; Shakina & Barajas, 2012, 2014;
Branswijck & Everaert, 2012) and number of subsidiaries (Edvinsson & Malone, 1997, Skandia,
1997; Shakina & Barajas, 2012, 2014; Branswijck & Everaert, 2012; Molodchik et al., 2012;
Cricelli, Greco & Grimaldi, 2013).
Relational capital indicators that were selected for further research are as follows: relational
expenses that are treated as marketing, advertising, representation expenses (Edvinsson & Malone,
Skandia, 1997; Gleason & Klock, 2003; Chen et al., 2005; Sussan, 2012; Branswijck & Everaert,
52
2012; Molodchik et al., 2012; Shakina & Barajas, 2014; Cricelli, Greco & Grimaldi, 2013) and
dissemination of company’s awareness that consists of three main indicators: social networks –
evaluation of the social networks the respective company is developing: the company is assigned 1
point for each active account (Poyhonen & Smedlund, 2004; Osman-Gani & Rockstuhl, 2008;
Stroppa & Spieβ, 2011; Cricelli, Greco & Grimaldi, 2013; Pinto & Araujo, 2016); company’s
website quality – it is evaluated as the total amount of points that a respective company’s website
receives for the following criteria: 1) availability of information to investors; 2) multilingual website
or not; 3) amount of information (more than 5 web pages with active links); 4) design (Shakina &
Bykova, 2011; Shakina & Barajas, 2012, 2014; Branswijck & Everaert, 2012; Molodchik et al.,
2012), and citations in search engines – the website as follows: www.prchecker.info/check_page_
rank.php is used for the evaluation of a respective company, for instance: Agrowill group – 4 out of
10 (Shakina & Barajas, 2012, 2014; Everaert, 2012; Molodchik et al., 2012).
All four aggregated intellectual capital structural parts’ indexes are calculated using
exploratory factor analysis. The data was collected using Latvian, Estonian, and Lithuanian
companies that are listed in Nasdaq Baltic stock exchange. At the time of analysis there were 27
Latvian companies, 17 Estonian companies, and 28 Lithuanian companies listed in Nasdaq Baltic
stock exchange. Five years data was collected for the period from 2011 to 2015 as more recent data
were not available. Moreover, some of the listed companies (“LHV Group”, “Linda Nektar”, “Pro
Kapital Grupp”, “Baltic Telekom”, “HansaMatrix”, “Amber Grid”, “Energijos Skirstymo
Operatorius”, “INVL Baltic Farmland”, “INVL Baltic Real Estate”, “INVL Technology”, “K2 LT”)
did not have enough data as they were newly established. One company was removed from the
research due to zero number of employees (“Trigon Property Development”). Overall, 58 companies
were included in the research: 24 Latvian, 13 Estonian and 21 Lithuanian listed companies. All
companies belong to the small capitalization group.
Aggregated intellectual capital index was calculated using regression analysis method and
when factor values are calculated, the data is automatically standardized. Standardization is needed
to ensure that variables with a large standard deviation are no longer dominant and do not distort the
results, also, data standardization allows comparison between different measurement scales. Simple
additive weighing (SAW) method was used to calculate the aggregate value of the company's
intellectual capital factors. In this research an oblique factor rotation method Promax was chosen. In
order to set weights for intellectual capital structural parts an expert evaluation was conducted.
Results (Mačerinskienė & Survilaitė, 2019) revealed that the highest weight was given to human
capital and the lowest to juridical capital (Kendall’s W value was 0,797 meaning that the model is
53
reliable and can explain 80 per cent of variable variation). Relational capital was on the second and
structural capital on the third place according to the importance.
Exploratory factor analysis was conducted taking into consideration Anti-image matrixes, MSA
(Measure of Sampling Adequacy) values, KMO (Kaiser-Meyer-Olkin) test for sampling adequacy,
and Bartlett’s test. According to Kavaliauskienė (2010), it is not acceptable if KMO is less than 0,5.
Correlation analysis of the intellectual capital structural parts indicators’ revealed that there is no
strong relationship between the pairs of indicators, that means that exploratory factor analysis can be
conducted further. Nevertheless, from the human capital indicators productivity of employees (MSA
– 0,438) was removed from further investigation. From the juridical capital indicators location in the
capital (MSA – 0,434) was removed from further investigation. All indicators from structural and
relational capital met necessary requirements.
To conclude, methodological approach depends on various criteria and research type and size.
The methodology suggested in this article can be used in various sectors for various sizes and types
of companies due to its simple and multi-criteria means.
Results of the intellectual capital evaluation and its influence on market value
The main goal of this article is to reveal if there is a statistically significant relationship
between intellectual capital of the company and its market value. Hypotheses are formulated and the
hypothesis is confirmed if the resulting regression models are statistically significant and consistent
with the criteria. If the resulting regression models are not statistically significant, the null hypothesis
is accepted, which means that there is no statistically significant relationship between intellectual
capital of the company and its market value. Results revealed (Table 1) that there is a statistically
significant relationship between intellectual capital of the company and its market value.
Table 1. The results of the multi-regression model of intellectual capital influence on the
market value of listed companies of the Baltic States
Criteria Listed companies of the
Baltic States
Pair correlation coefficients 0,340
Level of significance of correlation
coefficients
0,000
54
Determination coefficient (R2) 0,583
ANOVA p 0,000
Cook’s distance 0,019
Histogram of standardized residues
P-P graph
Diagram of Standardized Residue and
Regression Estimated Values
Shapiro–Wilk test 0,000
Kolmogorov–Smirnov test 0,000
Durbin–Watson statistic 0,452
According to the results, it is identified that there is a statistically significant relationship
between intellectual capital of the company and its market value. Therefore a linear regression model
can be built. Results revealed that ANOVA p value is less than respective significance level and
determination coefficient (0,583) is suitable for the further review. What is more, Cook’s distance
does not exceed one, so it can be concluded that there is no stand outs in the standardized residual
errors. The histogram of the standardized residuals and the P-P graph show the normality of the
standardized residual errors. There is no pattern in the scatter plot of the diagram of standardized
residue and regression estimated values, suggesting that the data is not heteroskedastic. In addition to
this, the review of the distribution of errors revealed that the values of the Shapiro–Wilk test and
Kolmogorov–Smirnov test criteria have not been distributed according to the normal distribution. As
per Čekanavičius and Murauskas (2014), it is recommended to interpret the results of these tests with
caution because in the case of a large number of observations, the assumption of normality can be
rejected even when the distribution of observations is not significantly different from the normal
distribution. Additionally, it can be assumed that the observations are autocorrelated if the Durbin
and Watson statistic is close to 0 or 4. Thus, the regression model can be described using the
regression equation as follows (Formula 1).
55
BSLCMV = 72275 + 125345𝐼𝐶, (1)
where:
BSLCMV – Baltic States’ listed companies’ market value;
IC – Baltic States’ listed companies’ intellectual capital.
The positive coefficient of the independent variable of the equation indicates that the
aggregated intellectual capital index of the Baltic States’ listed companies has a positive influence on
their market value. The tendency of this linear regression is provided in the graph below (Figure 2).
Figure 2. Dependency between Baltic States’ listed companies’ intellectual capital and their
market value
The graph reveals that those Baltic States’ listed companies that have a larger aggregated
intellectual capital index have a larger market value. Determination coefficient is 0,583 meaning that
the model explains 58 per cent of the Baltic States’ listed companies’ market value variation. In
addition to this, a further research was conducted in order to find out which structural parts of
intellectual capital have a statistically significant relationship with the market value of Baltic States’
listed companies. Results revealed (Mačerinskienė & Survilaitė, 2019) that human capital and
relational capital have the largest influence for the market value of listed companies. However, in
y = 72275 + 125345ICR² = 0,583
0
100000
200000
300000
400000
500000
600000
700000
-2 -1 0 1 2 3 4
Balt
ic S
tate
s’ li
ste
d c
om
pa
nie
s’ m
ark
et
valu
e
Baltic States’ listed companies’ aggregated intellectual capital index
56
companies where structural capital represents a major intellectual capital aggregated index part, a
lower level of intellectual capital was observed.
To sum up, it was noticed that there is a statistically significant relationship between
intellectual capital of the company and its market value and it can be useful to build a linear
regression model. When intellectual capital of the company increases, its market value increases too.
In order to identify how different elements of intellectual capital affect the market value of respective
companies, further research is needed.
Conclusions and recommendations
The concept of intellectual capital is dynamic and multi-criteria, which creates a need to
conduct multiple different studies in order to identify and describe company’s intellectual capital
appropriately. The research conducted by various scientists revealed that there are various
approaches used to describe and assess company’s intellectual capital. In this scientific paper it was
chosen to investigate company’s intellectual capital and what impact it has on its market value. Due
to the complex nature of intellectual capital there is no commonly used harmonised intellectual
capital description. In this scientific paper it was proposed to use intellectual capital description as
follows: the aggregate of intangible resources a company has at its disposal that enables a company
to operate at its best, creates a competitive advantage and increases market value. In order to
investigate how company’s intellectual capital impacts its market value exploratory factor analysis
was conducted. Results revealed that there is a statistically significant relationship between
intellectual capital of the company and its market value. In addition to this, it was identified that
human capital and relational capital have the largest influence for the market value of listed
companies. In companies where structural capital represents a major intellectual capital aggregated
index part, a lower level of intellectual capital was observed. These findings were not expected, so it
would be advised to investigate such phenomenon further. The results are consistent with Chen et al.
(2005, pp. 159-176), Wang (2008, pp. 546-563) and Shakina and Barajas (2014, pp. 861-881)
studies, which revealed that the company’s intellectual capital has an impact on its market value, and
human capital has the greatest impact on the company’s market value. Nevertheless, in the
intellectual capital model some elements that are important were not included due to their intangible
nature: employee motivation, employee satisfaction, trust, and others. It would be useful to
57
investigate the concept of intellectual capital further and include more elements that could provide a
better representation of it and how it affects market value of respective companies.
58
References
Adams, C. A. (2015). The international integrated reporting council: a call to action. Critical
Perspectives on Accounting. 27(1), 23-28.
Ahangar, R. G. (2010). A study of resilience in relation to personality, cognitive styles and decision
making style of management students. African journal of business management, 4(6), 953-961.
Alcaniz, L., Gomez-Bezares, F. & Roslender, R. (2011). Theoretical perspectives on intellectual
capital: a backward look and a proposal for going forward. Accounting Forum. 35(2), 104-117.
Andriessen, D. (2004). Making sense of intellectual capital: designing a method for the valuation of
intangibles. Elsevier Butterworth-Heinemann, Burlington, MA.
Baiburina, E. R. & Golovko, T. V. (2008). Empirical investigation of intellectual enterprise value
and its factors for big Russian companies. Corporate Finance. 2, 5-23.
Beattie, V. & Smith, S. J. (2013). Value creation and business models: refocusing the intellectual
capital debate. The British Accounting Review. 45(4), 243-254.
Bini, L. Dainelli, F. & Giunta, F. (2016). Business model disclosure in the Strategic Report
Entangling intellectual capital in value creation process. Journal of Intellectual Capital. 17(1), 83-
102.
Boesso, G. & Kumar, K. (2007). Drivers of corporate voluntary disclosure: a framework and
empirical evidence from Italy and the United States. Accounting, Auditing and Accountability
Journal. 20(2), 269-296.
Bontis, N., Janoševic, S. & Dženopoljac, V. (2015). Intellectual capital in Serbia’s hotel industry.
International Journal of Contemporary Hospitality Management. 27(6), 1365-1384.
Bontis, N. (1998). Intellectual capital: an exploratory study that develops measures and models.
Management decision, 36(2). doi: http://dx.doi.org/10.1108/00251749810204142.
Bozzolan, S., Favotto, F. & Ricceri, F. (2003). Italian annual intellectual capital disclosure: an
empirical analysis. Journal of Intellectual Capital. 4(4), 543-558.
Branswijck, D. & Everaert, P. (2012). Intellectual capital disclosure commitment: myth or reality?
Journal of Intellectual Capital. 13(1), 39-56.
Bukh, P. N. & Johanson, U. (2003). Research and knowledge interaction: guidelines for intellectual
capital reporting. Journal of Intellectual Capital. 4(4), 576-587.
Čekanavičius, V. & Murauskas, G. (2014). Taikomoji regresinė analizė socialiniuose tyrimuose.
Vilniaus universiteto leidykla. 561, ISBN 978-609-459-300-0.
Cezair, J. H. (2008). Intellectual capital, hiding in plain view. Journal of Performance Management.
21(2), 29-39.
59
Chen, M. C., Cheng, S. J. & Hwang, Y. (2005). An empirical investigation of the relationship
between intellectual capital and firms’ market value and financial performance. Journal of
Intellectual Capital, 6(2). doi: http://dx.doi.org/10.1108/14691930510592771.
Cooper, D. & Sherer, M. J. (1984). The value of corporate accounting reports: arguments for a
political economy of accounting. Accounting, Organizations and Society, 3(4). doi:
http://dx.doi.org/10.1016/0361-3682(84)90008-4.
Cricelli, L. & Greco, M. (2013). Relational capital and performances in interuni-versity research
consortia. International Journal of Engineering Business Management, 5(4). doi:
http://dx.doi.org/10.5772/56001.
Dalkir, K., Wiseman, E., Shulha, M., & McIntyre, S. (2007). An intellectual capital valuation
approach to a government organization. Management Decision. 45(9), 1497-1509.
Dameri, R. P. & Ricciardi, F. (2015). Smart city intellectual capital: an emerging view of territorial
systems innovation management. Journal of Intellectual Capital, 16(4). doi:
http://dx.doi.org/10.1108/JIC-02-2015-0018.
Daum, J. H. (2003). Intangible assets and value creation. West Sussex: John Wiley & Sons Ltd., 421.
Dumay, J. (2016). A critical reflection on the future of intellectual capital: from reporting to
disclosure. Journal of Intellectual Capital, 17(1). doi: http://dx.doi.org/10.1108/JIC-08-2015-0072.
Dumay, J. & Cuganesan, S. (2011). Making sense of intellectual capital complex-ity: measuring
through narrative. Journal of Human Resource Costing & Ac-counting, 15(1). doi:
http://dx.doi.org/10.1108/14013381111125305.
Edvinsson, L. & Malone, M. S. (1997). Intellectual Capital: Realising your Com-pany’s True Value
by Finding its Hidden Brainpower. Harper Collins Publish-ers Inc, NewYork, NY.
Firer, S. & Williams, S. M. (2003). Intellectual capital and traditional measures of corporate
performance. Journal of Intellectual Capital, 4(3). doi:
http://dx.doi.org/10.1108/14691930310487806.
Flower, J. (2015). The international integrated reporting council: a story of failure. Critical
Perspectives on Accounting. 27(1), 1-17.
Fox, A., Loon, A., Whitton, G., & Tunny, G. (2001). Some aspects of human capital investment in
Queensland. Working Paper No. 2, Queensland Department of Employment and Training, Brisbane.
Galbraith, J. K. (1969). Ambassador’s Journal: A Personal Account of the Kennedy Years, London,
Hamilton.
Gleason, K. I. & Klock, M. (2003). Intangible capital in the pharmaceutical & chemical industry.
Department of Economics and Finance. http://scholarworks.uno.edu/econ_wp/10/.
Guthrie, J., Petty, R., Yongvanich, K. & Ricceri, F. (2004). Using content analysis as a research
method to inquire into intellectual capital reporting. Journal of Intellectual Capital. 5(2), 282-93.
60
Hall, R. (1992). The strategic analysis of intangible resources. Strategic Manage-ment Journal, 13(2).
doi: http://dx.doi.org/10.1002/smj.4250130205.
Huang, Ch, J. & Liu, Ch. J. (2005). Exploration for the relationship between innovation, IT and
performance. Journal of Intellectual Capital. 6(2), 237-252.
Huang, C. & Wang, M. C. (2008). The effects of economic value added and intellectual capital on
the market value of firms: an empirical study. International Journal of Management. 25(4), 722-731.
Huang, Y.-C. & Wu, Y.-C. J. (2010). Intellectual capital and knowledge productivity: the Taiwan
biotech industry. Management Decision. 48(4), 580-599.
Kaplan, R. S. & Norton, D. P. (2001). The Strategy-Focused Organization: How Balanced Scorecard
Companies Thrive in the New Business Environment. Harvard Business School Press, Boston, MA.
doi: http://dx.doi.org/10.1108/sl.2001.26129cab.002.
Kavaliauskienė, Ž. (2010). Faktorinis darbuotojo įsipareigojimą organizacijai matuojančių skalių
validumas lietuvos populiacijos atveju. Ekonomika ir vadyba: aktualijos ir perspektyvos. 2(18), 64-
77.
Liang, C. J., Huang, T. T. & Lin, W. C. (2011). Does ownership structure affect firm value?
Intellectual capital across industries perspective. Journal of Intellectual Capital. 12(4), 552-570.
Liang, Ch.J., Chen T.Y. & Lin, Y.L. (2013). How do different business models affect intellectual
capital? Journal of Intellectual Capital. 14(2), 176-191.
Low, M., Samkin, G. & Li, Y. (2015). Voluntary reporting of intellectual capital. Comparing the
quality of disclosures from New Zealand, Australian and United Kingdom universities. Journal of
Intellectual Capital. 16(4), 779-808.
Luthy, D. H. (1998). Intellectual capital and its measurement. Proceedings of the Asian Pacific
Interdisciplinary Research in Accounting Conference (APIRA), Utah State University, Logan, UT.
www3.bus.osaka-cu.ac.jp/apira98/archives/htmls/25.htm.
Mačerinskienė, I., & Survilaitė, S. (2019). Company’s intellectual capital impact on market value of
Baltic countries listed enterprises. Oeconomia Copernicana, 10(2), 309–339. doi:
10.24136/oc.2019.016.
Markhaichuk, M., & Zhuckovskaya, I. (2019). The spread of the regional intellectual capital: the
case of the Russian Federation. Oeconomia Copernicana, 10(1), 89–111. doi: 10.24136/oc.2019.005.
Mention, A. L. & Bontis, N. (2013). Intellectual capital and performance within the banking sector
of Luxembourg and Belgium. Journal of Intellectual Capital. 14(2), 286-309.
Molodchik, M., Shakina, E. & Bykova, A. (2012). Intellectual capital transformation evaluating
model. Journal of Intellectual Capital. 13(4), 444-461.
Nahapiet, J. & Ghoshal, S. (1998). Social capital, intellectual capital and the organ-izational
advantage. The Academy of Management Review, 23(2). doi:
http://dx.doi.org/10.5465/AMR.1998.533225.
61
Nam, T. & Pardo, T. (2011). Smart city as urban innovation: focusing on management, policy, and
context. Proceedings of the 5th International Conference on Theory and Practice of Electronic
Governance, ACM, Tallin, 26-28, September.
Nimtrakoon, S. (2015). The relationship between intellectual capital, firms’ market value and
financial performance. Empirical evidence from the ASEAN. Journal of Intellectual Capital, 16(3).
doi: https://doi.org/10.1108/JIC-09-2014-0104.
Nogueira, C. G., Kimura, H., Barros, L. Jr. & Basso, L. F. C. (2010). The impact of intellectual
capital on value added or Brazilian companies traded at the BMF-BOVESPA. Journal of
International Finance and Economics. 10(2), 1-12.
Osman-Gani, A. M. & Rockstuhl, T. (2008). Antecedents and consequences of social network
characteristics for expatriate adjustment and performance in overseas assignments: implications for
HRD. Human Resource Development Review. 7(1), 32-57.
Pakalniškienė, V. (2012). Tyrimo ir įvertinimo priemonių patikimumo ir validumo nustatymas.
Vilniaus universiteto leidykla.
Peng, Tzu-Ju, Pike, Stephen, Roos, Goran. (2007). Intellectual capital and performance indicators:
Taiwanese healthcare sector. Journal of intellectual capital. 8(3), 538-556.
Pinto, L. H. & Araujo, R. C. (2016). Social networks of Portuguese self-initiated expatriates. Journal
of Management Development. 35(1), 89-103.
Poletti Lau, J. (2003). Effects of Intangible Capital on Firm Performance. Leuven: VIIth Spring
Meeting of Young Economists.
Poyhonen, A. & Smedlund, A. (2004). Assessing intellectual capital creation in regional clusters.
Journal of Intellectual Capital. 5(3), 351-365.
Rahman, S. (2012). The role of intellectual capital in determining differences be-tween stock market
and financial performance. International Research Journal of Finance and Economics, 89, April.
Rashid, A. A., Ibrahim, M. K., Othman, R. & See, K. F. (2012). IC disclosures in IPO prospectuses:
evidence from Malaysia. Journal of Intellectual Capital. 13(1), 57-80.
Riahi-Belkaoui, A. (2003). Intellectual capital and firm performance of US multinational firms: a
study of the resource-based and stakeholder views. Journal of Intellectual Capital. 4(2), 215-226.
Roos, G. & Roos, J. (1997). Measuring your company’s intellectual performance. Long Range
Planning, 30(3). doi: http://dx.doi.org/10.1016/S0024-6301(97)00022-8.
Roos, J., Roos, G., Edvinsson, L. & Dragonetti, N. C. (1998). Intellectual Capital–Navigating in the
New Business Landscape, New York University Press, New York.
Sachpazidu-Wojcicka, K. (2017). Innovation as a determinant of the competitiveness of Polish
enterprises. Oeconomia Copernicana, 8(2), 287–299. doi: 10.24136/ oc.v8i2.18.
62
Sanchez, M. P. & Elena, S. (2006). Intellectual capital in universities: improving transparency and
internal management. Journal of Intellectual Capital. 7(4), 529-548.
Sellers-Rubio, N.-G. & ’lbez, M.-R. (2007). The economic value of patent protection and rivalry in
the Spanish electrical sector. European Journal of Innovation Management. 10(4), 434-452.
Shakina, E. & Barajas, A. (2012). The relationship between intellectual capital quality and corporate
performance: An empirical study of Russian and European companies. Economic Annals, LVII, 192,
79-97.
Shakina, E. & Barajas, A. (2014). The dynamics of intellectual resources during the economic crisis.
Economic Research-Ekonomska Istraživanja, 27(1). doi:
http://dx.doi.org/10.1080/1331677X.2014.974918.
Shakina, E. & Bykova, A. (2011). Intellectual Capital Evaluation: Relationship between Knowledge
Management Implementation and Company’s Performance. Proceedings of the 12th European
Conference on Knowledge Management, 2, Leeds: Academic Publishing International Limited, 917-
929.
Shiu, H. J. (2006). The application of the value added intellectual coefficient to measure corporate
performance: evidence from technological firms. International Journal of Management, 23(2).
Sonnier, B. M. (2008). Intellectual capital disclosure: high-tech versus traditional sector companies.
Journal of Intellectual Capital. 9(4), 705-722.
Sonnier, B. M., Carson, K. D. & Carson, P. P. (2007). Accounting for intellectual capital: the
relationship between profitability and disclosure. Journal of Applied Management and
Entrepreneurship. 12(2), 3-14.
Stewart, T. A. (1997). Intellectual capital: the new wealth of organizations. Doubleday Dell
Publishing Group, New York. doi: http://dx.doi.org/10.1002/pfi.4140370713.
Strong, P. T. (2014). Integrated Reporting in Australia: A study of key Interests and Effects,
(dissertation). University of New South Wales, Sydney.
Stroppa, C. & Spieβ, E. (2011). International assignments: the role of social support and personal
initiative. International Journal of Intercultural Relations. 35(2), 234-245.
Sullivan, H. P. (1998). Profiting from Intellectual Capital: Extracting Value from Innovation. Wiley,
New York, NY. doi: http://dx.doi.org/10.1108/13673279910275585.
Sussan, F. (2012). Consumer interaction as intellectual capital. Journal of Intellectual Capital. 13(1),
81-105.
Sveiby, K. E. (2001). A knowledge‐based theory of the firm to guide in strategy formulation. Journal
of Intellectual Capital, 2(4). doi: http://dx.doi.org/10.1108/14691930110409651.
Taylor, M. (1998). A feedforward and feedback framework for analysing an organisation’s
resources, capabilities and development needs. Health Manpower Management. 24(6), 196-205.
63
Ting, I. W. K. & Lean, H. H. (2009). Intellectual capital performance in financial institutions in
Malaysia. Journal of Intellectual Capital. 10(4), 588-599.
Tseng, C. Y. & Goo, Y. J. J. (2005). Intellectual capital and corporate value in an emerging
economy: empirical study of Taiwanese manufacturers. R&D Management, 35(2). doi:
http://dx.doi.org/10.1111/j.1467-9310.2005.00382.x.
Tuckman, B. W. (1965). Developmental Sequence in Small Groups. Psychological Bulletin 63.
Ulrich, D. (1998). Intellectual capital equals competence x commitment. Sloan Management Review,
39. doi: https://doi.org/10.1177/031289620202701S09.
Užienė, L. (2014). Measurement of national intellectual capital: the benchmarking of the Baltic
Countries in the context of Europe. Proceedings of the European Conference on Intellectual Capital,
10–11 April 2014, Trnava, Slovak Republic, 283-292.
Vaičekauskaitė, R. (2014). Intelektinis kapitalas kaip mokslo ir verslo bendradarbiavimo stiprinimo
potencialas. Tiltai, 3, 1-16.
Vlacsekova, D., & Mura, L. (2017). Effect of motivational tools on employee satisfaction in small
and medium enterprises. Oeconomia Copernicana, 8(1), 111–130. doi: 10.24136/oc.v8i1.8.
Wang, J. C. (2008). Investigating market value and intellectual capital for S&P 500. Journal of
Intellectual Capital, 9(4). doi: http://dx.doi.org/10.1108/14691930810913159.
Whiting, R. H. & Miller, J. C. (2008). Voluntary disclosure of intellectual capital in New Zealand
annual reports and the ‘hidden value’. Journal of Human Resource Costing and Accounting. 12(1),
26-50.
Yi, A. & Davey, H. (2010). Intellectual capital disclosure in Chinese (mainland) companies. Journal
of Intellectual Capital. 11(3), 326-347.
Zéghal, D. & Maaloul, A. (2010). Analysing value added as an indicator of intellectual capital and its
consequences on company performance. Journal of intellectual capital. 11(1), 39-60.
Zéghal, D. & Maaloul, A. (2011). The accounting treatment of intangibles: a critical review of the
literature. Accounting Forum. 35(4), 262-274.
Zhang, J., Zhu, N. & Kong, Y. (2006). Study on intellectual capital and enterprise’s performance:
empirical evidence on the Chinese securities market. Journal of Accounting and Auditing, 2(1).
64
ECONOMIC SECURITY AS A PHENOMENON AND CONCEPT
Saulius KROMALCAS
Mykolas Romeris
Universitetas, Ateities g. 20,
LT-08303 Vilnius
Žaneta SIMANAVIČIENĖ
Mykolas Romeris
Universitetas, Ateities g. 20,
LT-08303 Vilnius
Daiva BESAGIRSKAITĖ
Lietuvos inovacijų centras,
Mokslininkų g. 6A, LT-08412
Vilnius
Abstract:The ever-changing geopolitical situation has led to a new approach of the world's countries
towards political, economic and military activity at the beginning of the relation. In the context of all
these phenomena, one of the priorities is national security, which is inextricably linked to economic
security. The paper aims to examine the conception and structure of economic security as a
phenomenon and to reveal the components of economic security that directly affect the
competitiveness of the state and the region.
Keywords: economic security, economic security concept, economic sovereignty.
65
Introduction
Economic security is the various forces expression that determines the country's economic
policy, reflecting the real country's internal policy situation and its integration degree in the
international political-economic context. Economic security definition is a relatively young
component of economic theory that underlies a state's national security. National economic security
sufficient level is directly related to a stable economic situation, where a high living standard is
ensured, disposal of sufficient assets are available to protect the existing resources, citizens,
institutions, enterprises, natural resources, state territory from possible internal and external threats.
Economic security objects can be the state, regions, territories, institutions and organizations,
society or citizens. The main subject is the state performing its functions in the economic security
area.
Scientific problem - what are economic security theoretical principles.
The aim of the article is to examine economic security conception, researchers' definitions
from theoretical aspects, to reveal the economic security components.
To achieve the research aim follows tasks are:
1. To examine theoretical approaches to economic security phenomenon.
2. To analyze and generalize the economic security phenomenon conception.
3. To reveal the economic security components.
The paper will analyze and summarize scientific literature to determine the country's economic
security concept and structure.
Approaches to the economic security phenomenon: historical experience
In the various countries' economic literature, the term "economic security" is relatively young
by the standards of historical science. Traditionally, economic security is considered the most
important economic system's qualitative feature. A. Kozlova (2009), who examines the conception,
states that the first ideas about economic security as a state of economic system's social change are
mentioned in the ancient Eastern philosopher's writings. Initially, "security" was understood only as
of the physical security of the state's territory against the external other states' armed invasion.
Capitalist relations and the member state's formation in the era of the 17th - 18th centuries, in
European civilization it was created and developed the idea that the primary state's goal is the
general prosperity and security. At that time, security meant "a state, a peaceful situation manifested
66
by the real danger's absence, as well as the material organizational structure existence which helps to
create and maintain that situation." However, state security cannot be realized without economic
security. In the 18th century, the perception of state security in advanced European countries was
inseparable from its economic prosperity. The active debate on national economic security only
began in the 1930s and 1940s., after the free competition era and the centralized regulation process
of the market economy beginning.
National economic security has become an essential national security element, and the primary
state objective in this area is stable economic growth. According to many authors' approaches,
economic security has traditionally been considered as a corner part of the qualitative system.
Current definition understanding was formed and spread in 1934. In the so-called Great Depression,
when the newly elected US President F. Roosevelt used the phrase "national economic security" in
his message to the nation.
A.King (2018) cites M. Mastanduno (1998), who says after World War II, the Truman
administration sought to create the concept of "economic security," which was to "create an open
world economic course that would serve the strategic goals of United States". US goal was to create
an open economic order as well as to ensure maritime freedom, free trade, equal access to raw
materials and international economic cooperation. In Japan, after the defeat of World War II, the
concept of „economic security" also developed at a similar time. Still, according to J.W.M. Chapman
(1998) after Japan's defeat in World War II, the country needed a new approach to protecting the
state and the ability to find access to the necessary raw materials and export markets. One of the first
results received in the 1970s - 1980s. - the new concept of "Comprehensive security" (sōgō anzen
hoshō) creating. That based on Japan's dependence on the outside world for access to natural
resources and markets and to develop security.
In the Russian Federation, the conception was inserted into the scientific language in the early
1990s as the country transitioned to a market economy system when the divergence of Russian
economic interests within the country led to a sharp increase in economic dependence on the outside
world. The economic security conception development was started in 1993. The working group
consisted of the Russian Academy of Sciences scientists and specialists and of various institutes and
university representatives.
In Lithuania, this term was examined after the 1990s and later incorporated into the Lithuanian
National Security Strategy. Economic Security The National Security Strategy (2002) defines as the
country's consistent economic growth, enabling facilitate conditions to the all economic fields
development, building preventive measures to reduce the shadow economy, ensuring a higher living
standard for its citizens and creating a competitive economy (art. 5.231).
67
According to B. Taylor and B. Luckham (2006), economics and security are directly connected
because states can use economic sanctions or other economic instruments to achieve broader
strategic goals. On the other hand, states can use security policies to pursue their economic goals,
such as engaging in expansionist military behavior to gain access or control of the territory and
resources.
The links between economics, stability, and security were defined differently before, during,
and today of the Cold War. Since they were adapted to the successive stages of international
relations. These differences have led to different security and economic models. In the past, the
security model was based on a balanced confrontation between forces or superpowers and their
allies. At present security is based on interdependence and cooperation between states, reinforced by
globalization, liberalization and institutional structures. In the past, the economy relied on a national
and largely autonomous model, which naturally excluded the significant possibility of external
cooperation. The security model was more often based on solid (military) security dimensions, while
the soft (non-military) dimensions played a limited role. In the modern international relations period,
the roles of both soft and hard dimensions have changed. Remarkable, there are two levels:
international and national.
Theoretical aspects of the country's security conception
Security policy analysis begins with an understanding of the national security concept. Bock
and Berkowitz (1966) defined national security as "the nation's ability to protect its internal values
from external threats". From Haftendorn (1991) point of view, there is no single concept of security:
national security, international security, and global security are related to different issues and come
from different historical and philosophical contexts.
In the middle of 2017 December, President Trump released US national security strategy. This
strategy is a very matter document providing key government leaders with guidance that should help
them perform key functions in federal departments, agencies, and other government organizations
over the next few years. The volume of the document is 55 pages, outlining the essential statements
that the National Security Council considered particularly important to the United States. The
President hopes to: 1) protect 2) promote US prosperity, 3) intensify peacekeeping 4) advance US
influence in the world (NSS, 2017, p. 4).
The security concept depends on a constantly changing context. According to Christopher
Daase, looking at the security concept from a historical perspective, the process is expansive and
goes in several directions (see Figure 1).
68
Source: Based on Ch. Daase (2011)
Figure 1. Expanded security concept
The origins formed in a peaceful environment, where the social emancipation process is
supported. This environment, as the basis for the primary security, allows the formulation of broader
security needs beyond national security. That is why, as Ch.Daase reasons in his publication, the
state becomes a victim of its own success. This conclusion can be drawn from the fact that when the
Danger dimension
Reference dimension Materiality dimension
Place dimension
Global
International
National
Regional
State
Society
Individual
Threat Military
Vulnerab
ility
Risk
Economic
Ecological
Humanitarian
69
basic needs are met, additional desires always arise. This also applies to the field of security. Since
the variety of security conception is developing, subject to legal regulation becomes more expedient.
Economic security conception
In the combination of the words "economic security", there are two logical terms "economic"
and "security". While the term "economy" as a state, organization, structure, and economic
management does not require explanation, the second term in the economic context is unclear and
requires additional justification regarding the place and role in the phrase of "economic security".
J. Bagdanavičius (2002), who broadly deals with economic security in the context of national
security and treats it as the joint between the state institutions and the country's economic situation,
where necessary national interests are assured, purposeful social policy, as well as sufficient
economic and defense resources are ensured.
Accordance with R. Šimašius and R. Vilpišauskas (2005), the concept of economic security is
multiplicity. Economic security aspects that are related to the economic security of the state's
citizens' majority, not single individuals or relatively small groups of them, should be considered as
state economic security. Economic security is identified with different phenomena:
• stability of state economic power and ability to finance defense needs;
• State’s supply of "strategic products" (energy, etc.);
• State's foreign trade diversification;
• independence from major (dominant) players in the international economy;
• security against economic espionage;
• good macroeconomic performance;
• property security;
• individual’s social security, such as a certain level of living income;
• employment, secure jobs and corporate profits.
A. Makštutis (2006) defines the object of this research as a certain state of economic and
government institutes, which ensures the protection of national priorities, guarantees a harmonious,
socially-oriented whole of the country's development, as well as sufficient economic and defensive
potential, despite internal or foreign positive - negative processes development. The scientist
identifies the state's economic security with its:
• economic power stability and ability to finance defense needs;
70
• state's "strategic products" (energy, etc.) sourcing;
• foreign trade diversification;
• independence from dominant players in the international economy;
• security against economic espionage;
• good macroeconomic performance;
• property security;
• the individual's social security, a certain level of income necessary for his livelihood;
• employment, secured jobs;
• economic efficiency.
According to A. Grebliauskas, the definition of economic security is formed as follows: It is
the state's and state entities ability (political will, opportunity, knowledge) to maintain the balance
between economic objects and systems, which is a basic condition (necessary and sufficient) for the
state's and state's entities development.
V. Pukelienė and N. Čepaitienė (2007) also provide a comprehensive analysis of the economic
security conception. As one of the exclusive definitions of economic security, these authors present
the alternative proposed by H. E. S. Nesadurai (2005) - three conditions are required for minimum
economic security:
1. income-expenses proportion is indispensable for the minimum needs of the individual and
the family;
2. market integration degree;
3. equitable allocation and social equality.
The authors summarize the following definition: economic security is the totality of the
country's economic system responses to external and internal factors that determine the country's
economy functioning and development.
In order to structure the "economic security" concepts, this article's authors have presented the
latter in the table.
Table 1. Economic security definitions
Definition of "economic security" concept Author
The state of the economic system that allows it to develop
dynamically, effectively solve social problems, and in which
Abalkin L. (1994)
71
the state has the ability to conform and implement independent
economic policies.
Economic policy instruments which are used for purposes of
aggression trade and investment boycotts, the restriction of
energy supplies.
Cable, V. (1995)
The nation (state) can sovereignly determine, without
interference and pressure from outside, the ways and forms of
their effective development in a state.
Olejnikov E.A. (1997)
State and its entities ability (the political will and ability) to
keep the economy facilities – systems in a balance, that is
necessary and sufficient condition to develop the state and its
entities evolution.
Grebliauskas A.,
Miliauskas G. (2008)
A preparation state of the economy for ensuring decent
conditions for living and developing the personality, the social-
economic stability and the political military capability of the
society and the country in order to eliminate internal and
external threats.
Hacker, J., et al. (2010)
A never ending (and not a standstill) process, evidencing that
(personal) economic security exists, and is fixed and stable,
directly and indirectly exert influence to the macroeconomic
environment, which becomes, for the sake of confidence, even
more stable, secure and consecutively reproduces the economic
security feelings through “hard macroeconomic indexes”
(inflation rate, employment) back to the micro economic level.
T. Sviderske (2014)
Economic security is a priority element of modern national
security that can occur in any area of modern society, like
energy, transport, communications, military, food, and etc.,
cannot exist outside the domestic economy.
Tamošiūnienė, Munteanu
(2015)
Economic security is the consistent country's economic growth,
which facilitates the development of all the economy fields,
preventive measures aimed at reducing the shadow economy,
ensuring a higher living standard for its citizens and a
competitive economy creating.
Republic of Lithuania
National Security
Framework Strategy
(1996)
72
R. Šimašius and R. Vilpišauskas (2003) point out that economic security, like any other sphere
of functioning of society, has to be examined from two perspectives - “sovereign” (holistic) and
individualistic. The holistic approach treats economic security as the sovereignty of the state and
identifies it with state security when protecting against external threats. This concept prevails
examining international relations, especially in the neorealist's works, since international relations
themselves are "relations between states and other collective joints". Individualists believe that
economic security must guarantee the individual's security against economic threats, pointing out
that: first, even decisions made on behalf of the state are decisions of the individuals, for the state
itself acts through individuals (the theory of public choice); second, economic relations involve
individuals and the entities they create, not the state or its institutions; and third, the state serves its
citizens. As can be seen, it can be referred to their summarized claim that the state's economic
security is the derivative of the economic security of the state's citizens. (Šimašius, Vilpišauskas,
2003).
Economic security phenomena components
Economic security is the state of the country's economy, which allows ensuring its stable
functioning under the influence of internal and external factors, satisfying sufficient social needs,
necessary defense capabilities, protection of national interests. Economic security ensuring is
creating the conditions to prevent irreparable damage to a state's economy from internal and external
economic threats.
According to Melnik (2008), one of the essential factors for ensuring the modern society's
well-being and creating social welfare, maintaining political stability and the priorities of the state
economic policy is the economic modernization. The flexible state's economic policy determines the
efficiency of solving society's economic problems, creating the measures to solve problems of social
development, health care, culture, education and science, law and order, nature security and nature
source use, national security and defense, foreign relations development and other social life areas
specific issues.
According to Tatul Melsik Mkrtchyan (2015) the analysis of the literature, the state’s economic
security system can be divided into internal and external subsystems, which have their own
components:
• external security subsystem components - technological, commercial, financial;
73
• internal security subsystem components - technical and production, food and raw materials,
energy, environment, information (see Figure 2).
Source: Tatul Melsik Mkrtchyan (2015)
Figure 2. Key state's economic security components
The author explains the external components following:
The technological state's economic security component is characterized by active participation
in international scientific and technological progress, which guarantees state capabilities.
The commercial state’s economic security component reflects the potential for diversifying the
country’s export and import trade structure.
The financial state's economic security component can be described as the ability of a country
to implement an independent monetary policy and to ensure the stable functioning of its financial
system by repaying international loans and obtaining, distributing, using and repaying foreign
investment under adverse external and internal conditions.
Internal components:
The industrial and technical component importance in the state's economic security determines
the production and technical feasibility of extended economic reproduction. It refers to the ability of
an economy to fulfill the needs of society, even when favorable external or internal conditions are
disrupted. Failure to function with this state's economic security component increases dependence on
other countries.
The food and raw materials component of the state's economic security implies the necessary
economic security of food and raw materials. Food and raw materials supply is one of the most
State‘s economic security
External Internal
Technological Commercial
Financial
Energy Information
Technical and
prodaction
Food and
raw materials
Ecological
74
important state's economic security components, and many countries have laws defining minimum
standards for food security.
Energy security as a component of the state's economic security means a stable supply of
sufficient energy for domestic consumption. To this end, a consistent analysis shall be carried out in
all countries to identify the circumstances which could adversely affect the functioning of the state's
fuel and energy system.
The ecological state’s economic security component is its ability to prevent and close the gap
between public interest and environmental protection in a timely manner. The environmental
pollution problem is closely linked to human economic activity and it is essential for ecological
security to minimize the anthropogenic impact on the ozone layer, flora and fauna, gene pool and
other environmental components.
The information state's economic security component, in the context of existing internal and
external relations, indicates economic activity that results from information exchange reliability, an
intangibles assets rising in national assets, and an increase in information. Information security is
closely linked to the telecommunication sector and primarily shows telecommunication security.
In examining Kompanejceva G. A. (2016), the object of economic security can be, in addition
to the state, the economic system, regions, society, business entities and the individual. The
complication of economic security conception makes it possible to define it systematically by
dividing that system's elements (see Fig.3).
Šaltinis: Kompanejceva G. A. (2016)
International security
National security
State‘s economic security
Regional economic security
Individual‘s economic security Company economic security
75
Figure 3. Economic security level relation model
Security threats at the regional level are presented as external and internal. According to the
author, internal threats are related to the depreciation of the fixed asset, the regional industrial
enterprises' profitability and the lack of budget support. External threats include the regional
economy dependence on foreign capital and imports, reduction of regional production, regional
separatism. Based on this, the following aspects of economic security in the region are notable:
industrial, financial, socio-demographic, informational and other complex types of security. The
lower level is represented by two components: the individual and the business entity. Alabiceva
(2014), meanwhile, argues that the main unit of economic security is personality. In modern society,
intellectual skills play a key role in the countries' economies. On them depends that technological
development which determines the production's expansion and rising in surplus-value. Accordingly,
there is a direct link between personality growth, company and country development.
So, summarizing the authors analyzed, it can be stated that economic security is a key
component of the national security system, which is characterized by the ability of the state's national
economy and its regions to ensure stable continuous development and relative protection for both
individual and the whole country, in reliance economic methods.
Conclusions
1. Early, the security model was more often based on solid (military) security dimensions, whereas
the soft (non-military) dimensions played a limited role. In the modern period of international
relations, the roles of both soft and hard dimensions have changed. Analyzing the scientific
literature, it should be noted that recently it is implemented into two levels: international and
national.
2. Remarkable, that there is no one general definition of economic security. In conclusion, it can be
argued, economic security is a situation in which the state can determine the ways and forms of
effective development without interference and external pressure.
3. Analysis of the authors dealing with economic security components, it can state that economic
security consists of external (technological, commercial and financial) and internal (technical and
production, food and raw materials, energy, environment, and information) components.
Meanwhile, in addition to the state, the economic system, regions, society, business entities and
the individuals can be the economic security objects.
76
References
Abalkin L. Jekonomicheskaja bezopasnost' Ros-sii: ugrozy i ih otrazhenie // Voprosy jekonomiki.
1994. № 12.
Bock, P.J., Berkowitz, M. (2000) American national security: a reader in theory and policy, 134
Brendan Taylor and Bruce Luckham, ‘Economics and security’, in Strategy and Security in the Asia
Pacific, ed. Robert Ayson and Desmond Ball, Allen & Unwin, Sydney, 2006, pp. 138–51.
C. Daase, in: Zoche/Kaufmann/Haverkamp (Hrsg.), Zivile Sicherheit. Gesellschaftliche
Dimensionen gegenwärtiger Sicherheitspolitiken 2011, p. 139, 143. ff. (142 ff.)
Haftendorn, H. (1991) The Security Puzzle: Theory-Building and Discipline-Building in
International Security, 3.
https://e-seimas.lrs.lt/rs/legalact/TAD/TAIS.165132/format/ISO_PDF (2019 07 15)
J.W.M. Chapman, Reinhard Drifte and I.T.M. Gow, Japan’s Quest for Comprehensive Security:
Defence, Diplomacy, Dependence, Frances Pinter Publishers, London, 1983, p. xvii; Y. Soeya,
‘Japan: Normative constraints versus structural imperatives’, in Asian Security Practice: Material
and Ideational Influences, ed. M. Alagappa, Stanford University Press, Stanford, 1998, pp. 216–17.
King, Amy. “Economics and Security.” New Directions in Strategic Thinking 2.0: ANU Strategic &
Defence Studies Centre's Golden Anniversary Conference Proceedings, edited by RUSSELL W.
GLENN, ANU Press, Acton ACT, Australia, 2018, pp. 23–36. JSTOR,
www.jstor.org/stable/j.ctv47wfph.9.
Makštutis, A. (2006). SECURITY COOPERATION: THE VIEWS OF LITHUANIA. In
MONOGRAPH: TENDENCY OF NATIONAL SECURITY IN THE BALTIC SEA REGION (pp. p.
179 - 192). Vilnius: THE GENERAL JONAS ŽEMAITIS MILITARY ACADEMY OF
LITHUANIA, INTERNATIONAL ACADEMY OF ORGANIZATION AND MANAGEMENT
SCIENCE.
Melnikas B., Modernios ekonomikos kūrimas: nauji iššūkiai ir valstybės politika globalizacijos ir
ţinių visuomenės kūrimosi sąlygomis. // Vadybos šiuolaikinės tendencijos: mokslo darbų rinkinys. –
Vilnius: Lietuvos karo akademija, 2008, p. 9-18. – ISBN 978-9955-423-69-0
Michael Mastanduno, ‘Economics and security in statecraft and scholarship’, International
Organization 52, no. 4, 1998, p. 836.
Nacionalinio saugumo pagrindų įstatymas // Prieiga per internetą:
http://www3.lrs.lt/pls/inter3/dokpaieska.showdoc_l?p_id=285559
Olejnikov E.A. Osnovy jekonomicheskoj bezopasnosti. (Gosudarstvo, region, predprijatie, lich-
nost'); pod. red. E.A. Olejnikova. M., 1997.
77
Šimašius R., Vilpišauskas R. Ekonominio saugumo samprata ir politikos principai Lietuvoje //
vddb.library.lt/fedora/get/LT-eLABa-0001:J.../DS.002.0.01.ARTIC
Алабичева М.А. Экономическая безопасность личности как структурная составляющая
экономической безопасности государств /М.А.Алабичева// Социально-экономические явления
и процессы. 2014. №11 С.9-14.
Козлова А. Экономическая безопасность как явление и понятие // Власть. – 2009. – № 1. – С
.14–17.
Компанейцева Г. А. Система экономической безопасности: уровни и механизмы оценки //
Научно-методический электронный журнал «Концепт». – 2016. – Т. 17. – С. 832–836. – URL:
http://e-koncept.ru/2016/46342.htm.
78
COUNTRY'S ECONOMIC SECURITY CONCEPT: THEORETICAL INSIGHTS
Inna KREMER-MATYŠKEVIČ
PhD student
Mykolas Romeris University, Ateities g. 20, LT-
08303 Vilnius
Gintaras ČERNIUS
Mykolas Romeris University, Ateities g. 20, LT-
08303 Vilnius
Abstract: The link between economic and national security is undeniable. Since the countries‘ economic
security perception is not yet unambiguous, the problem of this research arethe theoretical principles of
economic security. The research object is country's economic security concept. In order to achieve
scientific research‘s goal to prepare the concept of economic security of the country, the following tasks
are adressed: 1) to analyze theoretical assumptions of economic security, 2) to summarize the
phenomenon of economic security; 3) to define its concept and reveal the country's economic security
concept. The research analyzes the views of Lithuanian and foreign scientists on economic security and
summarizes these opinions in the proposed concept of country economic security. The article is divided
into three parts. The first part presents the scientific literature review of economic security, the second
part describes the proposed concept of country's economic security, research conclusions are presented
in the third part of the article.
Keywords: national economy, economic security, concept of economic security, economic growth,
national security.
79
Introduction
State‘s possibilities to protect person‘s, economic undertaking‘s, regional and country‘s economic
interests are especially important achieving the sustanable development at national and international
levels. Development of country's economic security is complex and composite process. Therefore, it
shall be analysed taking into account not only dynamics of economic growth. Country's economic
security should guarantee and protect the vital needs from external and internal threats. Economic
security understanding is a relatively new phenomenon in economic theory. In economic globalization
context, it is very important to reveal the essence of the problem, to identify real threats, to provide
reliable and effective problem solving methods. Under conditions of global economic development,
solving economic security challenges is a multifaceted task that should not only include a security
function but also a comprehensive approach, taking into account the overall political and financial
possibilities.
The research problem is the theoretical principles of economic security.
The object of the research is country's economic security concept.
The aim of the research is to develop country's economic security concept after having analyzed
scientific approaches to economic security.
In order to achieve this research aim, the following tasks are addressed:
1. To analyze theoretical assumptions of economic security.
2. To summarize the phenomenon of economic security;
3. To define its concept and reveal the country's economic security concept.
The analysis and generalization of scientific literature are used to define the concept and structure of
the country's economic security.
Country's economic security theoretical aspects
Before starting to analyze the aspect of economic security, one should understand the phenomen
of security. Glaser (1997) categorically defines security as the absence of any risk, because the risk has
a negative impact on anyone. This scientist describes the security dilemma as a solution to five major
security issues. Based on Glaser (1997), the security structure is shown in Figure 1 below.
80
1 Figure. Security structure
Source: based on Glaser (1997)
This is how Glaser (1997) divides security into two main components: international and national.
International security attributes are global and regional security. National security: state, public and
private security. Global security is the system of international and environmental safety relations against
threats, which can destabilize the world, trigger a global crisis. Regional security is a set of economic,
ecological, legal, geopolitical and other conditions that must ensure the security of state interests,
regional development, financial stability, infrastructure and business development, as well as influence
the development of internal and external security. State security - reducing the impact of internal and
external conflict threats, preparing for unconditional defense and global civic resistance in the event of
aggression. Public security - citizens' welfare policies that reduce the risk of potential social crises,
reduce the wealth gap and prevents the impoverishment of the population, thus introducing social
solidarity. Private security in the context of national security, Glaser (1997) is divided into enterprise
and personal security: enterprise – company’s financial stability and development; personal - principles
of safe behavior, active and passive safety measures.
The researcher has repeatedly emphasized the importance of the complex of economic and
financial conditions, ensuring the interests of the state, financial stability, reduction of internal and
external threats, individual welfare policy, economic development, etc., but does not provide a definition
of economic security.
Literature review of economic security definition
After analyzing the research of Lithuanian scientists, this theoretical insight presents two
understandings of economic security. As Šimašius and Vilpišauskas (2005) claim, economic security
definition is ambiguous. State’s economic security is being considered as such economic security
81
aspects, which relate to the economic security of the majority of the state's citizens, rather than of
separate individuals or their relatively small groups.
Tamošiūnienė and Munteanu (2015) provide a broader definition of economic security in their
joint research. Their opinion, economic security is a priority element of modern national security, which
can arise in any modern society, because safety of energy, transport, communications, army, food, etc
that cannot exist outside the national economic. Therefore, Tamošiūnienė and Munteanu (2015) present
the structure of economic security in their research (see Figure 2).
Figure 2. Economic security structure
Source: Tamošiūnienė and Munteanu (2015)
According to Tamošiūnienė and Munteanu (2015), economic security should be divided into two
approaches: individual and macroeconomic.
The individual economic security approach defines the economic security of a person as stable
income and other sources in order to maintain the standard of living in the present and the near future,
i.e: permanent solvency, predictable cash flow, efficient use of human capital (Tamošiūnienė, Munteanu,
2015 cit. Rupert, 2007, Montbrial, 2012).
The macroeconomic security approach has a complicated history, because the period of this
approach rise coincides with the times of the two world wars. In particular, the formation of this approach
was supported by a Russian economic school, which using critical meanings, attempted to quantify
economic security, as well as a model developed by Professor Lino Briguglio, which assesses economic
security, taking into account the country's economic vulnerability and resistance level (Tamošiūnienė
and Munteanu, 2015).
According to Šimašius and Vilpišauskas (2005) economic security since long time has been one
of the urgent interdisciplinary topics, but research on economic security as a separate area of economic
82
science is still developing. As economic security is understood at two (micro and macro) levels, it is
difficult to define it without analyzing the views of scientists on this topic.
By combining the findings of the different researches presented above, it can be stated that the
micro level examines the individual approach of the economic security structure of Tamošiūnienė and
Munteanu (2015) research and the private security of the Glaser (1997) national security description.
So, the household or individual economic security is the object of micro level research. Many
scientists are examining it. Human economic security is researched by Parthasarathy et al. (2014), Bloom
et al. (2010), Brown (2011), Hacker et al. (2014), Hsieh (2015), Muruthi, Lewis (2016), focusing on the
importance of savings and threats to the quality of life of adults and the elderly. The composite economic
security definition of these scientists is the personal financial provision supply, social integration, health
safety strategies, guaranteeing dignity and quality of life.
Analysing household, Muller (2015), Nam et al. (2016) also emphasize that long-term economic
security and family development depends largely on savings and accumulation of wealth. These
scientists describe economic security as a measure of individual or household capacity. The better are
indicators of economic security, the greater individual or household is protected from the negative
factors of the environment - labor, health, survivors' loss, solvency problems, the more they can expect
- quality rest, comfortable living environment, health insurance, pension fund increase.
Morris and Deprez (2013) analyze the financial sourcing, quality of life and competitiveness of
the working age women in the United States in the labor market, so the understanding of their economic
security is greatly simplified and focused on the ability of the individual to self-care. Their opinion,
economic security, is a financial situation where one can live the way he wants, not the way it is. Quinn
and Cahill (2016) analyzed the influence of different economic vulnerability measures on the general
economic security of the individual. Thus, the definition of their economic security is similar to those
already mentioned, i.e. financial opportunities, solvency, social welfare and sustainability from external
threats.
It needs also to be noticed, that the business enterprises' economic security is analyzing on micro
level. Economic security of business structures is studied by Falovič (2013), Misko and Maliuta (2015),
Kasyanova and Kasyanov (2015), Baldzhy (2017), Kočikin (2016). The definition of the economic
security of these scientists is a condition where resources are used effectively to prevent threats and
ensure the functioning and stable development of the company. They characterize economic security as
a combination of qualitative and quantitative indicators. In order to achieve the highest level of economic
security, companies must ensure maximum safety of the main functional components. Researchers
analyzing the economic security of companies distinguish the following elements of economic security:
finance, human resources, technology and innovation, political and legal environment, ecological
environment and information security.
83
Macro level is the country's economic security.
Theoretical interpretation of country's economic security
The National Security Framework Strategy of the Republic of Lithuania (1996) defines economic
security as a consistent growth of the country's economy, creating favorable conditions for the
development of all economic sectors, creating preventive measures for reducing the shadow economy,
ensuring a higher standard of living for citizens and creating a competitive economy.
Makštutis A. (2006) considers economic security to be a certain state of economic and government
institutions, where the protection of priority national interests is ensured, a harmonious, socially oriented
whole of the country's development is guaranteed, as well as a good economic and defensive potential,
despite the positive or negative development of domestic or foreign processes. This scientist separates
several economic security goals:
- stability of economic power and ability to finance defense needs;
- securing "strategic products" (eg energy, etc.);
- diversification of foreign trade;
- independence from dominant players in the international economy;
- security against economic espionage;
- good macroeconomic indicators;
- property security;
- the individual's social security, at a certain level of livelihood;
- employment secured by jobs;
- efficiency of economic activity.
Markevicius (2011), studying the functioning of a low-competitive economy in an integrated
economic environment, points out that economic security and welfare should always be a key priority
for the government as well as for political and national elites. This researcher distinguishes three national
security contexts: the first is the philosophical criterion - security should be a global value; a political
approach means shaping policy and its instruments to protect and sustain this value; from an economic
point of view, the well-being of the nation and the development of ways to improve this well-being.
Glotina (2014) analyzing modern economic security categories provides the following insights:
- economic security becomes relevant to the state and is an important element of statehood;
- economic security conceptis a rather complex, polemic and ambiguous category;
- without providing economic security, a country cannot solve the issues it faces both internally and
internationally;
84
- when assessing the economic security of a country, it is necessary to establish certain conditions
that set out the main preconditions for examining the category of economic security: differences
in national interests, limited public resources, increased competition for goods in production and
trade, increased competitiveness of individual countries, which others regard as a real threat to
national interests of the country;
- state (country) economic security is a complex socio-economic concept that reflects changing
material production conditions as well as external and internal threats to the country's economy.
Sviderskė (2014) assesses country risk in the context of economic security and sustainability.
According to her, every government in each country wants to be economically protected from any risk.
Economic instruments have long been part of the government's strategy, meaning that these measures
have an impact on other countries and their policies. From a traditional point of view, economic security
is a security against others authorities and manipulation of other powers. Referring to Rehm, Schlesinger,
2013; Quadrini, 2011; Ausloos, Miskiewicz, 2010; Rehm, Schlesinger, 2010; Marshall, Maulana, Tang,
2009; Besten, 2007; Estrada, 2000; Meldrum, 2000) this researcher presents some ideas for
understanding economic security:
- economic security is a key factor in national security, which is one of the resources to ensure a
balance between national security;
- economic security is one of the national, regional and global security aspects that aim to
economically protect and maintain every individual, community or national economy, etc.;
- the primary objective of governments, regional and international organizations is to ensure
universal human security;
- the country's economic situation, considered as a source and basis for tackling poverty, hunger,
social and economic inequalities.
Nam et al. (2016), focusing on new economic security measures, emphasizes savings and its
impact on short and long-term goals, however, starting from macroeconomic needs, they are moving
towards household goals as a basis for shaping the country's economic security. They interpret economic
security as a product of various measures, creating a method for achieving long-term and short-term
economic goals.
Western countries' scientists use the model developed by Professor Lino Briguglio to study
economic security (see Figure 2). His model reflects economic security, taking into account the country's
economy vulnerability and its capabilities, as well as the level of resistance (to combat the crisis and
prepare for shock absorption). Therefore, macroeconomic security is most often investigated by
analyzing internal and external economic security threats. According to Jakobs and Nagan (2012)
85
nuclear power security, human and international law violations are the most common economic security
threats. Papadopoulos (2011), analyzing economic security in Southern Europe, found that migration is
one of the threats to the economic security of the countries in this region. Also, Walker (2011) analyzes
the challenges of eliminating threats in the context of economic security. Hipp (2016), Angulo-Guerrero
(2017), Paraschivescu (2013), Yu (2017) investigate job losses and believe that this is a major internal
economic security threat. Johnstone et al. (2013), Sternberg (2009), Rosser (2012), analyzing the
technological development of the countries, found that technological development has a positive impact
on economic growth and economic security, and the backwardness of technology is one of the important
internal threats to economic security. Goldhau et. al (2018), Popescu (2014), Augutis et al. (2016), Franki
and Viskovich (2015) suppose energy dependence is the main external threat to economic security.
Gečienė (2016) attributed energy security to the general economic security of the country. Her
assessment is based on the subjective security perception of the Lithuanian population. The definition
of the economic security of these scientists is the reduction of the vulnerability of the country's economic
situation, the increase of resistance to internal and external threats, and the improvement of crisis
prevention mechanisms.
Tamošiūnienė and Munteanu (2015) systematize the approaches of economic security because
they believe that economic security was and will be the basis for the development of international
economic relations. Also, in another study of the same year, they analyze the economic security of the
Baltic States and Moldova using quantitative assessment methods. They understand economic security
as variables of two levels: national vulnerability and economic resilience.
Stankevičienė et al. (2013) analyzed the links between the economic security of the Baltic Sea
Region countries and the country risk indicators. These scientists describe economic security as being a
preparation for the economy to ensure proper living conditions and to develop social and economic
stability and political and military capabilities of society and country’s ability to eliminate internal and
external threats. Their opinion, the concept of economic security is not universal, it is multilateral and
multifaceted.
Scientists from Eastern countries use models of economic security assessment of critical limits
(Russian economic schools). CIS and Ukrainian scientists often use critical limits methods to assess
countries or regional economic security. Tokarev (2008) describes methods for determining the
economic security of a state, and separates three main indicators of economic security: an economic
indicator that describes the level of development of the country's economy; a social indicator that
determines the level of social state development; financial indicator assessing the country's fiscal-credit
and tax-budgeting policies. Kazantsev (2010) analyzes economic security and regional economic
security assurance. His separates the Russian Federation regions and groups them according to the
potential threats to economic security, identifies the causes of threats and ways of avoiding and
86
eliminating them. Blinichkina (2015) describes the conditions of economic security, proposes to
calculate economic security indices using the determinants system. These scientists describe economic
security as the formation of economic resilience using available resources. They also complement this
definition by introducing a mandatory condition for the development of the national economy. Dadalko
et al. (2017) argue that economic security is one of the key functions of state regulation. Ensuring
economic security leads to the realization of other functions. According to these researchers economic
security maintaining is particularly necessary in a crisis, because as the number of risks and threats has
increased, and the mechanisms for avoiding them become ineffective during the crisis. They offer several
definitions of economic security, broken down by approach (see Table 1).
Table 1. Definitions of economic security
Approach Description
By content and concept
protection individual vital interests, society, the countries and
national economic interests
state of economy, authorities, economic system
economic functioning regime
qualitative characteristics of the economic system
By subject
vital interests
national interests
economic interests
By security mechanism
assurance
without mechanism indication
normative - legal, administrative - organizational, economic,
technological, informational, etc.
By depending on the
consequences
dangers and threats
unfavorable external and internal factors
Source: based on Dadalko et al. (2017)
Tang (2015) suggests rethinking the concept of economic security as a result of the world
globalization. In his view, economic security is not only a sufficient financial provision for survival, but
it should be understood as a fight against poverty and unemployment, as an action against dangers and
threats, as a prevention against legal breaches and corruption. This is not only an existential question,
but economic security should become a priority for development of a common state security.
Summarizing the above discribed different theoretical insights of economic security, it is possible
to present such a complex definition of economic security - an economic regulation tool (a regulatory
87
mechanism), which helps to use the available resources, provides a sufficiently high and stable growth
trend of economic indicators, fights against poverty and unemployment, expands social security,
prevents a loss of competitiveness, effectively addresses economic needs, timely responds, neutralizes
and anticipates the occurrence of threats, shapes national security.
Country’s economic security concept
As mentioned earlier in this theoretical research, economic security is one of the main criteria for
national security. Ensuring National Security is the establishment of the conditions for the free and
democratic development of the Nation and the State, the protection and defense of the state's
independence, its territorial integrity and constitutional order (Lietuvos Respublikos nacionalinio
saugumo pagrindų įstatymas, 1996). Economic security in Lithuania is also widely understood and
becomes one of the most important topic of national security (Šimašius, Vilpišauskas, 2005).
The country's economic security is a complex socio-economic idea that reflects the enormous
range of production, external and internal threats to the country's ever-changing conditions (Senchagov,
2011). The main characteristics and principles of economic security are:
- country's economic development capacity;
- living standard assurance, according to social security standards;
- country's economy must be independent;
- country's economy must be stable and durable;
- there must be a positive dynamics of socio-economic indicators of social development;
- integration of national and international economic security, judgment of economic disputes
without force using.
Based on Quinn and Cahill (2016), Umbach (2010), Hacker, et al. (2014), Baldzhy (2017), Senchagov
(2011), Agbadi et al. (2017), Huet et al. (2017), Ike et al. (2015) and other quoted articles in this
theoretical research, the common country's economic security concept was proposed (see Figure 3).
88
Figure 3. Country's economic security concept
Country's economic security concept consists of 5 key components of security. The first of these
affects the level of economic development:
Investment security - private and public companies act as regulators of investment processes and
are directly involved in the investment process (Keppler, 2017). Increasing the attractiveness of capital
to the development of the national economy or reducing the risks associated with the investment process
must take into account the interests of all market participants (Trofimov, 2015). The State must create
conditions for attracting and protecting investments in its priority areas. The strongest investment
protection tool is law. According to Daujotas (2015), international direct investment is one of the main
sources of capital for developing countries, providing the necessary resources for the state infrastructure
and technological development, increase of its economic capacity. Elements of the European Union
countries investment activities have already been formed, but one more task remains to learn how to
manage them fairly (Franki and Viškovič, 2015).
Industrial security is the most important category of economic security, characterizing the level
of industrial development that covers all industrial production needs, influencing the dynamics of
production’s efficiency (Večkanov, 2007). According to Tang (2015), all industries sooner or later suffer
financial situation complications, which cause a decline in the country's trade demand and negatively
affect the overall level of economic development and security.
Scientific and technological security - realization of national interests and national economic
security is possible only on the basis of stable economic and industrial growth and development of
science, technology and innovation. It can provide sufficient social, economic and political stability in
society (Quinn and Cahill 2016). Science and innovation must influence the competitiveness and
efficiency of domestic production (Senchagov, 2002). Huet et al. (2017), analyzing the researcher's
89
contribution to economic development, conclude that it is important that new models of science and
technology development are been adapted to the socio-economic environment and resist the temptation
to overcome blind competition. Since scientific experience is needed to inspire these plans, it is
imperative that models incorporate strategies and stop the brain drain from country.
International economic security. According to Senchagov (2002), the essence of this security is
to ensure stable, independent economic development of the country, based on the principles of effective
formation, development and preservation of international economic relations. He also believes that the
ability to adapt to global market conditions, policies of governance, adaptability and liberalization are
key to sustainable economic growth. The goal of all countries is a safe relationship between exports and
imports.
Financial security is one of the most important components of economic security at a market
conditions. Dadalko et al. (2017) argue that financial security is a condition of finance and financial
institutions that provides guaranteed national economic interests protection, development of harmonious
and socially oriented domestic economy, financial system and whole set of financial relations in the
country. It is the ability of financial institutions to set up mechanisms to safeguard national finance,
endorse social interests and political stability. Financial security is the integrity of economic potential
and financial conditions, aimed at preserving the financial system even under the most unfavorable
conditions of internal and external development. Financial security is the ability to resist successfully
against internal and external threats to financial security.
Energy security is the most discussed topic in scientific literature, usually refers to the reliability
of energy resources. However, according to Sovacool (2014), there are four concepts of energy security:
- Accessibility refers to the availability of the necessary energy supply to consumers through increased
market access as well as to market conditions assurance, physical resources sufficiency, investment,
technology deployment, effective legal and regulatory mechanisms;
- Reliability is the uninterrupted energy services supply, which requires diversification of supply
chains, fuels and technologies, increased infrastructure resilience, ability to recover from a
malfunctioning system, timely access to market information;
- Affordability is not only about ensuring low prices for end-users and comparing the prices with end-
users income, but also about the price stability needed to plan energy projects and their economic
reasonableness;
- Sustainability is the efficient use of energy resources and energy while minimizing the social and
environmental impact of energy use.
The second part of the country's economic security concept is the living standard:
90
Food security - access to safe, nutritious and affordable food. This possibility is intrinsically linked
to stress or distress feelings and is strongly related to socioeconomic factors (Carter et al., 2011). Food
security research traditionally focuses on food (supply) accessibility, affordability of food price, and use
of food nutrition. Scientists who study food insecurity are interested in its causes, conditions and
experiences. Senchagov (2002), Agbadi et al. (2017), Huet et al. (2017), Ike et al. (2015) argue that food
security is an element of national security. The situation when the people have the physical and economic
capacity to access at all times the safe food they need to maintain a healthy and active life. Food security
is one of the main objectives of agricultural and economic policy.
Demographic security. Country's population and national composition security against external
and internal threats (Senčagov, 2002). Karmanov et al. (2015) think that demographic security is
characterized by the development of the socio-economic security of society against internal and external
demographic threats, which ensures the geopolitical preservation as well as economic and national state
status. Demographic imbalance affects social well-being, and demographic development is one of the
key components on which the future of countries in the medium and long-term depends. There is no
doubt that demographic development is a decisive factor in modern security studies, and demographic
factors can be signs of a security situation and possible changes (Malnar and Malnar, 2015).
Social security - a set of tools to protect the interests of the country and society in the social sphere,
development of social structures and public relations, maintenance of life systems. Social security is
violated when society begins to fear that it cannot survive on its own (Senchagov, 2002). Social security
vulnerability depends on many aspects and is caused by different factors. Migration has been widely
discussed in recent years (Kiaušienė, 2018; Abel, 2018; Kenneth and Winkler, 2015; Mence and
Parrinder, 2017; Cooke et al., 2016) as one of the most important stressors of social security today.
Law enforcement - activities during which law is enforced and realized, a system, which covers
not only the activity itself but also the activity’s subjects (law enforcement institutions, etc.). Law
enforcement is one of the key the state functions. Since the coercion monopoly is in the hands of the
state, it is the only one who can administer justice, regulate public relations, especially when
disagreements require coercive measures to restore order, peace, concord and other conditions for the
successful society existence, development and improvement (Kuconis and Nekrošius, 2001).
The third part of the country's economic security concept is internal threats:
Critical reduction of jobs, production scale and national product is the country's economy
deformation, as a result of the decline in output produced by important economic and industrial sectors,
which effect is manifested through national decline in domestic product (Dadalko et al., 2017).
The structural and technological economic backwardness is the backbone of the technological
base of many industries, which influences the inefficient use of energy and other resources, the quality
91
of the national product and high production costs. Deterioration of scientific and technological potential.
Decrease in major scientific and technological development due to lack of funding or unexpected losses.
Economic criminalization - uncontrolled extent of economic crime, shadow, tax evasion.
Banks and financial systems crises and financial panic - the financial system’s instability and
vulnerability, weak financial institutions regulation. The efficient financial system’s goal is to efficiently
redistribute resources, which will help maintain price stability and economic growth (Deksnytė, 2010).
Financial panic destabilizes the financial institutions activities that violate the financial system.
In addition, internal economic security threats include national currency destabilization, the
national bank's currency reserves lowering to the critical level, and domestic debt exceeding the state
financial capacity.
To generalize the internal threats to economic security, it can be argued that all of threats are
related to domestic production scope and financial risks. Among the internal threats, the most dangerous
are the development of social and scientific-technological spheres trends, because they are involved in
the development of strong industrial and financial systems.
The fourth component of the country's economic security concept is external threats:
- economic pressure;
- blockade or other hostile economic actions;
- the whole industry dependence on any one country or group of countries;
- capital investment for political purposes: for example, taking control of the ownership and
management of companies in the energy and other sectors of strategic importance to national
security, financial and credit institutions, major communications (rail, motorways, pipelines,
seaports, airports);
- energy dependence on the one country’s or a group of countries’ resources, energy system’s
vulnerability;
- as well as the level of external debt that destabilizes the public financial system; destabilizing
interventions in the financial-banking system and its disruptive effects (Lietuvos Respublikos
nacionalinio saugumo pagrindų įstatymas, 1996).
The fifth part of the country's economic security concept is building economic security principles
(Quinn and Cahill, 2016; Umbach, 2010; McMichael, Mindi, Schneider, 2011; Senchagov, 2002;
Dadalko et al., 2017; Trofimov, 2015):
- A clear understanding of economic security;
- The formation of economic security is based on the individual characteristics and specificities of the
entity;
- Setting thresholds for evaluation indicators;
92
- Choosing the most appropriate economic security method to ensure;
- Ensuring effective measures against corruption and economic crime;
- Optimization of economic security implementation mechanisms;
- Abolition of unfair competition and monopolies.
93
Conclusions
1. Investigation of the scientific literature of economic security has shown that the concept of
economic security is ambiguous and that research as a separate field of economic science is still being
developed.
2. Economic security is understood on two levels (micro and macro). It has been clarified that
often economic security is examined at the micro level. Household or individual and the business
enterprise economic security belongs to micro level. Internal and external threats to economic security
are being analyzed at the macro level.
3. After having analyzed various Lithuanian and foreign researches, a complex definition of
economic security has been introduced – it is - economic regulation tool (a regulatory mechanism),
which helps to use the available resources, provides a sufficiently high and stable growth trend of
economic indicators, fights against poverty and unemployment, expands social security, prevents a loss
of competitiveness, effectively addresses economic needs, timely responds, neutralizes and anticipates
the occurrence of threats, shapes national security.
4. The scientific insights in the field of economic security presented a possible general country's
economic security concept, which is divided into five parts. Four of them are the main factors that
influence a country's economic security: level of economic development (investment, industrial,
scientific and technical, international economic, financial, energy security), living standard (food,
demographic, social security and law enforcement), internal and external threats. The fifth part of the
country's economic security concept is principles - foundation of economic security: the formation of
economic security is based on the individual characteristics and specificities of the entity; setting
thresholds for evaluation indicators; choosing the most appropriate economic security method to ensure;
ensuring effective measures against corruption and economic crime; optimization of economic security
implementation mechanisms; abolition of unfair competition and monopolies.
94
References
Abel, G. (2018). Non-zero trajectories for long-run net migration assumptions in global population
projection models. Demographic Research, 38, p. 1635-1662.
Agbadi, P., Urke, H., & Mittelmark, M. (2017). Household food security and adequacy of child diet in
the food insecure region north in Ghana. PLoS One, Vol. 12, Nr. 5, Retrieved from:
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0177377.
Angulo-Guerrero, M., Pérez-Moreno, S., & Abad-Guerreroa, I. (2017). How economic freedom affects
opportunity and necessity entrepreneurship in the OECD countries. Journal of Business Research, Vol.
73, p. 30 - 37.
Augutis, J., Krikštolaitis, R., Martišauskas, L., Ušpuras, E., & Žutautaitė, I. (2016). Energetinio
saugumo tyrimų raida Lietuvoje. ENERGETIKA, Vol. 62, Nr. 4, p. 232 - 246.
Baldzhy, M. (2017). Economic risks management in projects of development and environmental &
economic security. Economic Processes Management: International Scientific E-Journal, Vol. 2,
Retrieved from: http://epm.fem.sumdu.edu.ua/download/2017_2/epm2017_2_7.pdf.
Bloom, D., Mahal, A., Rosenberg, L., & Sevilla, J. (2010). Economic security arrangements in the
context of population ageing in India. Social security and the challenge of demographic change, Vol.
63, Nr. 3-4, p. 59 - 89.
Brown, R. (2011). Economic Security in an Aging Canadian Population. Canadian Journal on Aging /
La Revue canadienne du vieillissement, Vol. 30, Nr. 3, p. 391 - 399.
Carter, K. N., Kruse, K., Blakely, T., & Collings, S. (2011). The association of food security with
psychological distress in New Zealand and any gender differences. Social Science & Medicine, Vol.
72, p. 1463 – 1471.
Cooke, T., Mulder, C., & Thomas, M. (2016). Union Dissolution and Migration. Demographic
Research, Vol. 34, p. 741 - 760.
Daujotas, R. (2015). Tarptautinė investicijų teisė ir arbitražas, Monografija. Vilnius: Eugrimas.
Deksnytė, I. (2010). Finansinės sistemos stabilumo vertinimas šalyje: Lietuvos atvejis. Ekonomika ir
vadyba: aktualijos ir perspektyvos, Vol. 1, Nr. 17, p. 34 – 42.
Franki, V., & Viskovic, A. (2015). Energy security, policy and technology in South East Europe:
externalities and asymmetric investment incentives. Energy Policy, Vol. 105, p. 562 - 570.
Gečienė, I. (2015). Lietuvos gyventojų subjektyvus saugumo suvokimas išorinių grėsmių kontekste.
Socialinių tyrimų žurnalas, Vol. 6, Nr. 1, p. 55 - 69.
Glaser, C. (1997). The Security Dilemma Revisited. World Politics, Vol. 50, Nr. 1, p. 171-201.
95
Goldthau, A., & Keating, M. (2018). The International Political Economy of Energy and Natural
Resources. Cheltenham, UK, Northhampton, MA, USA: Edward Elgar Publishing.
Hacker, J., Huber, G., Nichols, A., Rehm, P., Schlesinger, M., Valleta, R., & Craig, S. (2014). The
Economic Security Index: A New Measure for Research and Policy Analysis. The review of Income
and Wealth, Special Issue: Economic Insecurity: Challenges, Issues and Findings, Vol. 60, Nr. 1, p. 5 -
32.
Hipp, L. (2016). Insecure times? Workers' perceived job and labor market security in 23 OECD
countries. Social Science Research, Vol. 60, p. 1 - 14.
Hsieh, N. (2015). Economic Security, Social Cohesion, and Depression Disparities in Post-Transition
Societies: A Comparison of Older Adults in China and Russia. Journal of Health and Social Behavior
(JHSB), Vol. 56, Nr. 4, p. 34 - 51.
Huet, C., Ford, J., Edge, V., Shirley, J., King, N., & Harper, S. (2017). Food insecurity and food
consumption by season in households with children in an Arctic city: a cross-sectional study. Public
Health, 17:578 DOI 10.1186/s12889-017-4393-6.
Ike, C., Jacobs, P., & Kelly, C. (2015). Towards Comprehensive Food Security Measures. Comparing
Key Indicators. Africa Insight, Vol. 45, Nr. 3, p. 91 – 110.
Jacobs, G., & Nagan, W. (2012). Nuclear Threats and Security. Cadmus: Promoting Leadership in
Thought that Leads to Action, Vol. 1, Nr. 5, p. 174 - 179.
Johnstone, C., Pratt, D., Clarke, J., & Grant, A. (2012). A techno-economic analysis of tidal energy
technology. Renewable Energy, Vol. 49, p. 101 - 106.
Kasyanova, N., & Kasyanov, A. (2014). Diagnosing economic security of an industrial enterprise.
Economy of Industry, Vol. 65, Nr. 1, p. 136 - 144.
Kazantsev, S. (2012). Economic security and assessment of economic protectability of regions.
Regional Development, Vol. 2, Nr. 1, p. 34 - 40.
Kenneth, J., & Winkler, R. (2015). Migration Signatures across the Decades: Net Migration by Age in
U.S. Counties, 1950–2010. Demographic Research, vol. 32, p. 1065 – 1080.
Keppler, J. H. (2017). Rationales for capacity remuneration mechanisms: Security of supply
externalities and asymmetric investment incentives. Energy Policy, Vol. 105, p. 562 - 570.
Kiaušienė, I. (2018). Review One: The Impact of Migration Factors on International Migration
Decisions: A Behavioural Economics Perspective . ransformations in Business and Economics, Vol.
17, Nr. 2, p. 246 - 252.
Knutsen , C. H. (2011). Security Threats, Enemy-Contingent Policies, and Economic Development in
Dictatorships. International Interactions, Vol. 37, Nr. 4, p. 414 - 440.
Kuconis, P., & Nekrošius, V. (2001). Teisėsaugos institucijo. Vilnius: Justitia.
96
LIETUVOS RESPUBLIKOS NACIONALINIO SAUGUMO PAGRINDŲ ĮSTATYMAS. (1996, 12
19). Vilnius, Lithuania: Retrieved from: https://e-
seimas.lrs.lt/portal/legalAct/lt/TAD/TAIS.34169/ZXfEMCxiex.
Makštutis, A. (2006). SECURITY COOPERATION: THE VIEWS OF LITHUANIA. In
MONOGRAPH: TENDENCY OF NATIONAL SECURITY IN THE BALTIC SEA REGION (pp. p. 179 -
192). Vilnius: THE GENERAL JONAS ŽEMAITIS MILITARY ACADEMY OF LITHUANIA,
INTERNATIONAL ACADEMY OF ORGANIZATION AND MANAGEMENT SCIENCE.
Malnar, D., & Malnar, A. (2015). Demographic Security Trends in Southeastern Europe. CIRR,
Vol.21, Nr. 73, p. 57 - 87.
Markevicius, N. (2011). Low competitive economy functioning in the integrated economic space.
Socialinių mokslų studijos: mokslo darbai, Vol. 2, Nr. 3, p. 505 - 518.
McMichael, P., & Schneider, M. (2011). Food Security Politics and the Millennium Development
Goals. Third World Quarterly, Vol. 32, Nr. 1, p. 119 - 139.
Mence, V., & Parrinder, A. (2017). Environmentally related international migration: Policy challenges.
Long Way to Go: Irregular Migration Patterns, Processes, Drivers and Decision-making, p. 317 –
342.
Misko, G., & Maliuta, O. (2015). Dairy plants financial and economic security evaluation. Соціально-
економічні проблеми і держава, Vol. 1, Nr. 12, p. 153 - 159.
Morris, L., & Deprez, L. (2013). The faltering safety net in a reluctant nation: Women's economic
security at risk in America. Women's Studies International Forum, Vol. 47,
10.1016/j.wsif.2013.11.002.
MULLER, C. (2015). CALIFORNIA STATE UNIVERSITY CATASTROPHIC (CAT) LEAVE
DONATION PROGRAM: DEMOGRAPHICS, ECONOMIC SECURITY, AND SOCIAL EQUITY.
Journal of Health and Human Services Administration, Vol. 38, Nr. 1, p. 108 - 159.
Muruthi, J. R., & Lewis, D. C. (2016). Factors Impacting Ols-ege Economic Security: Views of
Presenting and applyjing an energy security index to Croatia. Energy, Nr. 90, p. 494 - 507.
Nam, Y., Lee, Y., McMahon, S., & Sherraden, M. (2016). New Measures of Economic Security and
Development: Savings Goals for Short‐ and Long‐Term Economic Needs. The Journal of Consumer
Affairs , Vol. 50, Nr. 3, p. 611 - 637.
Papadopoulos, A. (2011). Migration and security threats in south-eastern Europe. Journal of Southeast
European and Black Sea Studies, Vol. 11, Nr. 4, p. 451 - 469.
Paraschivescu, C. (2013). SOCIAL PROTECTION AND ECONOMIC SECURITY OF NORTH
AFRICAN MIGRANT WORKERS IN FRANCE. Romanian Journal of Sociology, Vol. 1, p. 91 - 99.
97
Parthasarathy, P., Dailey, D., Young, M.-E., Lam, C., & Pies, C. (2014). Building Economic Security
Today: making the health-wealth connection in Contra Costa county's maternal and child health
programs. Matern Child Health, Vol. 18, Nr. 2, p. 396 - 404.
Popescu, M. F. (2015). The Economics and Finance of Energy Security. 22nd International Economic
Conference – “Economic Prospects in the Context of Growing Global and Regional
Interdependencies”, IECS 2015 (pp. p. 467 - 473). Procedia Economics and Finance, Vol. 27.
Quinn, J., & Cahill, K. (2016). The new world of retirement income security in America. American
Psychologist, Vol. 71, Nr. 4, p. 321 - 333.
Rosser, S. (2012). Breaking Into the Lab: Engineering Progress for Women in Science. New York:
NYU Press.
Sovacool, B. (2014). Diversity: Energy studies need social science. Nature, 511, p. 529 - 530.
Stankevičienė, J., Sviderskė, T., & Miečinskienė, A. (2013). Relationship between Economic Security
and Country Risk Indicators in EU Baltic Sea Region Countries. Entrepreneurial Business and
Economics Review, Vol. 1, Nr. 3, p. 21 - 34.
Sternberg, E. (2009). Prospects for Building Regional Clusters of Economic Strength in Security
Technologies: The Case of New York State. Journal of Applied Security Research, Vol. 4, p. 341 -
362.
Sviderskė, T. (2014). Country Risk Assessment in Economic Security and Sustainability Context:
doctoral disseration. Vilnius: VGTU leidykla „Technika“.
Šimašius, R., & Vilpišauskas, R. (2005). The Concept of Economic Security and the Principles of
Economic Security Policy in Lithuania. Lithuanian Annual Strategic Review, p. 249-266.
Tamošiūnienė, R., & Munteanu, C. (2015). Current Research Approaches to Economic Security. New
Challenges in Business Research : 1st International Conference on Business Management (pp. p. 55 -
58). Valencia (Spain) : Universitat politècnica de València. Valencia : Universitat politècnica de
València.
Tamošiūnienė, R., & Munteanu, C. (2015). Modern Approaches in Quantifying Economic Security.
Case Sudy of Estonia, Latvia, Lithuania and Republic of Moldova. Journal of Security and
Sustainability Issues : International Entrepreneurial Perspectives and Innovative. Outcomes 4.4, p.
596 - 610.
Tang, S. M. (2015). Rethinking economic security in a globalized world. Contemporary Politics, Vol.
21, No. 1, p. 40 – 52.
Umbach, F. (2010). Global energy security and the implications for the EU. Energy Policy, Nr. 38, p.
1229 – 1240.
Walker, J. (2011). Security Challenges and Threats for the CISO's Agenda. EDPACS, Vol. 44, Nr. 1, p.
1 - 13.
98
Блиничкина, Н. (2015). Особенности расчета индекса экономической безопасности в аспекте
взаимосвязи «инновационная безопасность - инновационная культура». ЭКОНОМИКА
РЕГИОНА, Vol. 4, p. 179 – 185.
Вечканов, Г. С. (2007). Экономическая безопасность. Могография. Санкт-Петербург:
Издательский Дом ПИТЕР.
Глотина, И. (2014). Современная архитектоника исследования категории «Экономическая
безопасность». Вестник Кемеровского государственного университета, Vol. 1, Nr. 3 (59), p. 236
- 238.
Дадалко, В., Исаев, Н., Чекмарев, В., & Соловкина, Е. (2017). Эффективность Системы
Экономической Безопасности Субъектов Российской Федерации. Вестник ВСГУТУ, Vol. 1, Nr.
64, p. 85 – 95.
Карманов, М. В., Кучмаева, О. В., & Петрякова, Л. О. (2015). ДЕМОГРАФИЧЕСКАЯ
БЕЗОПАСНОСТЬ: ТЕОРИЯ, МЕТОДОЛОГИЯ, ОЦЕНКА. Экономика, Статистика и
Информатика, Nr. 4, p. 123 - 128.
Кочикин, И., & Буянский, С. (2016). Обеспечение экономической безопасности в сфере
кредитования. Вестник ВолГУ, Vol. 2. Nr. 35, p. 83 - 89.
Сенчагов, В. (2011). Инновационные преобразования как императив экономической
безопасности региона: система индикаторов. Инновации,Nr. 5., Retrieved from:
https://cyberleninka.ru/article/n/innovatsionnye-preobrazovaniya-.
Токарев, К. (2008). Современные методы оценки уровня экономической безопасности
государства. Российское предпринимательство, Vol. 8, Nr. 2, Retrieved from:
https://cyberleninka.ru/article/n/sovremennye-metody-otsenki-urovnya-ekonomicheskoy-bezopasnosti-
gosudarstva (дата обр.
Трофимов, М. Н. (2015). Инвестиции и механизм их воздействия на экономическую
безопасность. Безопасность бизнеса, Nr. 2015/2, p. 21 – 28.
Фалович, А. (2013). Дослідження сучасних напрямків визначення сутності економічної безпеки
підприємства. Соціально-економічні проблеми і держава, Vol. 1, p. 271 - 178.
99
IMPACT OF FOREIGN DIRECT INVESTMENT ON TAX REVENUE
Ligita GASPARĖNIENĖ
Mykolas Romeris
University,Vilnius,
Lithuania
RITA REMEIKIENĖ
Mykolas Romeris
University,Vilnius,
Lithuania
Renata ŠIVICKIENĖ
Mykolas Romeris University,
Vilnius, Šiauliai State
College, Šiauliai, Lithuania
Abstract: Foreign direct investment (FDI) is recognized as one of the key factors for
economic development of the country by stimulating foreign trade, technology transfer and
etc. The study attempts to analyze the impact of foreign direct investment on tax revenue.
The empirical part of the paper examines the relationship between FDI and tax revenues,
examines the effects of foreign direct investment on different types of taxes (personal
income taxes, value added taxes and corporate income taxes), using data for the period
2008 to 2017. The methods of the research include systematic and comparative analysis of
the scientific literature, correlation and regression analysis. The study finds that foreign
direct investment has positive and significant impact on total tax revenue, but the impact
on different types of taxes differs. The results showed the biggest impact on value added
tax revenues. So the study concludes the positive contribution of foreign direct investment
in tax revenue.
Keywords: foreign direct investment (FDI), tax revenue, value added tax, personal income
tax, corporate income tax.
100
Introduction
Foreign Direct Investment (FDI) is considered to be one of the key drivers for
economic growth. Due to this fact, a number of studies have been done to determine its’
impact on the country's economic development. Many authors (Bayar, Ozturk, 2018;
Magombey, Odhiambo, 2017; Iqbal, Mahmood, 2016; Agrawal, Khan, 2011; and others)
agree that FDI has a positive impact on the host country's economic growth in the following
ways: promoting new jobs, increasing the local country's capital, introducing new
technologies and technical experience, promoting export.
Theoretical models (including the neo-classical trade theory) focusing on the effect
that FDI has on a host country‘s general welfare and tax revenue showed that FDI could
increase national welfare, particularly through in-creased tax revenue (Faeth, 2011). Welfare
and revenue from FDI can also be improved by introducing an optimal tax on foreign-owned
capital. Countries could lose out on tax revenue when incentives are paid to multi-
nationalenterprise (MNEs) or when transfer pricing (including other strategies tominimise
taxes) is an issue (Faeth, 2011).
Problem of the research. There is a lack of the studies to analyse how FDI influence
tax revenues and if the increase of FDI in separated economic branches determines the
growth of tax revenues as well.
Lithuania offers tax exemptions to foreign companies and conditions have made it
easier for companies to set up their businesses. The country made positive reforms in four
key areas: obtaining construction permits was facilitated, connecting to electricity networks
was improved, minority investors were better protected, and the tax payment system became
electronic. As such, Lithuania was second in the whole of Europe and Central Asia for the
number of reforms leading to an improvement in the conditions for business. So it is
important to analyse how attracted FDI impacts tax revenue.
This article is aimed at researching the impact of FDI in host countries for tax revenues
of the Lithuanian example. The aim has been detailed into the following objectives: 1) with
reference to the scientific literature, to assess the impact of foreign direct investment on tax
revenues; 2) to perform comparative analysis of foreign direct investment and tax revenue
during period 2008 - 2017; 3) empirically evaluate impact of FDI on Lithuanian tax revenue,
using methods such as correlation and regression analysis. Methods of the research include
101
systematic and comparative analysis of the scientific literature, correlation and comparative
data analysis.
Theoretical background of the impact of FDI on tax revenues
Foreign companies transfer modern management, production management,
technology engineering solutions, organisational management experience while
implementing in the local market of the country. In this way, local business and country’s
manufacturing productivity are being promoted to grow up and develop (Kuliavienė,
Solnyskinienė, 2014).
Capital is exported and imported in international (foreign) investment form. According
to foreign investments it can be decided about size of the country's attractiveness for the
international market, the country's economic relations with other countries (Davulis, 2003).
So one of the country's economic integration into the global market indicators is foreign
direct investment. However, some authors argue that the FDI positive impact on the
economy of the country has only on a short period (Miyagawa, Ohno, 2009), and the
distinction between the short-term level effect and long-term rate effect of FDI on the
productivity of domestic firms is important. If resources must be expended in order to learn
from foreign-invested firms, it is possible that the spillovers have a negative effect on the
productivity of domestic firms in the short run yet a positive effect on the productivity of
domestic firms in the long run, because such learning helps enhance the firm's future
productive capacity (Liu, 2008).
Developing countries perceive FDI as a panacea for addressing their low investments,
foreign exchange shortages, and tax revenue gaps, etc. and providing a wide range of
incentives to attract the FDI inflows among which tax incentives assume important place. In
general, there are several theoretical and empirical evidences available on the role and
impact of FDI and a few on tax revenues.
Mahmood and Chaudhary (2013) studied the impact of FDI on the tax revenue in
Pakistan from 1972 to 2010, using the ADF, Phillip-Perron and Ng-Perron tests and the
ARDL model. In order to disclose the impact, the survey used indicators - GDP per capita
and FDI as independent variables - and tax revenue was used as a dependent variable. The
results showed that both GDP per capita employment and FDI have a positive and significant
102
impact on Pakistan's tax revenue. Okey (2013) studying economic indicators also found a
positive impact of FDI on tax revenue by analyzing Sri Lanka's indicators.
Nguye, Nguyen and Goenka (2013) examined the implications of high net inflows
foreign direct investment (FDI) characterized by number of entries of heterogenous
multinational firms on corporate tax revenues’ decline. They showed that the impact of FDI
on tax revenue will depend on the competition effect, demand creation effects, technology
transfer cost and the technological spillovers. They argue that the competition effect reduces
production of domestic firms and thereby, lowers the level of corporate tax revenue while
the technological spillovers could be positive or negative due to the absorptive capacity of
local firms.
Bunescu and Comaniciu (2014) analyzed the economic and non-economic factors
affecting the tax revenues in 27 EU countries during 1995-2011 period with correlation
analysis and revealed that FDI inflows had a weak positive effect on the tax revenues. On
the other side Tabasam (2014) researched the interaction among tax revenues and FDI
inflows in Pakistan over the period 1975-2012 using time series analysis and discovered that
FDI inflows affected the tax revenues negatively. Gaalya (2015) researched the same
relationship for Uganda during the period 1994-2012 with regression analysis and reached
the same findings. Aslam (2015) also analyzed the long run interaction between FDI inflows
and tax revenues in Sri Lanka during 1990-2013 period and found that FDI inflows made a
significant positive contribution to the tax revenues.
Balkcioglu et al. (2016) researched the effect of FDI inflows on the tax payments of
the firms with different technology levels in Turkey during 2004-2012 period and discovered
that FDI inflows increased the tax payments by the firms and the effect was found to be
highest in the firms with high technology level. Odabas (2016) researched the causal
interaction between tax revenues and FDI inflows in 7 EU transition economies during 1996-
2012 period using causality test of Dumitrescu and Hurlin (2012) and discovered a one-way
causality from FDI inflows to the tax revenues.
Jeza, Hassen and Ramakrishna (2016) analyzed impact of FDI on tax revenue using
the data for the study period (1974-2014). They propose that, offering tax incentives to
attract FDI may lead to a significant revenue decline in Ethiopia. Among different tax
revenue types, the corporate income tax revenue has been highly affected due to tax holiday
provision.
103
Bayar and Ozturk (2018) analyzed the short and long run interaction among tax
revenues, FDI inflows and economic growth were analyzed in 33 OECD countries during
1995-2014 period with Dumitrescu and Hurlin (2012) causality test and Westerlund-Durbin-
Hausmann (2008) cointegration test. The results revealed that both FDI inflows and
economic growth did not have significant effects on the total tax revenues at the panel level.
However, FDI inflows affected the total tax revenues positively in Iceland, Israel, Sweden,
the United Kingdom, and the United States, while FDI inflows affected the total tax revenues
negatively in Austria, France, Italy, and Poland. They evaluated that the composition of FDI
inflows and the level of financial incentives by host countries are determinative for the
relationship between total tax revenues and FDI inflows.
Ade, Rossouw and Gwatidzo (2018) investigated the determinants of tax revenue
performance in the SADC for the period 1990-2010, using panel data estimation techniques.
Specifically, the aim was to ascertain the level of causality of FDI and taxation (CIT rates,
VAT rates, tax policy harmonisation variables) amongst other variables on collected tax
revenue in the region. The results showed that tax revenue collected in the SADC is sensitive
to tax rates (VAT and CIT rates) and tax policy harmonisation variables, but insensitive to
FDI inflows.
According to Basheer, Ahmad and Hassan (2019) tax revenue is affected by both
economic and financial factors of the countries, the key factors which have a significant
contribution both from economic and financial factors are GDP growth, Bank capital to asset
ratio, Risk premium on lending, Foreign direct investment net inflow and Cash surplus
deficit. From the results, it is found that in Oman and Bahrain during 1990-2010 period the
economic variables such as GDP growth, Foreign direct investment net inflow and Cash
surplus deficit had a greater impact on Tax Revenue than those on financial variables.
Methodology of the research
The empirical research was based on the methods of regression and correlation
analysis. For the evaluation of the FDI impact on tax revenues, the data showing FDI and
tax revenues in Lithuania during the period 2008 – 2017 was engaged. Examining the impact
of FDI on Lithuania tax revenue volumes were calculated correlation coefficients between
tax revenue and FDI in individual sectors of the economy which are the most important
Lithuania.
104
Evaluating impact of FDI to tax revenue in this research was performed regression and
correlation analysis between FDI and different types of taxes. The taxes were chosen
according to research, performed by Rudytė, Šalkauskienė, Lukšienė (2009), who performed
an assessment of the efficiency of the Lithuanian tax system in accordance with the
diagnostic evaluation criteria distinguished by Tanzi. They determined that the following
basic taxes forms the basis of the country's tax revenue: value added tax (VAT), personal
income and profit taxes.
Correlation is a statistical measure that indicates the extent to which two or more
variables fluctuate together. A positive correlation indicates the extent to which those
variables increase or decrease in parallel; a negative correlation indicates the extent to which
one variable increases as the other decreases.
The correlation coefficient indicates whether there is a connection between variables.
The more value is closer to 1, the more the connection is stronger. It is important to note that
the strongest correlation is when the correlation coefficient is from 0.7 till 0.99 value. The
results are presented showing their statistical reliability. The levels of reliability are as
follows: if p>0.05 – it is statistically unreliable; if p<0.05 – it is statistically reliable.
FDI and tax revenue analysis in Lithuania
In recent years more and more companies have started to trade on international markes.
At the same time, the internationalization of developed economies has taken a new direction
through outward foreign direct investment (FDI). This dramatic increase in the exchange of
goods and services – within a progressive liberalization of international economic relations
– has generated considerable interest in the dynamics of trade and investments.
105
Figure 1. Foreign direct investment, million EUR
Source: Statistics Lithuania
The analysis of foreign direct investment and tax revenues indicates noticeable
changes. The investment volume during the analysis period increased from 9191 million
euro to 14816 million euro, so their volume increased by 61,2 percent. Integration into the
European Union was one of the accelerators for growth of FDI as well. The figure shows
that the largest increase in FDI recorded in 2012, because FDI in the beginning of 2011 was
- 11029 million euro (an increase of 1072 million euro), while the decrease of FDI was not
recorded in the analyzed period. Summing up the FDI dynamics during the analyzed period,
it can be stated that the overall development shows the growing investment attractiveness of
Lithuania. Lithuania offers tax exemptions to foreign companies and conditions have made
it easier for companies to set up their businesses. The country made positive reforms in four
key areas: obtaining construction permits was facilitated, connecting to electricity networks
was improved, minority investors were better protected, and the tax payment system became
electronic.
Lithuania’s six Free Economic Zones (FEZ), at Kaunas, Klaipėda, Šiauliai, Kėdainiai,
Panevėžys and Marijampolė, have been key for attracting FDI to the country, due to the
companies located there receiving special economic and legal operating conditions.
Businesses choosing to locate to one of the FEZs has zero per cent corporate income tax
during their initial 10 years of operation, 7.5 per cent tax over the next six years, and no tax
on dividends and real estate.
9 191 9 206 10 031 11 029 12 101 12 720 12 747 13 497 13 926 14 816
0
5 000
10 000
15 000
20 000
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
106
Lithuania has most attracted FDI in manufacturing, trade, transport, financial
intermediation and real estate sectors. In these areas during the period invested on average
about 70% of all incoming FDI in Lithuania.
The manufacturing industry was a branch which attracts largest amount of FDI till
2014. In 2008, investment in this area amounted to 2.1 billion euro, or even 22.4 % of the
total FDI in Lithuania received. Most of this area has attracted investments in 2012, when
investment reached 3.1 billion euro limit, or even 26 % of total FDI. It may be noted that
FDI in the manufacturing industry increased especially after Lithuania's accession to the EU.
The opening up of the EU's border led to reduced barriers and facilitated trade with other
EU countries. In Lithuania made items has become easier to export to other Community
countries. Investors considered that possibility and trusted Lithuanian market. From 2014
the leading branch which attracts largest amount of FDI is Financial and insurance activities.
In 2017 investment in this area amounted to 3.9 billion euro, or even 26.4 % of the total FDI
in Lithuania received.
Figure 2. Lithuanian tax revenue, million EUR
Source: Statistics Lithuania
In 2008, the world was shaken by an economic crisis whose negative effects had
impact on the economic situation in most countries. During the analyzed period, the tax
revenue volume increases, decrease was recorded only in 2009 and 2010. Comparing FDI
and tax revenue volume trends, it is noted that FDI grew faster than investment. During the
analysis period, tax revenue increased by 24.4 per cent.
9943,443
8122,69 7908,8018464,27
8944,1089376,7559980,386
10726,38511450,879
12367,555
0
2000
4000
6000
8000
10000
12000
14000
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
107
In order to justify the assumption that FDI has an impact on tax revenue volumes it
was performed correlation and regression analysis.
The estimated correlation coefficient between tax revenue and FDI of 0.796 (p-value
<0,05) shows that between the indicators is a strong linear relationship. By calculating the
correlation coefficient can be calculated slope of the regression equation: the sign means
"dependency direction”, and size - changes the scale of what it is, the more changes the
dependent, the independent amount of change at the same pace.
Using Microsoft Excel function Slope, this factor is 1.08. This means that FDI
increased by 1 euro, tax revenue will increase by 1.08 euro.
Examining the impact of FDI on Lithuania tax revenue volumes were calculated
correlation coefficients between tax revenue and FDI in individual sectors of the economy
which are the most important Lithuania.
Table 1. Tax revenue and FDI in individual sectors of the economy in Lithuania, million
Eur
Years FDI Tax
revenue
Electricity,
gas, steam
and air
conditioning
supply
Financial
and
insurance
activities
Manufac-
turing
Wholesale
and retail
trade;
repair of
motor
vehicles
and
motorcycle
2008 9190,33 9943,443 679,65 1545,21 2060,45 1300,9
2009 9206,19 8122,69 483,19 1716,13 2339,24 1316,47
2010 10030,97 7908,801 618,04 1877,74 2682,5 1305,91
2011 11028,93 8464,27 583,32 2156,98 2931,08 1418,78
2012 12100,64 8944,108 647,61 2397,6 3144,74 1395,46
2013 12719,9 9376,755 599,59 2945,5 3136,06 1379,69
2014 12746,53 9980,386 278,8 3686,49 2476,19 1511,25
108
2015 13496,82 10726,385 265,66 3532,09 2761,85 1611,2
2016 13925,59 11450,879 290,71 3867,28 2579,36 1847,32
2017 14816,47 12367,555 306,34 3916,28 2779,27 2018,12
Corr.
coeff. R
0,796 0,708332 0,956223 0,471512 0,853828
P-value 0,005892 0,021871 0,000015 0,16891 0,001668
Source: Statistics Lithuania
According to contents of table is clear that, there is a very strong and significant direct
connection between foreign direct investment and tax revenue, but the calculation analysis
of indicators by economic branches shows that the results are a little bit different. In this
paper it was identified four economic branches with the largest FDI. Correlation coefficient
showed that in Financial and insurance activities and Wholesale and retail trade branches
exist a direct link between FDI and tax revenue (0.956 and 0.854).
In summary, that despite the fact that foreign direct investment and revenue volumes
have a very strong direct link, not all economic sectors felt the same connection.
The Republic of Lithuania implements a business-friendly taxation policy, and the
taxation system is adapted to the legislation of the European Union. Since 1990, the
Lithuania's taxation system has drastically changed to support foreign investments and
labour market development.
Taxes and other payments are collected to the budget based on the order by the
Supreme Council; however, regional and city councils act separately in matters of tax
collection. In Lithuania, basic principles of tax payment and their regulation is governed by
the Law on Tax Administration stipulating the rights and obligations of a tax administrator
and tax payer, as well as the tax calculation procedure and chargeable amounts.
In order to justify the assumption that FDI has an impact on tax revenue volumes it was
performed correlation and regression analysis among FDI and different types of taxes.
Table 2. FDI and value added, personal income and corporate income taxes in Lithuania,
million Eur
109
Years FDI Value added tax Personal income
tax
Corporate
income tax
2008 9190,33 2593,043 2118,067 887,784
2009 9206,19 1960,85 1097,094 489,272
2010 10030,97 2180,499 1004,967 276,26
2011 11028,93 2443,756 1092,69 252,871
2012 12100,64 2520,82 1159,734 432,917
2013 12719,9 2611,225 1249,824 476,662
2014 12746,53 2764,438 1325,393 499,767
2015 13496,82 2888,216 1439,5 573,882
2016 13925,59 3026,282 1547,842 627,648
2017 14816,47 3309,606 1626,751 631,03
Corr. coeff.
R 0,8767136 0,1129131 0,1017422
P-value 0,000869 0,756128 0,779729
Source: Statistics Lithuania
Analyzing impact on tax revenue, the impact on different types of taxes were analysed.
For this research were chosen taxes which forms the basis of the country's tax revenue: value
added tax (VAT), personal income and profit taxes. According to content of 2 table is clear,
that there is a very strong an statistically reliable direct connection between FDI and value
added tax (0,8767). On the other hand, statistically reliable impact on personal income tax
and corporate income tax during 2008-2017 period was not found.
Conclusion
• The analysis of the scientific literature has revealed that the increase of FDI leads to
the increase of tax revenue. FDI inflows also may contribute to the development of
financial sector, raising the competitiveness and tax revenues indirectly.
• The volume of FDI during the period 2008 – 2017 in Lithuania altered in accordance
with the economic cycle: from 2008, FDI in the country was only increasing as a
result of the recovery from difficult economic situation worldwide, company's share
110
capital and reinvestment reduction; from 2008 to 2017, the volume of FDI in
Lithuania increased by almost 61.2 % as a result of the recovery of developed
economies and investment promotion inside the country during the post-crisis period;
• During the analyzed period, the tax revenue volume increases, decrease was recorded
only in 2009 and 2010. Comparing FDI and tax revenue volume trends, it is noted
that FDI grew faster than tax revenue. During the analysis period, tax revenue
increased by 24.4 percent and FDI increased by 61,2 percent;
• The estimated correlation coefficient between tax revenue and FDI of 0.796 shows
that between the indicators is a strong linear relationship. The result of correlation
between FDI and tax revenue proves results of Mahmood and Lee (2019), Balkcioglu
et al. (2016), Bayar and Ozturk (2018), that FDI has a positive and significant impact
on tax revenue, so the FDI is helpful in raising general welfare through raising the
tax revenue to the government.
• Correlation coefficient showed that in Financial and insurance activities and
Wholesale and retail trade branches exist a strong direct link between FDI and tax
revenue (0.956 and 0.854).
• Correlation coefficient showed that there is a very strong and statistically reliable
direct connetion between FDI and value added tax. But statistically reliable impact
on personal income tax and corporate income tax during 2008-2017 period was not
found. It shows that the corporate income tax revenue probably has been affected
due to tax holiday provision. This issue was not addressed in this paper and needs
further investigation.
111
References
Ade, M., Rossouw, J., & Gwatidzo, T. (2018). Determinants of tax revenue performance
in the Southern African Development Community (SADC).
Agrawal, G., Khan, A. (2011). Impact of FDI on GDP: A Comparative Study of China
and India. International Journal of Business and Management, 6(10), 71-79 [online].
[cit. 2018-11-26]. Available: http://dx.doi.org/10.5539/ijbm.v6n10p71
Aslam, A.L.M. (2015). A case study of Cointegration relationship between Tax Revenue
and Foreign Direct Investment: Evidence from Sri Lanka. Second International
Symposium -2015, pp 246-251.
Balıkcıoglu, E., Dalgıc, B., Fazlıoglu, B. (2016). Does Foreign Capital Increase Tax
Revenue: The Turkish Case. International Journal of Economics and Financial Issues,
6(2).
Basheer, M., Ahmad, A., & Hassan, S. (2019). Impact of economic and financial factors
on tax revenue: Evidence from the Middle East countries. Accounting, 5(2), 53-60.
Bayar, M. U. S. Y., Ozturk, O. F. (2018). Impact of foreign direct investment inflows on
tax revenues in OECD countries: A panel cointegration and causality analysis.
Theoretical and Applied Economics, Volume XXV, No. 1(614), 31-40.
Bunescu, L., Comaniciu, C. (2014). Analysis of correlation between tax revenues and
other economic indicators in European Union member states. Studies in Business &
Economics, 9(1).
Davulis, G. (2003). Ekonomikos teorija. V.: Lietuvos teisės universitetas.
Faeth, I. (2011). Foreign Direct Investment in Australia: Determinants and
Consequences. Custom Book Center, University of Melbourne.
Gaalya, M.S. (2015). Trade liberalization and tax revenue performance in Uganda.
Modern Economy, 6(02), p. 228.
<http://file.scirp.org/pdf/ME_2015021114525929.pdf>
Iqbal, Z., & Mahmood, A. (2016). ARDL Approach for Determinants of Foreign Direct
Investment (FDI) in Pakistan (1961-2013): An Empirical Study. Global Journal of
Quantitative Science, 3(2).
Jeza, M.T., Hassen, A.A., Ramukrishua, G. (2016). The impact of foreign direct
investment (FDI) flows on aggregate and disaggregate tax revenues in Ethiopia: a
BOUNDS test with a structural break. Asian- African Journal of Economics and
Econometrics, Vol. 16, No. 2, 171 -201.
Kuliavienė, A., Solnyskinienė, J. (2012). The evaluation of the impact of foreign direct
investment on Lithuanian economy using Lag-analysis. ECONOMICS AND
MANAGEMENT: 2014. 19 (1), p. 16-24.
Liu, Z. (2008). Foreign direct investment and technology spillovers: Theory and
evidence // Journal of Development Economics. Vol. 85, p. 176-193.
112
Magombeyi, M. T., & Odhiambo, N. M. (2017). Causal relationship between FDI and
poverty reduction in South Africa. Cogent Economics & Finance, 5(1), 1357901.
https://doi.org/10.1080/23322039.2017.1357901
Mahmood, H. and Chaudary, A. R. (2013). Impact of FDI on Tax Revenue in Pakistan,
Journal of Commerce and Social Sciences, Vol. 7 (1), 59- 69.
Mahmood, H., Lee, J. (2019). Impact of Foreign Direct Investment on Tax Revenue in
Pakistan. Information Communication and Society. 10. 59-69.
Miyagiwa, K., Ohno, Y. (2009). Multinationals, Tax Holydays, and Technology
Transfer. Japanese Economic Review, 60(1), 82-96.
Nguyen H. T. T., Nguyen, M. H., Goenka, A. (2013). How does FDI affect corporate
tax revenue of the host country? (No. 13-03). Centre d'Études des Politiques
Économiques (EPEE), Université d'Evry Val d'Essonne.
Odabas, H. (2016). Foreign direct investment inflows on tax revenues in the transition
economies of European Union, Global Journal on Humanites & Social Sciences.
[Online]. 02, pp 17-22. Available from: http://www.world-education-
center.org/index.php/pntsbs
Okey, M. K. N. (2013). Tax revenue effect of foreign direct investment in West Africa.
African Journal of Economic and Sustainable Development, 2(1), 1-22.
Rudytė, D., Šalkauskienė, L., & Lukšienė, L. (2009). Lietuvos mokesčių sistemos
vertinimas pagal V. Tanzi diagnostinius rodiklius.
Tabasam, F. (2014). Impact of Foreign capital Inflows on Tax Collection: A Case Study
of Pakistan. Issues, 2(2). <http://www.globalcentre.org/uploads/JGSI%202-
2%20Farhana%20T.%20P%2051-60.pdf>
113
ENERGY AND ECONOMIC RELATIONS IN A POSITIVE ECONOMY
Žaneta SIMANAVIČIENĖ
Professor, habil.dr.,
Mykolas Romeris University,
Faculty of Business and Management,
Vilnius, Lithuania
E-mail: [email protected]
Virgilijus DIRMA
Doctoral student
Mykolas Romeris University
Faculty of Business and Management,
Vilnius, Lithuania
E-mail: [email protected]
Abstract: Depending on the specific economic structure of the country, the energy sector
plays a more or less important role in terms of added value, jobs and other indicators, but more
importantly is the role of energy products in the production processes and end use of other industries.
Being one of the branches of the economy, energy sector is also an integral part of the concept of
economy both in terms of national economy and research discipline.
As shown in the following analysis, attempts to separate energy from the economy can only be
very conditional. The concept of economics used in this work in the context of energy relations
should be understood not as one of two completely separate objects, but as a sort of enveloping
element. Here, it is also useful to remember the concept of the remaining economy, which describes
the economy of the country, except energy - all other economic activities, their products, institutional
sectors.
Keywords: economic relation; energy sector, economic growth; energy consumption;
economic models.
114
Introduction
Energy relations with the rest of the economy are perceived intuitively: energy is considered
"the blood of a modern economy", because in virtually all economic activities energy resources are
an element of higher or lower importance. Changes in the supply of energy resources in this article
are understood as displacements in the supply curve of resources provided by the energy sector,
leading to internal and external factors. Taking into account the objectives of the analysis, the
methods of positive and normative economy discussed in this article are used to assess energy
relations with the rest of the economy.
For a long time, changes in oil demand have been seen as a kind of an indicator for all changes
in energy demand because the prices of other fossil fuels and their potential demand relate directly to
oil. This is also due to practical reasons: most energy technologies can use both petroleum products
(mazut) and natural gas. Because of effectiveness parameters of the technologies, the environmental
impact, prices of pollution permits and similar factors, the prices of petroleum products and natural
gas are linked, therefore in some studies are generalized and analyzed as hydrocarbon resources (van
Ruijven and van Vuuren, 2009).
In connection with that, it makes sense to start the analysis of the relationship (such is the
purpose of the study) between energetics and the rest of the economy from the impact oil prices
have on the economy.
Studies of the relationship between energetics and the economy
The relations between energetics and the economy are illustrated in fig. 1, which portrays the
dynamic of the spot price of probably the most representative energy source – oil – (Spot Oil Price:
West Texas Intermediate) and the periods of economic recession in the USA (as defined by (National
Bureau for Economic Research, 2012)).
115
The source of the information in the graph: (Federal Reserve Bank of St. Louis, 2012)
Fig. 1 The dynamic of oil prices and periods of recession in the USA 1946-2012
As seen in fig. 1, in the last few decades, increasing oil prices have been accompanied by a
recession in the USA. On the one hand, Hamilton (2013) observes that ten out of eleven periods of
recession in the USA occurred after an abrupt increase in oil prices. On the other hand, fig. 1 also
shows that during some periods of recession or immediately afterwards oil prices went down.
Although data from other countries are not as abundant, due to globalization a similar situation
(economic development slows down as the prices of energy sources rise) can be seen in other
countries that import oil and other energy sources (opposite effect – decrease of oil prices – is
usually conditioned only by the situation in large economies).
Some causes for peaks of oil prices shown in fig. 1 are explained by the information on the
most prominent global oil supply disruptions of the 20th century, provided in table 1.
Table no. 1 External disruptions of global oil demand
Date The event
Decrease in
global oil
production, %
Change in
US Real
GDP, %
1956, November The Suez Crisis 10,1 -2,5
1973, November Arab-Israeli War 7,8 -3,2
116
1978, November The Iranian
Revolution
8,9 -0,6
1980, October Iran-Iraq War 7,2 -0,5
1990, August Gulf War 8,8 -0,1
Source: (Hamilton, 2008)
From the elasticity and energy share of GDP it is calculated that the direct impact increasing
oil prices have on the economy of the USA could not exceed 0.4 pct., although because during the
typical years of the period presented, the economy of the USA grew 3.4 pct., it is stated that in
practical observations the resulting numbers are completely different – 4 pct. instead of 0.4 pct. The
big difference is explained by such indirect causes as complications resource reallocating between
the sectors of economy that had experienced oil shock. An often mentioned and greatly illustrative
consequence of oil shock is the decrease in demand for fuel-inefficient cars, which leads to poorer
utilization of their factories. Because reallocating the resources of work and, especially, of capital
require additional costs, a part of recourses is not utilized and that increases the impact oil shocks
have on the economy (Hamilton, 2008).
There has been a lot of research trying to empirically evaluate the energetics-economics
relations, especially using the same methods for the time series of different countries (in studies of
this kind the initial application of methodology is probably the most time consuming stage).
Different methods of econometrics are utilized; the studies include different time periods and
countries or their groups. There are four widespread main hypotheses tested in empirical studies of
the relationship between energetics and the economy (Yildirim and Aslan, 2012; Ozturk, 2010;
Salahuddin, M., Gow, J. 2014):
• Conservation hypothesis: the dynamic of economic growth determines the consumption of
energy sources. This hypothesis is validated by uni-directional causality when economic
growth determines the consumption of energy.
• Growth hypothesis: the consumption of energy plays an important direct or indirect
(complementing work and capital) role in the process economic growth. The growth
hypothesis shall be deemed to have been validated if uni-directional causality is established
when energy consumption leads to economic growth (e.g. as energy consumption increases,
the real GDP increases).
117
• Feedback hypothesis: presupposes the variables in question are mutually dependent. The
validation of this hypothesis – the existence of bi-directional causality.
• Neutrality hypothesis: states that there is no causal link between energy consumption and
economic growth. The hypothesis is validated if it is proven that there is no causal link
between the variables of economic growth and energy consumption.
From a practical point of view, the validation of one of these hypotheses is greatly significant
for the choice of policy instruments. In case of the growth hypothesis, austerity policies might
negatively affect economic growth. Meanwhile, if the conservation and neutrality hypotheses are
validated, it is to be expected that saving energy would not have such consequences. Finally, the
validation of the feedback hypothesis shows that saving energy would have a negative effect on
general economic growth, therefore policy instruments should be applied in a less aggregated way,
taking into account different types of energy or the economic sectors of a country (Yildirim and
Aslan, 2012). Explicit validation of one of these hypotheses would also have implications for the
optimal integration of energy technologies: the validation of the conservation hypothesis suggests
that as the development of the energy sector is being modeled, the rest of the economy can be seen as
an exogenous factor, which determines energy consumption. Meanwhile, the validation of the
feedback or growth hypotheses presupposes energy sector’s impact on the rest of the economy.
Causality from electricity consumption to economic growth
Research solely on the relations between the consumption of electric power and economic
growth has shown that the prevalent direction of causality is from the consumption of electricity to
economic growth, therefore, a conclusion is drawn that electric energy is a limiting factor for
economic growth (Ozturk, 2010). However, Payne (2010) conducted an immensely broad review
(broader than Ozturk (2010)) of studies on consumption of this energy source and economic growth
and found that, not taking into account the countries analyzed, time periods and methodologies used,
31.15 pct. of articles validate the neutrality hypothesis, 27.87 pct. – the conservation hypothesis,
22.95 pct. – the growth hypothesis and 18.03 pct. the feedback hypothesis. Such even distribution is
explained by choice of variables, model specifications, different time periods analyzed and
econometric views. Greater granularity of variables (for example, in the environment of the
production model or by including other variables, such as formation of fixed capital, population
growth, etc.) is suggested as one of the possible solutions to the problem of inconsistency in the
118
studies (Ozturk, 2010). The premise that granularity of variables is significant is confirmed in a study
conducted by Gross (2011), which shows that the determination of causation in econometric models
depends on the level of aggregation of variables. In the opinion of the scientist, because of the
Simpson paradox (a situation where statistical dependence is valid for subpopulations, but disappears
on a population level), two-dimensional models that analyze causality on a macro level only are not
suited for the analysis of the relations between energetics and economics, especially in those cases
where coverage of variables differs. Excessive aggregation can interfere with correct evaluation of
the relations when economic growth (recession) is determined by different sectors of economy. For
example, if economic boom is achieved due to the impact of energy-intensive industries and
economic decline is primarily affected by industries that consume energy relatively not as intensely,
it is obtained that during the recent period a country’s economic growth did not affect energy
consumption (Medlock, 2009). Analysis of a whole chain of periods like these derives distorted
results, although on a subpopulation (in this case – a country’s economic sectors) level identification
of clear links between energy consumption and economic growth would be possible.
Results may also be distorted by indicators of the shadow economy, which are highly unstable
(Karanfil, 2008). Moreover, at least a few authors in their works conclude that an econometric
analysis, when the same methods and variables are utilized, and only the time period analyzed is
changed, does not have great potential to expand knowledge of the relations between energetics and
economics (Ozturk, 2010).
It should be noted that empirical assessment of such a structure of the energetics-economics
relationship is also quite complicated due to its cyclic nature and the complexity of the time series,
when econometric methods are simply unable to abstract the effects of the factors under
consideration. Generally, the time series in question spans over more than thirty years and in that
time technologies operating in the energy sector change greatly, alongside the structure of economic
sectors, manufacturing technologies in some sectors of the economy, therefore the nature of the
relations can change. From a methodical point of view, different objects are covered in different
sections of the time series; therefore their econometric analysis is also only partly correct.
This problem is well illustrated by studies that analyze the relations between renewable energy
resources and economic growth, because as practice has shown, twenty years ago precisely
technologies of renewable energy resources could be called “energy technologies of the future”.
Because the rapid development of renewable energy resources has started relatively recently, it
impossible to use time series that span over many years in studies. Bobinaite et al. (2011a) analyzed
Lithuania’s GDP growth and the volume of consumption of renewable energy resources and found
that consumption of renewable energy resources has a short term positive effect on real GDP. Using
119
data from Russia and twelve other countries of Eurasia from 1992-2007, Apergis and Payne (2010c)
identified bi-directional relations between the consumption of energy from renewable resources and
economic growth, thus validating the feedback hypothesis. This hypothesis is also validated by data
from countries of the OECD from the 1985-2005 period (Apergis and Payne, 2010b), data from
countries in Central America from the 1980-2006 period (Apergis and Payne, 2011b). A study by the
same authors, covering 80 countries and the 1990-2007 time period, also confirms bi-directional
relations for both renewable energy sources and fossil fuels (Apergis and Payne, 2011a).
The already discussed series of studies by Apergis and Payne contrasts with a study by
Menegaki (2011), which analyzed 27 countries of the European Union in the 1997-2007 time period
and found only a very weak connection between the consumption of renewable energy resources and
economic growth. The author notes that such validation of the neutrality hypothesis should not be
applied to the future because an assessment of the past does not indicate the future situation,
especially bearing in mind the EU’s commitments to the development of RES, in the background of
which “cost-minimizing consumers and profit-maximizing manufacturers will be governed by an
improved regulatory environment”.
The role of change in energy technologies is also emphasized by Beaudreau (2010), who points
out that, even though historians and growth theorists see the evolution of the steam engine, the
electromagnetic motor and the development of energetics that followed afterwards as a crucial factor
in economic growth, current econometric tests weakly support such a point of view. According to
this scientist, the Granger test (the same could be applied to other evaluation methods of econometric
causality) does not have a solid theoretical basis; therefore the economic interpretation of the results
is problematic. Although many studies show that the consumption of energy has an effect on
economic growth, they do not show by what mechanism that is achieved. As the consensus on the
treatment of energy resources in the production function is lacking, studies on causality should be
seen as speculative and serviceable for studies of exploratory nature, but not for a more in-depth
analysis. As Beaudreau (2010) notes, most non-economists believe that causality works from energy
consumption to GDP growth, while from the point of view of most economists, GDP growth leads to
energy consumption, considering energy resources as elements of intermediate consumption.
Meanwhile, from the perspective of energy consumption, the most important factor is availability of
energy, and not energy consumption as such. Noteworthy is that, in this case, not only the change in
energy technologies is significant, but also the change in economic sectors of a country. It is possible
that different elements of the time series represent completely different levels of energy intensity due
to integrated measures to increase efficiency of energy consumption.
120
While analyzing the relations between oil prices and economics using econometric methods,
their weakening was recorded by the end of the 20th century, which is explained by a decrease of the
input of the energy sector to gross domestic product during some periods, increasing productivity,
also from past experience connected to improvement of policy instruments intended to neutralize
price shocks (Brown and Yücel, 2002), although the recession of the recent period (see Fig. 1) denies
the premise of weakening relations. Also, although most studies show a negative impact increasing
oil prices have on importing countries, there are studies with opposite results. Results, interesting
from a theoretical and practical point of view alike, from a study conducted in China showed that
both inflation and GDP growth are positively correlated to oil price (if oil price increased by 100
pct., GDP would grow by 9 pct. and inflation would increase by 2.08 pct., although China is an
importing country (Du, Yanan and Wei, 2010)).
An aspect related to the use of economic models, especially concerning the “net” econometric
models – the so called Lucas critique. Its essence is that if an econometric model’s structure consists
of optimal decision rules of economic agents and if the optimal decisions systematically change due
to the decision makers’ impact, it means that changes in policy have to systematically change the
structure of econometric models (Lucas, 1976). This way any policy changes change the parameters
of econometric models. The Lucas critique can also be applied in the case of change in technologies:
if the parameters of an econometric model were assessed with one kind of technology, they might
not be valid if the technology park changes more significantly. Lithuania’s example illustrates this
very clearly: the closure of the Ignalina nuclear power plant not only meant change in the structure of
electricity generation, but the loss of a power plant that generated over 70 pct. of consumed
electricity in the country. Changes of this magnitude in the structure of the energetic sector greatly
limit the application of econometric methods due to the lack of relevant time series.
The arguments that are presented to reject the Lucas critique can also be used as arguments
against modeling as a way of knowing reality.
121
Conclusion
Studies have shown, that any methodologic approach is good when it is chosen based on what
questions are desired to be (or can be) answered. In conclusion, it can be said that econometric
studies of energy consumption and economic growth allow the identification of fundamental
tendencies (therefore can be used for forecasting in a relatively stable environment) and regularities
and also produce valuable material for economic interpretation. Improving such studies, more
attention should be payed to economic interpretation of the results, which is also related to the need
for disaggregation of the time series. Econometric models can summarize past data well, but their
use for a deeper ex-ante analysis is limited by the ever-changing nature of the relations under
consideration and, in the case of most countries, limited statistical data. It should be noted that a
complex approach to energetics and a detailed analysis of the energetics-economics relations on a
technology and economic activity type level might not only contribute to optimal integration of
energy technology, but also (when applied retrospectively) might be useful in studies of the positive
economy.
122
References
Akpan, G.E., Akpan, U.F. (2011). Electricity consumption, carbon emissions and economic
growth in Nigeria. Int. J. Energy Econ. Policy 2(4), 292–306.
Apergis, N., & Payne, J. E. (2010). Natural gas consumption and economic growth: A panel
investigation of 67 countries. Applied Energy, 87(8).
Apergis, N., & Payne, J. E. (2011). The renewable energy consumption–growth nexus in Central
America. Applied Energy, 88(1), 343-347.
Bataille, C. G. F. (2005). Design and Application of a Technologically Explicit Hybrid Energy-
Economy Policy Model with Micro and Macro Economic Dynamics.
Beaudreau, B. C. (2010). On the methodology of energy-GDP Granger causality tests. Energy,
35(9), 3535-3539.
Bobinaite, V., Juozapaviciene, A., & Konstantinaviciute, I. (2011a). Assessment of Causality
Relationship between Renewable Energy Consumption and Economic Growth in Lithuania.
Engineering Economics, 22(5).
Bobinaite, V., Juozapaviciene, A., & Konstantinaviciute, I. (2011b). Regression Analysis of
Gross Domestic Product and its Factors in Lithuania. Ekonomika ir vadyba, 16.
Böhringer, C., & Rutherford, T. F. (2008). Combining bottom-up and top-down. Energy
Economics, 30(2), 574-596.
Brown, S. P. A. ir Yücel, M. K. (2002). Energy prices and aggregate economic activity: an
interpretative survey. The Quarterly Review of Economics and Finance, 42(2), 193-208.
Du, L., Yanan, H. ir Wei, C. (2010). The relationship between oil price shocks and China’s
macro-economy: An empirical analysis. Energy Policy, 38(8), 4142-4151.
Gross, C. (2011). Explaining the (non-) causality between energy and economic growth in the
U.S.—A multivariate sectoral analysis. Energy Economics.
Huang, B.-N., Hwang, M. J., & Yang, C. W. (2008). Causal relationship between energy
consumption and GDP growth revisited: A dynamic panel data approach.
Yildirim, E., & Aslan, A. (2012). Energy consumption and economic growth nexus for 17 highly
developed OECD countries: Further evidence based on bootstrap-corrected causality tests.
Energy Policy, 51, 985-993.
Jaccard, M. (2009). Combining top down and bottom up in energy economy models. In J. Evans
& L.
123
Huang, B.-N., Hwang, M. J. ir Yang, C. W. (2008). Causal relationship between energy
consumption and GDP growth revisited: A dynamic panel data approach. Ecological Economics,
67(1), 41-54.
Karanfil, F. (2008). Energy consumption and economic growth revisited: Does the size of
unrecorded economy matter? Energy Policy, 36(8), 3029-3035.
Lucas, R. J. (1976). Econometric policy evaluation: A critique. Carnegie-Rochester Conference
Series on Public Policy, 1(1).
Medlock, K. B. (2009). Energy demand theory. In J. Evans ir L. C. Hunt (Eds.), International
Handbook on the Economics of Energy. Cheltenham: Edward Elgar Publishing Limited.
Menegaki, A.N. (2011): Growth and renewable energy in Europe: a random effect model with
evidence for neutrality hypothesis. Energy Econ. 33, 257–263
Ozturk, I. (2010). A literature survey on energy–growth nexus. Energy Policy, 38(1), 340-349.
Payne, J. E. (2010). Survey of the international evidence on the causal relationship between
energy consumption and growth. Journal of Economic Studies, 37(1), 53-95.
Reboredo, J.C. (2015). Renewable energy contribution to the energy supply: is there convergence
across countries? Renew. Sustain. Energy Rev. V 45, 290–295
20. Van Ruijven, B., van Vuuren, D. P. (2009). Oil and natural gas prices and greenhouse gas
emission mitigation. Energy Policy, 37(11), pp. 4797-4808
Salahuddin, M., Gow, J. (2014). Economic growth, energy consumption and CO2 emissions in
Gulf Cooperation Council countries. Energy 73, 44–58
Sebri, M. (2015). Use renewables to be cleaner: meta-analysis of the renewable energy
consumption-economic growth nexus. Renew. Sustain. Energy Rev. 42, 657–665.
Xu, H. X. (2016). Linear and nonlinear causality between renewable energy consumption and
economic growth in the USA. J. Econ. Bus. 34(2), 309–332.
124
124
TRADITIONAL AND MACHINE LEARNING-BASED METHODS FOR FINANCIAL
INSTRUMENT PRICE FORECASTING: A THEORETICAL APPROACH
Justinas Lape
“UVS Group”, Savanoriu ave.
123A, LT-03150, Vilnius,
Lithuania,
Inga Zilinskiene
Mykolas Romeris university,
Ateities st. 20, LT-08303
Vilnius, Lithuania,
Saulius Preidys
Vilnius University, Saulėtekio
ave. 9, I house, 10222 Vilnius,
Lithuania,
Abstract. Financial markets are one of the main components of the economy, and their growth and
development is a crucial and significant factor in the world. Meanwhile, artificial intelligence is an
exponentially developing field. The use of artificial intelligence in financial markets is a new and intensely
developing phenomenon, requiring extensive research. The aim of this paper is to present a methodology
of machine learning-based method effective application in financial instrument price forecasting in
comparison with the traditional method, namely ARIMA. Consequently, the scientific literature on
financial markets, traditional and machine learning-based methods were analyzed. Finally, a theoretical
model for stock prices forecasting is presented. The main results of the analysis show the most extensively
techniques applied in the stock markets and cash markets are methods of time series analysis, econometrics
and machine learning. After analysis of methods of machine learning, it can be found most popular
supervised learning algorithms are linear regression, decision trees/regression tree, random forest
classification/ regression, and support vector machines. As a result of the research, a theoretical model for
financial instrument price forecasting is presented and discussed. Based on the theoretical approach
proposed several experiments will be performed using ARIMA and Support Vector Regression (SVR)
methods.
Keywords: financial instruments, forecasting, ARIMA, artificial intelligence, machine learning, SVR.
125
125
Introduction.
Financial markets are one of the main components of the economy, and their growth and development is
a crucial and significant factor in the world. Meanwhile, artificial intelligence is an exponentially
developing field. The use of artificial intelligence in financial markets is a new and intensely developing
phenomenon because of “<… both supply factors, such as technological advances and the availability of
financial sector data and infrastructure, and by demand factors, such as profitability needs, competition
with other firms, and the demands of financial regulation.” (Financial Stability Board, 2017).
Financial market is a broad term describing a marketplace where buyers and sellers are involved
in exchange of such assets as shares, bonds, derivatives, currencies and other tradable financial
instruments. Investors have access to many financial markets and exchange offices where different types
of instruments can be traded. There are different sizes of financial markets across the globe, one of which
has only a few participants, while other daily turnover amounts to trillions of euros. Financial markets
differ not only in their size but in their functionalities and in the possibility of trading different risk
financial instruments. Also, the financial market can be called a complex system, because it is influenced
by many of the economic, political and psychological factors involved. Knowing when and how to invest
in financial instruments is a complex decision to be taken by the investor. Trading financial instruments
requires knowledge, deep market analysis and extensive experience.
Both investors and scientists are trying to predict the stock markets. This is an interesting and
challenging area of research for the academic world, while the investor's main incentive is profit.
Nevertheless, not everyone manages to cope with this task successfully. The successful pricing and trading
of financial instruments is aggravated by factors such as the constant price volatility of financial
instruments due to various economic, political factors and sentiments of participants operating in the
markets. The price volatility of financial instruments means that the price may fall below the cost of the
purchased instrument. A trivial solution that would lead to a successful investment would be the sale of a
financial instrument before it was struck by its value or, ideally, by the price rise. For this reason, a variety
of approaches are designed to create the most accurate model of the price forecasting of the financial
instrument.
There is a wide range of studies found in the literature, which apply traditional forecasting
techniques. In this regard, it can be noted that still a large part of the focus is given by applying traditional
forecasting methods. On the other hand, research on the application of new types of methods is increasing
126
126
significantly in scientific databases. A new trend is the development of artificial intelligence-based
approaches, such as the application of machine learning algorithms, in developing predictive price models
for financial instruments.
The purpose of the scholarly analysis – to present a methodology of machine learning-based
method effective application in financial instrument price forecasting in comparison of the traditional
method, namely ARIMA.
Research object – traditional and machine learning-based methods for financial instrument price
forecasting.
Applied methods – scientific literature analysis, mathematical modeling.
The remainder of this paper is organized as follows: Section I reviews the concepts of financial
markets, financial instruments; methods for financial instrument price forecasting; Section II describes the
theoretical approach for stock prices forecasting; Section III presents conclusions.
Background
1.1. The Concepts of Financial Markets and Financial Instruments
The financial market is defined as the market in which the trading of financial instruments takes
place (Fabozzi, Modigliani and Jones, 2014). Other researchers define the financial markets as a place
where various types of financial instruments can be sold and/or exchanged by different entities based on
their price, which is influenced by the supply and demand prevailing on the market for change. (Financial
Stability Board, 2017)
Financial markets use different types of financial instruments. All financial instruments belong to
a particular type of financial market (Kidwell, Blackwell and Whidbee (2011), Levinson (2014), Fabozzi,
Modigiliani and Jones (2014), etc.). Different classifications of financial markets can be found as well:
e.g., Kidwell, Blackwell and Whidbee (2011) differentiate financial markets to primary and secondary,
organized and unorganized (over the counter), institutional and domestic markets. In the secondary
financial market, financial instruments such as shares, bonds, futures and forwards, options, currencies,
loan securities and other, newly derived instruments are traded. (Parameswaran, 2011) This article
describes the methodology intended to forecast only the prices of financial instruments for which various
mathematics-based analyses and forecasts are used, i.e. shares, options, futures, and currency pairs. In the
127
127
stock and currency markets usually time series analysis, econometrics is used. Recently testing of the
efficiency and benefits of machine learning algorithms has been researched (Dymova, Sevastianiov and
Bartosiewicz, 2016).
It is important to underline that the simultaneous application of different approaches to the analysis
can only enrich and improve the accuracy of the forecast – the latter factor can be identified as one of the
limitations of this work since the created methodology of the forecast will not incorporate hybrid methods.
1.2. Methods for financial instrument price forecasting
Scientists Akansu and Torun (2015) distinguish three main trading strategies of financial
instruments – fundamental, technical and quantitative analysis methods. Nazario, Silva, the Obobir and
Kimura (2015) argue that the fundamental analysis relies on various economic factors to determine the
real price of the securities, while technical analysis relies on the historical cost of the security and volume
data. The fundamental analysis assesses the finances of businesses – cash flow, income, business
performance, credit risk and other factors that allow to draw conclusions about the company's value. In
contrast to the fundamental analysis, the technical analysis focuses on the changes in the price of the
financial instrument. The latter method uses a wide range of visualization techniques (trends, resistance
and support lines, etc.) to decide when to purchase and when to sell the financial instrument. Meanwhile,
the most recent branch is called quantitative analysis is about studying and developing complex
mathematical models and using statistical methods of analysis.
Both the technical and the quantitative analysis of financial instruments use mathematical and
statistical methods to help investors detect the most optimal moment for opening or closing a position. In
recent times, artificial intelligence methods as machine learning, deep learning, neural networks are
applied more often with quantitative analysis.
The quantitative trading is any form of trading that uses sophisticated algorithms (programmable
systems) to automate all or multiple trading cycles. It also includes encoding of the rules that the computer
will have to perform and carry out backward testing or forward testing (Treleaven, Galas and Lalchand,
2013).
Quantitative trading can be divided into two parts: creating mathematical models for analysis and
forecasting and developing an automated trading system by programming. Only the development of
128
128
specifically applied math-based methods is retained in this work. The quantitative analysis relies to a large
extent on statistics and the application of math-based methods.
Due to the fact that the financial data is a time-series data, a quantitative analysis can be called a
time series analysis. Time series analysis is based on data analysis to find the optimal model that fits the
given data in the best possible way. The primary purpose of the time series analysis is to find a model that
can be successfully extrapolated to future data. Time series analysis is widely used for non-stationary data,
namely this type of data and is generated in financial markets.
Time series forecasting methods are divided into two groups: based on statistical concepts and
based on computer-intelligence techniques such as machine learning, neural networks, or genetic
algorithms. The most popular statistical predicting methods for the time series are exponential alignment
methods, regression methods, autoregressive integrated moving average methods (ARIMA), threshold
methods, generalized autoregressive conditional heteroskedasticity or autoregressive conditional
heteroskedasticity methods (GARCH/ARCH).
In the light of the studies carried out and on the basis of the reviews found in scientific sources, it
can be concluded that, in most cases, research papers or studies on the analysis of data from financial
instruments are seen in the context of machine learning algorithms (Dingli and Fournier, 2017): logistical
regression; Random forests (RF); Support vector machines (SVM); K-Nearest Neighbor (kNN).
The traditional statistical ARIMA method generates the relevant predicted price values for the
financial instrument. For this reason only the regression methods of supervised learning are suitable for
verifying the effectiveness of the two methods of forecasting, since the classification methods distribute
the predicted data into categories, which means that the latter methods are more appropriate to identify
the tendency – the price trend of a financial instrument is at rising or at fall. Literature review reveals
machine learning techniques commonly used to predict share prices, mainly: Neural networks, support
vector machines are among the most commonly used methods. Both neural networks and support vector
machines are standard machine learning techniques that can be used to predict time series data due to their
specifics (Meesad and Rasel, 2013). Support vector machines is a machine learning algorithm falling into
the category of supervised learning. The algorithm can be used to execute the price forecast of financial
instruments. On the basis of the studies found in the scientific databases, it can be argued that the SVM
algorithm is used in most cases, or the SVM algorithm is combined with other algorithms. Also, the SVM
algorithm is applied not only in the financial markets but also in other areas due to its versatility. The
129
129
popularity and applicability can be justified by the high aggregate performance of the method and by a
mathematically well-prepared method of algorithm training (Liu and Duan, 2018).
In the context of the financial market, the application of the SVM algorithm is promising for two
main reasons:
1. The algorithm does not apply any categorical assumptions to the data you are working with;
2. Characterized by the ability to minimize data over-fitting.
In conclusion, the algorithm is extremely sophistical, capable of working with non-linear data,
while it also can be used as a regression method, with the help of which real values are predicted, so the
methodology presented will be based on the application of SVR algorithm for financial instrument price
forecasting.
II. A methodology to stock prices forecasting
In order to be able to determine if machine learning methods are effective in financial instrument
price forecasting a methodology of research is needed. The two methods are selected on the basis of
scientific analysis. First one is traditional statistical method ARIMA, the second one is a machine learning-
based, support vector regression (SVR) algorithm. Each method has its own specific course of application,
which is presented below.
2.1 The Methodology
The subsequent methodology (Fig. 1) will be used in the future to determine whether the results
obtained by the SVR method are more accurate than the results using the ARIMA method.
130
130
Fig. 1. A methodology for financial instrument price forecasting
Data selection.
Due to the availability of data and the specificity of the financial instrument, shares will be selected as a
tool and the data provided by them will be used to construct and study the models. The same datasets will
be used to compare ARIMA and SVR models.
Overview of selected data.
When forecasting models are created, it is necessary to divide the dataset into two groups – one of the
groups being defined as a training set, another as a testing set (Nayak, Pai and Pai, 2016). The training
data set is designed to train models and the testing data set is designed to evaluate the predictive
capabilities of a trained model. In the future experiments, predictive models are verified by the principle
of the back-testing. The non-return test will be carried out using historical price data. The purpose of the
back-testing is to determine the effectiveness of the forecast with an assumption that if the prediction has
been successful in the past, it is likely to continue to perform well in the future (Bailey et al., 2014).
Testing methods.
The RMSE (Root Mean Square Error) indicator will be used to measure the accuracy of the forecasts. The
RMSE indicator is the standard deviation of residues. RMSE is used in situations where there is a
131
131
comparison of forecasts of several different methods for the same datasets based on RMSE metering, the
lower the error, the better the forecasting performance. In this case, the residue is a dimension that shows
how far is the real value of the data from the regression line (Chai and Draxler, 2014).
Application of ARIMA.
ARIMA method shall be used for both the training and testing data sets to achieve the most accurate
results. The forecast is for the fixed period, using a one-step approach.
Based on the Box-Jenkins methodology, the ARIMA process covers a total of three essential steps
– identification, evaluation, diagnosis. The main goal is to define ARIMA (p, d, q) parameters because
only the successful selection of parameters can lead to the reliability of the prediction of the method.
Identification involves checking time series stationarity. During this phase, Dickey-Fuller test or/and ACF
and PACF analysis are usually employed to identify stationarity. Estimation phase involves estimation of
the parameters – information criteria such as AIC, AICc and BIC are used. Diagnosis phase involves
determining whether the model is adequate – Ljung-Box test or ACF/PACF analysis are performed to
check if there are any remaining correlation between residuals. If a correlation between the residual values
is recorded, this means that the model can still be adjusted by the GARCH/ARCH methodology.
Application of SVR.
SVR method is categorized into several types and parameters. SVR method development includes three
major steps: method type has to be selected, kernel function has to be chosen and parameters has to be set.
SVR model predictions are heavily influenced not only by the type and kernel function but also by the
choice of parameters. R software e1071 package is limited to the following types of SVR: C-classification,
NU-classification, ONE-classification and EPS-regression. Model type is usually selected considering the
purpose of the modelling. EPS-regression type is the most relevant because this type is for forecasting real
values. In addition radiant kernel function is found to be the most accurate for time series data. The
parameters of "gamma", "epsilon", "cost" usually are determined in two ways:
1. The first method is error-testing – Multiple variants of all parameter values are tested and
determined which combination of parameters provides the best results;
2. The second method is using tune.svm function from the e1071 package to check the suitability
of the selected parameters, which determines the most efficient combination of parameters in the 10-level
cross-validation method. Cross-validation is a statistical method for determining the performance of a
machine learning algorithm, in which the data set is randomly selected for group data on which the model
is and is trained and adaptable.
132
132
The parameters selected in both ways will be checked and the most efficient model will be selected.
Comparison of forecasts using both methods.
In this part, the results obtained will be compared and conclusions drawn. After the forecast parameters
have been completed, the RMSE indicators will be calculated and based on them a comparison of the
effectiveness of the methods will be concluded.
Implementation environment
For the analysis, open source data analysis and programming software R will be used. Forecast package
will be used for ARIMA modelling, e1071 package will be used for SVR modelling.
2.2. Assumptions and limitations
1. The Efficient Market Hypothesis (EMH). According to the EMH, stocks always trade at their fair
value on stock exchanges, making it impossible for investors to either purchase undervalued stocks
or sell stocks for inflated prices.
2. Evaluation of the performance of the forecast is done through historical data. The model is not
tested in real-time. It is assumed that if the forecast worked well on historical data, it will work
well in real-time.
3. The study aims to predict the prices of financial instruments rather than develop a trading strategy
and calculate profitability and/or returns.
4. Software specifics – it is likely that by developing models and forecasting using other software
platforms such as Python, MATLAB, etc., there may be an error in the results.
5. The work-study is limited to the application of a traditional statistical method and a single machine
learning approach. The application of multiple methods may affect the final results.
6. Forecasting methods are created solely on the basis of the historical cost of shares. The scope does
not include additional data sources that are likely to increase the accuracy of the forecast.
7. Both models are adapted for a specified period. In the next period (longer/shorter), the results of
models are likely to vary as well.
8. ARIMA and AVR methods can be parameterized differently. For this reason, the final forecast
results may vary.
Conclusions
133
133
The forecasting of stock prices is highly researched, but most of the research is concentrated on
the application of traditional methodologies, such as Box-Jenkins or technical analysis. Nevertheless, the
application of artificial intelligence techniques are already being explored on the market. To analyze
whether the application of machine learning-based approaches in the forecast of share prices can actually
have tangible benefits, a literature analysis is done. A methodology based on the traditional ARIMA
method, and the support vector regression method is proposed as well. The main findings are as follows:
1. Following the theoretical analysis of financial markets, it was found that the types of analysis
in the financial markets are: fundamental analysis, technical analysis and quantitative analysis. The
quantitative analysis consists of two parts: the development of mathematics-based methods and the
automation of trade. Mathematics-based methods are mainly used for the analysis and forecasting of
shares, options and futures, as well as currency pairs. Time-Series analysis, econometrics, and machine
learning techniques have been found to be most intensely applied in the stock and currency markets.
2. As a result of the literature analysis, data generated by the financial markets were found to be
time-series data. The most commonly used methods of analysis of the time series are the methods of
ARIMA class. Machine learning is one of the main subgroups of artificial intelligence, the main purpose
of which is to find the templates in the data, summarize them, learn without programming. Given that the
financial timeline data is dynamic, non-linear and highly volatile, machine learning is the appropriate tool
for analyzing the latter type of data. Machine learning algorithms are classified in the classes of supervised
learning, unsupervised learning, enhanced learning and deep learning. However, the most commonly used
algorithms for the price forecast of financial instruments are those falling under the class of supervised
learning. Not limited to this, supervised learning algorithms are also categorized into classification and
regression algorithms with different and specific requirements at the beginning and different results at the
end of the application.
3. Based on the scientific analysis, a theoretical approach, how to determine the effectiveness of
machine learning method for financial instruments prices forecasting were presented. Several experiments
will be performed following the methodology proposed using ARIMA and SVR methods.
References
Akansu, A. N., & Torun, M. U. (2015). A Primer for Financial Engineering: Financial Signal Processing
and Electronic Trading. Cambridge, MA: Academic Press.
134
134
Bailey, D. H., Ger, S., Lopez de Prado, M., Sim, A., & Wu, K. (2014). Statistical Overfitting and Backtest
Performance. SSRN Electronic Journal. doi:10.2139/ssrn.2507040
Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? –
Arguments against avoiding RMSE in the literature. Online: https://www.geosci-model-dev-
discuss.net/7/C473/2014/gmdd-7-C473-2014-supplement.pdf
Dingli, A., & Fournier, K. S. (2017). Financial Time Series Forecasting – A Deep Learning Approach.
International Journal of Machine Learning and Computing, 7(5), 118-122.
doi:10.18178/ijmlc.2017.7.5.632
Dymova, L., Sevastianov, P., & Bartosiewicz, P. (2010). A new approach to the rule-base evidential
reasoning: Stock trading expert system application. Expert Systems with Applications, 37(8), 5564-5576.
doi:10.1016/j.eswa.2010.02.056
Fabozzi, J. F., Modigliani, F. P., & Jones, F. J. (2014). Foundations of Financial Markets and Institutions.
New York, NY: Pearson Higher Ed.
Farias Nazário, R. T., E Silva, J. L., Sobreiro, V. A., & Kimura, H. (2017). A literature review of technical
analysis on stock markets. The Quarterly Review of Economics and Finance, 66, 115-126.
doi:10.1016/j.qref.2017.01.014
Financial Stability Board. (2017). Artificial Intelligence and machine learning in financial services.
Market developments and financial stability implications. http://www.fsb.org/wp-
content/uploads/P011117.pdf
Kidwell, D. S., Blackwell, D. W., & Whidbee, D. A. (2011). Financial Institutions, Markets, and Money,
11th Edition. Hoboken, NJ: Wiley Global Education
Levinson, M. (2014). The Economist Guide To Financial Markets 6th Edition. London, England: Profile
Books.
Liu, M., & Duan, C. (2018). A Review of Using Support Vector Machine Theory to Do Stock Forecasting.
Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering
(NCCE 2018). doi:10.2991/ncce-18.2018.184
Meesad, P., & Rasel, R. I. (2013). Predicting stock market price using support vector regression. 2013
International Conference on Informatics, Electronics and Vision (ICIEV).
doi:10.1109/iciev.2013.6572570
Nayak, A., Pai, M. M., & Pai, R. M. (2016). Prediction Models for Indian Stock Market. Procedia
Computer Science, 89, 441-449. doi:10.1016/j.procs.2016.06.096
Parameswaran, S. (2011). Fundamentals of Financial Instruments: An Introduction to Stocks, Bonds,
Foreign Exchange, and Derivatives. Hoboken, NJ: John Wiley & Sons.
135
135
Treleaven, P., Galas, M., & Lalchand, V. (2013). Algorithmic trading review. Communications of the
ACM, 56(11), 76-85. doi:10.1145/2500117
136
THE PROBLEM OF INEQUALITY IN INCOME IN GEORGIA AND THE ROLE OF
PROGRESSIVE TAXATION IN ITS ELIMINATION
George ABUSELIDZE
Batumi Shota Rustaveli State
University, Georgia
Jilda ABASHIDZE
Batumi Shota Rustaveli State
University, Georgia
Anna SLOBODIANYK
National University of Life
and Environmental Sciences
of Ukraine
Abstract: One of the most important factors for the development of the country is the
functioning of the tax system; however, with the development of the functioning of the tax
system is important, which ensures the economic well-being of the country to improve the
welfare of the population. Along with taxes it is important to define the level of inequality
between different layers of population. In this regard Georgia, as well as other developing
countries, has an unfavorable situation and needs to take appropriate measures.
Reason of research: In order to eliminate the inequality of income, it is necessary to carry out
such measures, which will improve the tax system of Georgia and will solve the problem of
equality. In order to eliminate the inequality of income, we need to consider the possibilities of
introduction of progressive taxes. It is noteworthy that the progressive taxation method of
revenue contributes to the implementation of the social function of tax and equity distribution in
comparison with the country's wealth.
Research object: One of the main sources of tax revenue is income tax and taxation of taxes in a
progressive way. Also the right to introduce a taxable minimum.
Research Methodology: Conducting empirical analysis based on the data of the National
Statistics Office of Georgia and Ministry of Finance of Georgia, Analyze studies conducted in
this direction. The analysis of documents in fiscal policy was carried out during the research
process. Surveys were conducted by means of qualitative and quantitative methods.
Keywords: Tax policy, income tax, tax burden, budget, well-being
137
Introduction
The level of community life is determined by different criteria and different indicators. The
country's gross income may be high, but most of it is distributed to a small number of people. In
this regard, a significant study was prepared by Italian statistician Corrado Jinn in 1912, it shows
how well the income is distributed. The highest rate of Gini is recorded in Sweden - 0.25, the
most equitable distribution is around the world, the highest is in Seychelles -0.6581. This
indicator in Georgia is 0.421 or 42.1%.
Duncan and Sabirianova (2016) find that progressivity reduces inequality in observed
income, but has a significantly smaller impact on actual inequality approximated by consumption
based Gini indices.
As we have seen, The Gin Index for Georgia is 0.42, which is quite high in equality and it
is necessary to alleviate the situation with certain measures. Due to this, that amounts raised by
income tax there is a large part of tax revenue. Our attention is concentrated directly income tax
and Inequality associated with it. Labor remuneration is clearly different between different layers
and therefore, the volume of taxes should be paid attention.
Literature Review
Tax as an important element of the system of economic relations is so complicated without
alternatives in fiscal policy that it always becomes an actual issue in the public discussions
(Abuselidze, 2015, p. 602). The study of the optimization of taxable income taxes in the modern
stage is not only not only science but also an important concern for practitioners. First of all, the
severity of this tax is based on the income tax analysis and impacts the welfare of society.
Secondly, it is one of the major tax revenues for the budget, which is predictably predictable,
which is more or less accurate and dependent on the functioning of the state to fund the
necessary expenditures. It is universally recognized that tax burden affects not only budget
revenues, but investments, production factors, price standards etc. Finally, all above affects
socio-economic position of country (Abuselidze, 2013, p.1451).
1 Human Development Reports, 2014.
138
Adam Smith regarded taxes as the index of freedom for tax payers, but for Montesquieu nothing
and no one needs such a wisdom and mind as defining tax size and that part, which stays with a
tax payer.
Smith wrote about the necessity of existing optimal tax policy in the country: “The owner of the
capital is actually the citizen of the whole world and doesn’t represent the property of one
country. He will immediately leave the country with undesirable tax condition and invest his
capital in the country where he will be able to run his business and property without pressure”
(Smith, 2011).
Adam Smith and David Ricardo (Ricardo, 1937) pointed to the factors determining real
addressees of tax burden in their works. Adam Smith connected size of wages to elasticity of
labour supply, but David Ricardo developed the ideas related to reaction of demand and supply
regarding change of price for different goods, i.e. he considered elasticity of demand and supply
a reference point. These considerations underlie the modern views which explain the problems of
shifting of tax burden (Abuselidze, 2012, p. 497).
Unfortunately, most of the authors do not pay attention to the question of different tax regimes
and rates influence on macroeconomic balance and employment level. However, we disagree
with the Laffer-Keynes theory postulates, considering that introduction of optimal mean tax rates
only is not able to increase employment level, initiate establishment of a new balance and
mobilization of maximal tax revenue in the budget (Abuselidze, 2011, p. 163).
1. The role of tax system To improve the socio-economic situation of the country
1.1 Taxes and their importance in the process of revenue equalization
Taxes are mostly related to property ownership and with income. Therefore the size of the
revenue is significantly determined tax base size. The state has a significant leverage in the
process of revenue equal to taxes, since earnings are taxable. The state can establish a tax
system, that the taxpayer should be differentiated and promote low income and Equity of high
income taxpayer by using different methods of tax.
In this regard, the state faces a significant problem. It is quite difficult to establish such a tax
system, which will help as a function of tax functions, As well as its social aspects of justice.
Hus, Legislation is a task based on the economically grounded proposal Taxation of a tax
139
regime, which will not slow down the payer's economic activity and at the same time the
appropriate level of tax payments in the budget.
The margin of tax deductions is a taxable condition. Which is optimal as a taxpayer, as well as
for the state treasury. Move the conditional point on either side, creates contradictory
circumstances, which is revealed chronic budget deficit or in political conflicts, in the
disobedience of taxpayers to tax authorities, massive tax avoidance and Migration of the
population.
1.2 Taxation methods and ability to use them
Optimal merger of direct and indirect taxes are required to create a flexible, fair and
comprehensive tax system, but in our case direct taxes and, in particular, income tax effective,
fair and social support mechanisms can be achieved Progressive tax taxation scaling and action.
Since social support can be realized by taxation in a variety of ways and implies a progressive
rate of income tax (As well as other tools: undeclared minimum, deduction System, benefits and
other means of use) by establishing equalizing the income of different social groups of the
population, reduction of social differentiation, which is closely linked to the principle of equality
and fairness.
The use of any taxation depends on the economic condition of the country, main directions of the
tax system, functions and goals. Separate any of them or give preference not right.
If you do not take into account the situation in the country. The state must define and choose the
optimal option itself, because taxes play an important role in the socio-economic development of
the country.
It is possible to use a regressive tax method to generate positive results in one country and
significantly facilitate the improvement of the country's tax system and improve social
conditions, but the use of this method in other countries is a completely different and negative
result. Therefore, when choosing a method, the state of the economy should be considered,
peculiarities of tax system and problems related to the equality of distribution of the income of
the population, because the tax burden, which should be rationally distributed to the population
and it's also convenient to remove.
140
It should be noted there is no clear causal connection on the one hand, inequality and between
progressive taxes, and on the other hand poverty and between progressive taxes. However, we
can say that the country where there is a progressive tax on poverty and inequality is low.
However, low and high economic growth may be in countries where there is a progressive tax
and in countries where there is a so-called fixed income tax.
2. Implementation of progressive tax methods in Georgia
2.1 The peculiarities of the tax system of Georgia
After Georgia became an independent state, maximally trying to improve all economic
indicators, however, none of the Georgian government has been able to do so to implement
social-economic policy, which ensured the inclusive development of the country, to reduce
unemployment, the problem of inequality in income and poverty.
Today, the income tax is uniform and the proportional method of taxation is used. It’s rate is
20%2. The proportional rate indicates taxation of the social direction. In this case, the tax
pressure is more likely to cost the population less revenue. As a result the social differentiation
of the society is strengthened.
Since the state should pay tax and money to pay higher taxes, and low-income (especially
subsistence minimum) should help, do not overload it. Therefore, it is advisable and correct to
some extent that the system of progressive rates will be restored and introduced again, but rates
must necessarily be analyzed and more desirable is based on scientific research.
2.2. Progressive tax models.
Most OECD countries employ different types of progressive income tax structures, in which tax
rates grow with increasing income (Olsen, J., Kogler, C., Stark, J., & Kirchler, E. 2017). Most of
the overseas countries: Belgium, Sweden, Finland, Denmark, Spain, the United Kingdom, etc.3
Has a progressive income tax, but we will discuss, Spanish and Danish tax models. It should be
noted that in Denmark as well as in Spain progressive tax is very different.
2 Tax Code of Georgia Chapter XI "Income Tax" 2010 year. 3 Forbes. http://forbes.ge/news/3234/romel-qveynebSia-dabali-gadasaxadebi
141
Spain's progressive tax rate exceeds the average European rate. In Denmark, compared to Spain,
the progressive income tax is higher4.
Table 1: Progressive income taxes in Denmark
From(euros) Up to (euros) Tax Rates
€ 0 € 12.450 20%
€ 12.450 € 20.200 25%
€ 20.200 € 35.200 31%
€ 35.200 € 60.200 39%
€ 60.000 $ above € 47%
Source: european.ge
Table 2: Progressive income taxes in Spain
Tax Tax Base ( in DKK)
0% Up to 41 000
37.46% 41 001 – 279 800
43.48% 279 801 – 335 800
59% 335 8001 and over
Source: european.ge
As for the Gini coefficient, inequality in Denmark is 0.37. However, since taxes and
transfer factors have been taken into account, this figure lasts up to 0.24. Therefore, taking into
consideration these factors, it turns out that inequality decreases by 35%5. The same indicator in
Spain is 0.34 in 20166. Even though in both states, progressive taxes are high, such well-being
indicators such as: Unemployment, inequality, poverty is still low. We will discuss some of the
following factors:
4 Eurostat. http://appsso.eurostat.ec.europa.eu/nui/show.do 5 Social state and social democracy. 2014 year. 6 Eurostat. http://appsso.eurostat.ec.europa.eu/nui/show.do
142
First of all, it should be said that there is a high correlation between the progressive taxes
and the degree of redistribution, however, the direct cause is not confirmed.7 So does not
necessarily result in high quality distribution. Low level of redistribution indicates the existence
of problems in certain areas, for example, economics and education.
Different studies show, that Spain has significant problems especially in the third level of
education. Despite the fact that in Spain this indicator has increased and exceeds EU average
indicators, In addition, the school's early abandonment rate and the number of people with poor
education is quite high.
In Spain, there are two major factors influencing inequality and poverty. First, this is the
catastrophic growth of unemployment since 2008 and the second is the size of the informal
sector. Part of the researchers believe, that high level of unemployment creates significant
problems in reducing re-distribution and inequality8.
Figure 1: Unemployment UK, US and EU.
Source: Forbses.ge
7 What nation has the most progressive tax system? http://gregmankiw.blogspot.com/2011/03/what-nation-has-mostprogressive-tax.html 8 WHY IS INCOME INEQUALITY SO HIGH IN SPAIN? Gradín., C, 2015, http://www.ecineq.org/ecineq_lux15/FILESx2015/CR2/p124.pdf
143
Denmark is undoubtedly an example of the development of the state of its development or
prosperity. In Denmark, unemployment, poverty, inequality and social deprivation9 are not only
the EU but also worldwide.
According to the Information Global Index (IDFI) of 2018, 10 countries around the world, which
are the highest in different countries, including Denmark.
Table 3: Global Index of Information.
Country Place Score from 100,
Switzerland 1 68,4
Netherlands Kingdom 2 63,32
Sweden 3 63,08
Great Britain 4 60,13
Singapore 5 59,83
USA 6 59,81
Finland 7 59,63
Denmark 8 58,39
Germany 9 58,03
Ireland 10 57,19
Source: Institute for Development of Freedom of Information
Thus, Georgia can take some sort of a Danish state model of the welfare state in terms of
implementing progressive taxes, as it provides the inclusive education of the society, more
equality, less unemployment and poverty. However, we should also take into consideration that
Georgia has a large non-informal sector like Spain, high unemployment rate and problems with
management system. We should take into account, that progressive taxes will certainly not bring
fair and inclusive development of the country, but with his introduction it will be another step
forward to the country's socio-economic development.
9Eurostat,http://ec.europa.eu/eurostat/statisticsexplained/index.php/File:Unemployment_rates,_seasonally_adjusted,_December_2015.png
144
2.3. Positive and negative sides of progressive taxes.
Progressive tax helps us to accomplish three goals of tax policy: Balancing inequalities between
social groups, increasing budgetary revenue and to encourage entrepreneurial activities.
Of course the primary task of the progressive tax system is to solve the first issue, elimination of
tax imbalances. For this purpose, we have to determine what kind of revenue is presented in
comparison with the budget. Georgia's 2018 budget most of the tax revenues come in two taxes:
VAT and income tax. (45.7% and 29.7% ).
Figure 2: The Structure of Georgian Tax Revenue.
Source: Ministry of Finance of Georgia
Accordingly, it's easy to explain, most of the tax cargo comes to the final customer, because they
are VG and income taxpayers, while only 7.6% of tax revenue on banking business and large
business. Progressive taxes can therefore be divided into several categories and everything will
be given its individual interest index.
Description of Income Tax Intervals:
At present there is a single rate for income tax in Georgia, regardless of the size of the
income. It does not matter whether the salary will be 500 GEL or 5 000, all are taxed at 20%.
Thus, we may introduce a percentage scale and interest rates according to revenue, however,
consider the average salary in Georgia according to the different sectors employed.
Table 3: The average monthly salary for different sector employees in Georgia (GEL)
Income tax30%
Import tax1%
VAT46%
excise15%
profit tax7%
other taxes1%
Income tax Import tax VAT excise profit tax other taxes
145
Sectors average salary
Financial activity 1500
construction 1413
State Governance 1313
Mining industry 1229
Transparency and communication 1219
Electricity, gas and water production and
distribution 1126
Real estate with real estate 1095
Health and social security 843
The degrading industry 825
Trade 791
Hotels and restaurants 614
Agriculture 593
Education 470
Source: Statistical Office of Georgia
As a result, the average monthly salary starts from about 500 GEL and it increases according to
the relevant sector.10 As a result of our analysis, we developed the following progressive taxation
method:
• 0% - 500 GEL
• 10% - from 500 to 1000 GEL
• 20% - from 1000 to 1500 GEL
• 35% - 1500 to 3000 GEL
• 45% - 3000 GEL above
Positive features of progressive taxes:
➢ Additional budget sources of income are created at the expense of high income
individuals, which is not provided in the normal system and most of the taxes come in
10 National Statistics Office of Georgia.
146
low and middle layers. Of course, it should not be so high that the tax is obliged to leave
the country.
➢ Based on the fact that the tax burden increases with great revenue, it is automatically
minimized to persons with small and medium income, which increases their
competitiveness.
➢ Tax rate reduction will be equal to the increase in salaries for small and medium income
persons, therefore, the state will facilitate business development.
➢ Progressive tax will facilitate the strengthening of middle class and its further
development.
➢ Reduces the inequality between the income of the population.
As for the negative sides of the progressive taxation method. There is a misunderstanding, that
the progressive taxation method prevents economic growth and stimulates the high income
people. We do not agree with this assumption, because the world's developed and developing
countries show the practice11, that the progressive tax system is essential for the formation of
social justice and economic welfare state, however, of course it is necessary to develop a tax
model and then implement it according to the economic and other social conditions of the
country.
Conclusion
So that, the real and perfect tax system in Georgia can not function properly without proper use
of tax base and tax regulatory function. Tax policy should be derived from a specific situation
and perspective strategic objectives.
Nowadays Georgia is actively developing different types of fiscal incentives, who may respond
to failures of economic development of the country. In this context, it is of utmost importance to
analyze European experience in terms of different fiscal approaches, while progressive tax is to
be implemented in Georgia step by step, accompanied by relevant management reforms and
industrial policies (Change of economy structure).
11 Forbes.ge. Which countries have the lowest taxes.
147
References
Abuselidze, G. (2015). Formation of Tax Policy in the Aspect of the Optimal Tax Burden.
International Review of Management and Business Research, 4(3).
Abuselidze, G. (2013). Optimal Tax Policy-Financial Crisis Overcoming Factor. Asian
Economic and Financial Review, 3(11), pp. 1451-1459.
Abuselidze, G. (2012). The Influence of Optimal Tax burden on Economic Activity and
Production Capacity, Intellectual Economics, Vol. 6, Iss. 4(16).
Abuselidze, G., (2011). The Prospects Of Budget Revenue In The Aspect Of Optimal Tax
Burden, International Scientific Conference “Whither Our Economies” November, pp.161-
166
Ricardo, D. (1937). Principles of Political Economy and Taxation.
Smith, A. (2011). Research, An Inquiry into the Nature and Causes of the Wealth of Nations.
Duncan, D., & Sabirianova, P. K. (2016). Unequal inequalities: Do progressive taxes reduce
income inequality?. International Tax and Public Finance, 23(4), 762-783.
Olsen, J., Kogler, C., Stark, J., & Kirchler, E. (2017). Income tax versus value added tax: A
mixed-methods comparison of social representations. Journal of Tax Administration, 3(2),
87-107.
The Tax Code of Georgia. Tbilisi. 2010 year;
https://matsne.gov.ge/ka/document/view/1043717
National Statistics Office of Georgia. The living and income of the population.
http://www.geostat.ge/?action=page&p_id=181&lang=geo
Overview of the tax system of Georgia. Tbilisi 2010 year;
https://www.transparency.ge/sites/default/files/post_attachments/Taxation%20in%20Geo
rgia_GEO.pdf
Progressive taxes and well-being in the state of Denmark and Spain;
http://european.ge
Newspaper "Radio Liberty" Gini Coefficient and Georgia 2017;
https://www.radiotavisupleba.ge/a/ekonomika/28583105.html
The Announcement of Taxes on Income Tax at the Akaki Tsereteli State University 2015;
https://moambe.atsu.edu.ge/uploads/files/1527240059_51-57.pdf
Developing Countries Tax Challenges, Richard M. Buridi, March 2008
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1114084
Eurostat.ge
http://appsso.eurostat.ec.europa.eu/nui/show.do
148
CULTURAL AND ECONOMIC INTERACTION: TRENDS IN EMPLOYMENT AND
BUSINESS OF CULTURAL ORGANIZATIONS IN THE EU
O.G. Rakauskienė Mykolas Romeris
University,Vilnius, Lithuania
V.V. Velikorossov Plekhanov Russian
University of Economics, Moscow, Russia
D.K. Balakhanova Plekhanov Russian
University of Economics, Moscow, Russia
Abstract: In conditions of economic instability the approach based on the study of the relationship
between culture and economy gains particular importance. The article considers the problem of
dependence on the previous development of the economy - the path dependence problem. Most
scientific studies on the issue of successful development, social and economic breakthroughs, and
well-being of a country are based on the concepts of economic growth, democratization of the
system, and liberalization of the economic regime. However, studies of the dynamics of world
statistical data for various countries over a very long period refute the validity of existing
hypotheses. In order to achieve social and economic progress and consolidate the successful
development of the country, the problem of the interconnection of culture and the economy is put
forward. The article gives some analytical conclusions on employment and business trends in the
EU culture organizations.
The purpose of the article is to put forward the problem of the interaction between culture and the
country's economic development, as a way for securing successful economic development for the
long term and to assess the impact of cultural indicators on economic growth indicators.
Key words: Interaction of culture and economy, dependence on the path of previous development,
path dependence problem, employment trends in the field of culture of the EU countries,
entrepreneurship in the field of culture, added value in the culture sector in the EU countries by
type of activity.
Key words: cultural and economics interaction, path dependence effect, general trends in cultural
employment and business, value added of cultural enterprises.
149
Introduction
The economic situation in the world, indeed in the EU countries, is alarming for many today. And
the matter is not only in the sanctions, in the fall of prices on oil, although of course they play a
role, but perhaps in another factor. Responding to the question, why one country becomes highly
developed while the other doesn’t, Professor Aleksandr Auzan asserts, that "in economic theory,
there is a path dependence problem: the country falls into the track, trying to get out of it, but all
the time slipping on the same path. Values and behavioral attitudes, what people consider bad or
good, acceptable and unacceptable, hold the country in the path, not the economic growth or
political system. For example, what is people’s opinion on paying tax, telling on your neighbor,
stealing from the treasury, claiming social benefits, when you're not entitled to them" (1).
The study of path-dependence problem, dependence on the trajectory of the previous development,
brought Nobel Prize to Douglass North. There is a David - North hypothesis, which assesses how
the institutions and culture influence this ‘sticking’ of the country on the path. It is culture that
makes a country follow this path.
Theoretical aspects: the problem of path dependence
Why do people live differently? The reasons for the difference are in cultural attitudes and the
resulting development of institutions. The economic behavior of entire nations is determined by
their cultural attitudes. Historically, the most prosperous in economic terms were countries with
"Protestant morality". However, the matter is not in religion, but in the behavioral settings. The
well-being is affected by values chosen by the society. The most economically prosperous
countries are those who have chosen "Protestant morality". It is not about the choice of religion,
but about the world view. Alexander Auzan thinks that the societies, which stop being suspicious
about rich people, and poverty is no longer considered a dignity, with time reach a higher level of
life. This is true for societies with different cultural backgrounds. In the long run, society itself
chooses the values and behavioral patterns. Cultural processes are slow, but very effective and
very significant.
There are two paths of economic development. Two paths of development are approximately like
the first and second space speeds. Both are connected with growth, with development. But the
countries that are in the B-path are growing much more slowly, according to long-term
observations, than the countries in the A-path. So B-path is like the first space speed. 200 years of
observations show that there have been only five cases of countries switching from the first speed
to the second. These are South Korea, Japan, Hong Kong, Singapore and Taiwan. These are all
150 Asian countries, all Buddhist. But it is necessary to notice, that, say, in the end of XX - beginning
of XXI century, there was a Catholic boom, as Ireland, Poland, the southern parts of Germany
sharply rocketed in development.
There were attempts to explain in different ways, why the country is in a certain path. In this
regard, three hypotheses were put forward.
The first hypothesis states that the main goal is to achieve economic growth, and all the rest
will be corrected on its own. It is known that countries sometimes achieve economic growth;
sometimes they grow even faster than others. Russia grew in the so-called "fat" years at a rate of
7-8% per year. This is a pretty good pace. Russian economy grew in the 1930-s at an incredible
pace, starting from 1929, the year of great breakthrough. Lithuania reached 10.3% growth rate in
2003-2004, and 9.8% in 2007, becoming one of the leaders (and got the name of the Baltic Tiger,
the Amber Republic), but again returned into the same track. This is a path dependence effect.
This means, that there are some blocks, which prevent countries form moving forward, from
making a breakthrough. One of these blocks is unwillingness to get into long periods of
transformations, the lack of a strategic approach; another one is impairment and lack of demand
for culture and education, giving its place to commercial and monetary targets; and the third one
is a complete lack of understanding of these mechanisms and no attention to them. As a result, the
country, which struggles for success, self-fulfillment, material prosperity, leaving its former state,
can’t achieve a new stage.
It was a hypothesis that the point is in economic growth, which is not confirmed in real life. High
rates of economic growth can be achieved, but the question arises, what is next.
The second hypothesis claims that the matter in the political system, that it is necessary to
change the system, to democratize, and then people will require development, and the development
will go. Unfortunately, this hypothesis is also not confirmed. (for example, Singapore is not
democratic at all).
There are quantitative researches, including studies on the consequences of revolutions, and it
turns out, that if a revolution takes place in a country with poor institutions, where people are not
used to comply with the laws, the economic situation of the country worsens. (Egypt, Ukraine).
This hypothesis proved wrong, too.
The third version, which is being tested currently, says that the matter is in the culture, but in
culture, which can be changed under the influence of education, some kind of long-term
work with the population, that it is possible to change the culture in the right direction in the
period of 10-15 years, destroying blocks for development. (As is happened in the south of
Germany and Poland).
The point is in the interpretation of values, such as labor, wealth, freedom, a long-term view.
151 The fundamental thing is that all countries, that made such a transition, were distinguished
by some common features, they all considered important self-realization, and not survival,
they counted upon long-term view.
Douglass North, a Nobel laureate in the field of economy, historian John Wallis and analyst Barry
Weingast in their book "Violence and social orders" (2) say, that the transition from the behind the
time world into the advanced world occurs rarely and requires not less than 50 years. They came
to the conclusion, that there are three conditions, on which different countries, like England, France
the United States, came to this curve of the 50-year transition.
These conditions are as follows. First, the elites should make laws for themselves and distribute
them to others, and not to make laws for others, making exceptions for themselves. This is the first
condition. Secondly, economic, commercial, political organizations must exist independently of
the lifetime of their creators, not being personalized. Third, the elites should not distribute
instruments of violence between themselves, like you get the military air force, and me - the secret
police, but collectively control these instruments. That's when a society takes this conception of
life, there begins the transition to a high stage of development.
One of the most intelligent politicians in Russia, Vyacheslav Nikonov, (3) as opposed to the
‘conflict of civilizations’ by S. Huntington (4) suggested a concept of a “concert of civilizations”
as an interaction of cultures and countries. He says that multiculturalism is considered today as the
official basis of state policy in many countries, people are increasingly looking for and find sources
of strength in their civilizational roots, national self-esteem is growing, pride is being revealed for
their country and its culture.
Also, in the book of Samuel Huntington and Lawrence Harrison, “Culture Matters,” the title itself
undoubtedly makes the topic undeniable.
Methodological approaches
There are many theories of culture, of which, it seems important to us, to undermine two main
approaches to the definition of culture. According to the first one, culture should be understood as
a way of life and a system of world perception inherent in one or another people (group of people).
This definition covers all aspects of people's lives, and is essentially identified with the country's
culture. In the second approach, culture is perceived as a set of values and norms of a given society
(5).
In our opinion, culture is a very broad concept, it is a system of behavioral, moral and ethical
values, beliefs, mediated by the mentality of the people, view of the world, education, motivation
of the population, and finally, the quality of the elite (thinking globally or, on the contrary, having
152 narrow concepts). Indeed, the meaning of the existence of a nation is culture. A country, a nation
is remembered not by economics, but by culture.
According to academician D.S. Likhachev, “culture represents the main meaning and main value
of the existence of both individual peoples, small ethnic groups, and states. Without culture
independent existence is meaningless... Culture can transcend time, connect the past, present and
future... A people’s life is influenced a lot by the environment created by the culture of ancestors
and their own." (6).
It’s important to add that D.S. Likhachev spoke about the increased aggressiveness in life. And
indeed he looked into the future. The aggression of wars between nations, repressions, and
alienation of peoples from each other, hostility and civil wars are aggressiveness that must be
fought. How to fight it? This aggressiveness should be overcome by culture, claimed Likhachev.
The culture of communication, education, reading, interests, etc. Culture is not aggressive, culture
calms and brings some kind of restrained force into a person.
It seems to us that culture is one of the strengths and advantages of a country: it is a source of
creating values and identity, self-awareness, sovereignty, the essence of the existence of a nation,
a people. It also contributes to improving the quality of life of the population, cohesion and
inclusiveness of a society. Creative and cultural sectors are the driving force for the economy, job
creation and foreign trade.
In all civilized countries considerable funds are allocated for the needs of culture. However, it is
pointless to expect immediate returns from all cultural institutions and organizations, because
culture has a long-term effect. Business on the contrary expects immediate returns. The return,
payback of culture is expressed in the material and spiritual development of people, a positive
impact on society, raising its morality and intellectual potential.
Cultural values mean not only individual objects, like monuments of architecture, sculpture,
painting, writing, printing, archeology, applied art, music, folklore, but also such phenomena as
traditions and skills in the fields of art, science, education, behavior, customs of peoples,
population groups, individuals.
The following sections of cultural statistics are highlighted in the Eurostat statistical database (7):
• employment
• entrepreneurship
• foreign trade in cultural goods and services,
• participation in the field of culture (reading books, newspapers, magazines, other publications;
visiting cinemas, theaters, museums, concert halls, etc. ),
• the use of IT for cultural purposes (Internet, social networks),
153 • spending on culture (households).
In the EU there is a statistical classification of economic activities (NACE) and occupations
(ISCO) in the field of culture. Cultural activities by economic sector include the following:
“creative, artistic and recreational activities”, “libraries, archives, museums, etc.”, “publication of
books, periodicals and other types of publications”, “printing", "creation of programs", "cinema,
video production, television, music production", "designer products". As to professions in the
cultural sphere, they include writers, architects, composers, journalists, actors, dancers, librarians,
artists, graphic designers, and so on.
The world knows the techniques, like techniques of Ronald Inglehart, Geert Hofstede, Schwartz,
Trompenaars and others, which allow to measure the dynamics of sociocultural characteristics.
And no matter how many discussions there are about the reliability of the technique of the
ingenious Geert Hofstede, the first person who came up with how to measure culture with
sociological tools, serious study involves the analysis of some statistical indicators on culture from
an economic point of view.
In this paper we will analyze only some of the available statistical data on the culture of the EU
countries, on employment and entrepreneurship in the field of culture.
The main trends in employment and entrepreneurship in the field of culture in the EU
countries
In our opinion there are two main trends in the EU countries on employment in culture in 2017:
• About 8.7 million workers are employed in the field of culture in the EU, which is 3.8% of all
workers in the EU economy. This proportion is relatively small, but stably unchanged over
a long period.
• The majority of workers are with higher education in almost all EU countries for all professions
in the field of culture. Culture, as a rule, is an area of highly educated people, and in a sense
it is an elite sphere.
According to Eurostat statistics, in 2017 there were 8.7 million people employed in all types of
activities and cultural professions, which is 3.8% of all employees. (Fig.1). During the period of
2012-2017 there was a small but steady growth of employed in the cultural sector. (Table 1). In
2017 the number of cultural workers increased by 6.7 % and amounted to 544,000 people. Average
annual growth was at 1.3%. However, this does not mean growth in employment in percentage
terms. In 2017, the share of employees remained at the same level as in 2012, and amounted to
3.8% of the total number of employees. This means that employment in the field of culture does
154 not lag behind the growth rate of total employment.
Source:: Eurostat, 2018
Table 1. Employment in the field of culture, 2012 - 2017
Source: Eurostat, 2017
155 The employment rate in the sphere of culture in the EU countries in 2017 varied from 1.6% in
Romania to 5.0% in Estonia. In the countries of the European Free Trade Association (EFTA)
(Iceland, Switzerland and Norway), this figure is significantly higher than the EU average, while
in the EU candidate countries (Monte Negro, Macedonia and Turkey) the percentage of people
employed in culture is below the average in the EU. Employment dynamics in the members of the
EU for the period of 2012-2017 is not homogeneous. While in most EU countries there has been
a slight growth or a steady level of employment in the field of culture, other countries
(Luxembourg, the Netherlands, Germany, Estonia, Greece, Hungary, Finland) are characterized
by a decrease in the level of employment. In 2017 compared to 2012 employment in occupations
in the culture field increased by 155 000 workers, the UK accounting for 30% of the total growth
of cultural employment in the EU, Spain accounting for 22%. Spain is the country with the highest
relative growth in culture employment (from 3.1% to 3.6% of total employment ).
The professions in the field of culture are filled mainly by employees with higher education,
that is, culture is a branch of highly educated people. In 2017 almost 60% working in this field
had tertiary education, only 8% were with lower secondary education, and about one third with
upper secondary and post-secondary education (Figure 2).
The share of workers with higher education in the field of culture (59 %) was twice higher than in
general employment (34 %), the gap between these indicators was 25 p.p. (Figure 3). Education is
the most significant characteristic of cultural employment. And this is not surprising, since most
professions in the field of culture require long years of study (e.g. architects, journalists, writers,
composers, etc.).
156
Fig. 2. Persons in cultural employment by educational attainment level, 2017 (%)
Source: Eurostat, 2018
Fig. 3. Persons with tertiary education in cultural employment and in total employment,
2017 (%)
Source: Eurostat, 2018
In 2017, almost half of the cultural workers in 25 EU countries had tertiary education (Figure 3).
The proportion of people with higher education accounted for two thirds in four EU countries:
Cyprus, Ireland, Belgium and Spain. This situation is significantly different from the general level
of employment, where the share of people with higher education reached 50% only in Ireland, and
in only 9 EU countries it is about 40%.
Entrepreneurship in the cultural sphere. In 2015 1.2 mln cultural businesses created an
added value of 200 bln euros. 5% of 1.2 mln cultural businesses worked in non-financial
business. Commercial enterprises in the cultural sector accounted for 2.8% of the total added value
of the EU, or about 200 billion euros.
For comparison, it should be noted that this indicator is higher than the same indicator for
wholesale and retail trade and vehicle repair (165 billion EUR). Total sales volume in the sector
of culture reached 475 billion euros, with non-financial businesses accounting to 1.7% of it.
There are over 150,000 enterprises of the culture in France and Italy alone, each of these countries
accounts for 15% of the total number of EU cultural enterprises. Together with Germany (with
130 000 culture enterprises) and the UK (100 000 companies), these four countries account for
about a half of the total number of EU cultural enterprises. All these countries, with the exception
of Italy, account for an average of 5% of the total number of cultural enterprises in the EU. The
157 biggest shares of cultural enterprises accrue to Sweden (7.6%), Holland (7.3%), Belgium and
Slovenia (6.5% each).
In terms of the total sales volume of cultural enterprises Croatia, Cyprus, France, Sweden and the
United Kingdom create an average of about 1.7% of the total turnover, including Cyprus's highest
contribution of 3.2%, mainly due to computer games production. The share of the UK, which
accounted for 8.2% cultural enterprises of the total number of the EU in 2015, is 25% of the total
trade volume of the EU culture.
Value added is the total income from current activities after deduction of subsidies and taxes. The
structure of the added value of cultural enterprises by country usually corresponds to the structure
of trade. Only Cyprus and the United Kingdom exceeded the average for the European Union
2.8%. Thus, the UK with a share of value added from cultural enterprises of 4.2% created 30% of
the total value of cultural enterprises in the EU. In contrast to the UK in such countries as Bulgaria,
Latvia, Lithuania, Hungary, Slovakia, the share of culture entrepreneurship in the national value
added did not exceed 2%.
In 2010-2015 economic trends in the cultural sector were subject to sharp fluctuations in the
EU countries. There is a positive trend at the EU level in 2011-2015: added value grew from year
to year by an average of 2.4%. This happened while the overall annual economic growth was at
3.2%. The gap is not due to a change in the number of cultural enterprises, it increased in pace
with the growth of all enterprises (an average of 1.5% per year).
In 2010-2015 the growth of the number of cultural enterprises was stable in seven EU countries,
where it did not exceed an average of 1% per year. In the rest 14 EU member states, the number
of cultural enterprises increased by more than 1% per year, in the Netherlands and Lithuania,
growth was above 10%.
And only in one country, Greece, the number of culture enterprises declined by more than 2%,
actually by 8.1% per year, and added value dropped by 19%.
A smaller decrease in the number of enterprises was observed in Italy (1.9% on average per year)
and Portugal (1.6%), which respectively affected the drop in value added (to 4.1% and 3.1%). In
contrast, with a 3.7% annual growth in the number of enterprises, the UK achieved a record
increase in value added of 11.8%, Lithuania came second with 8.5% increase in value added.
Another distinguishing examples are Poland (with an average rate of growth in the number of
enterprises 4.2%) and Slovenia (6.1%), with the considerable drop in value added - up to 3.0%
and 1.8% respectively.
The reasons for such fluctuations in the activities of cultural enterprises were the general trends in
the economy and were associated with structural and cyclical trends.
However, there are a few exceptional examples, where economic growth was several percentage
158 points lower than the growth of value added in the sector of culture. Thus, in Cyprus, the relative
stability of value added in the cultural sector (an annual decline of 0.5%) was observed due to the
business cycle that is declining, and a decrease in value added was 3.7% annually. While the UK
is a country where culture is represented at a high level, growing at a faster pace than the economy
as a whole (respectively 11.8% and 8.8%).
In contrast, in several countries the trends in value added were less positive than could be expected
from the state of the economy as a whole. It was observed partially in Italy (annual decrease of
added value in the cultural sector at 4.1% with consistently zero growth of total value added), in
Poland (decline in business development for a culture of 3.0%, while the total value added
throughout the economy grew by 2.5% annually), in Hungary (value added in the cultural sector
decreased by 4.1% against 3.8% of GDP growth), in Slovenia (decrease of 1.8% against growth
of 1.9%) Portugal provides an exceptional example of the low level of development of the cultural
sector, where the rate of aggregate economic growth was declining (by 1.3% annually), and in the
cultural sector there was a twofold decrease in the growth rate of value added (3.1%).
Cultural business is dominated by architecture, design and photography, that account for 51% of
all kinds of culture activities.
Figure 4. Number of EU cultural enterprises by broad group of activities, 2015 (%)
159 Source: Eurostat, 2015
At the EU level, the main activities in the cultural business are architecture, design and
photography. Architecture alone accounts for 25%. The remaining activities are distributed more
evenly (approximately 10% each), with the exception of publishing (4% of all cultural enterprises),
and television and radio-broadcasting programs, news agencies occupy a small proportion (2 %)
(Figure 4).
In contrast, in terms of added value (Figure 5), film production and software products (programs
creating) create approximately the same share of added value - 21%, along with television and
radio broadcasting programs, news agencies, printing books and periodicals, computer games
(21%). The exception is the sale of specialized sectors (4%), translation and interpretation (2%).
Figure 5. Value added of EU cultural enterprises, by broad group of activities, 2015 (%)
Source: Eurostat
At the national level, architecture, design and photography occupy a leading position in all EU
countries in the activities of cultural enterprises. However, in a number of countries, such as
Bulgaria, the Czech Republic, Hungary, one third of enterprises are engaged in this type of
activity. Production activities are often in the second and third positions (the highest figure is 25%
in Croatia), with the exception of the Netherlands and Luxembourg (6% of all cultural enterprises).
Culture in the EU is given an important role, there is an understanding that is embodied in real
160 life, that the creative sector makes a huge value added and is the driving force of the economy and
the socio-economic progress. The EU gives a substantial support for the culture as part of the
Creative Europe program. The development of cultural policies is carried out at the state level,
which is embodied in developing of the Work Plan for Culture (2015-2018). This plan reflects the
priority directions the field of culture of the EU. At present the development of a unified
methodology and the creation of a statistics system for comparative culture analysis of the EU
countries is carried out.
Conclusions
1. The path dependence theory gives response to the question, why one country becomes
developed, and another one trails behind. A country, which strives for success, fulfillment, material
prosperity, leaving its former state, is not able to enter a new stage for some reason, i.e. gets into
the common path. What holds the country in this path is not about economic growth or political
systems, but is about values and behavioral attitudes. The reasons for the difference are in cultural
settings. The economic behavior of peoples is determined by their cultural attitudes.
Culture is one of the strengths and advantages of the country: it is a source of creating values and
identities, self-awareness, the essence of the existence of a nation, people. It also contributes to
improving the quality of life of the population, cohesion and inclusiveness of society. Creative and
cultural sectors are the driving force of the economy, job creation and foreign trade. Therefore, the
development of relationship between culture and economy should become one of the strategic and
backbone areas in achieving social and economic progress.
2. The development of culture, creative technologies, the culture entrepreneurship sector in the EU
is given a special place, there is an understanding that the culture sector creates a significant share
of value added in total national income. In our opinion, two main trends stand out in culture
employment in the EU countries: the first, about 8.7 million people are employed in the field of
culture in the EU, which is 3.8% of all workers in the EU economy. This proportion is relatively
small, but stably unchanged over a long period. And the second, in almost all EU countries in all
cultural occupations, the majority of workers are with higher education. Culture, as a rule, is an
area of highly educated people.
3. The cultural sector of entrepreneurship makes a significant contribution to the economy of the
EU countries. 1.2 mln cultural businesses functioned in 2015 in the EU. 5% of them worked in
non-financial business. Commercial enterprises in the cultural sector accounted for 2.8% of the
total added value of the EU, or about 200 billion euros. Total sales volume in the sector of culture
reached 475 billion euros, which is three times higher than the same indicator for wholesale and
161 retail trade (165 billion EUR).
4. Considerable funds are allocated for the needs of culture in all civilized countries. However, it
is pointless to expect immediate returns from all cultural institutions and organizations, because
culture’s effects are long-term. At the same time business expects immediate returns. The return,
payback of culture is expressed in the material and spiritual development of people, a positive
impact on society, raising its morality and intellectual potential. The most effective way to change
social and culture settings and values of a society is to develop its education, abilities, skills and
competence.
162 References Auzan, A. Development and the path dependence problem. World Economy and International Relations, Volume 61, No. 10, 2017. Douglass North, John Joseph Wallis, Barry R. Weingast. Violence and Social Orders. Cambridge University Press, 2012. Nikonov, V. Code of Civilization. Moscow: Publishing House "E", 2016. Huntington, S. The Clash of Civilizations. Moscow: AST, 2014. Pivovarov S. E., Maksimtsev I. A. Comparative management. - St. Petersburg: Peter, 2008. Likhachev, D.S. Favorites. Thoughts on life, history, culture. Moscow: Russian Culture Fund, 2006. Eurostat. Culture statistics. 2015 Eurostat. Culture statistics - cultural enterprises. 2016. http://ec.europa.eu/eurostat/about/our-partners/copyright Eurostat. Culture statistics Cultural employment, 2018. http://ec.europa.eu/eurostat/about/our-partners/copyright Alexander Auzan. Modernization Begins in People Minds and their System of Value. http://esquire.ru/auzan.
