MEASURING OF THE RISK FOR FOREIGN DIRECT INVESTME...
http://www.economy.nayka.com.ua/?op=1&z=2137
Електронне наукове фахове видання "Ефективна економіка" включено до переліку наукових фахових видань
України з питань економіки (Наказ Міністерства освіти і науки України від 29.12.2014 № 1528)
№ 6, 2013 Назад
Головна
UDC 330.3:477/478/479.22=111
T. Zhydetskyi,
magister department of economics and entrepreneurship,
National technical university of Ukraine «Kyiv polytechnic institute», Kyiv
MEASURING OF THE RISK FOR FOREIGN DIRECT INVESTMENT IN COUNTRIES WITH
LARGE SHADOW ECONOMY ON EXAMPLE OF EASTERN EUROPE
In this article we investigate the risk of foreign direct investment (FDI) to countries with a high level of shadow economy (more than
40%) on example of three Eastern European countries: Georgia, Moldova and Ukraine. This study is relevant because of the
tendency to increase FDI around the world. As a basis for measuring investment’s attractiveness was taken in two-dimensional
adapted model of country risk analysis. This article shows how based on economical and shadow economical data, which is
available free on the internet, everyone can get a universal model for the calculation of risks for FDI.
Keywords: FDI, shadow economy, Eastern Europe, investment, risk, firm.
Problem statement. The world global finance market was in the deep depression in 2008. At that time, the volume of the FDI was falling down on 16%. However since
2010 FDI was stable growing and reached at the 2011 level of 2008-th. United nations conference of trade and development [1] forecasts the following growth of FDI in 2012 on
the level of 1,6 trillion $ US.
Investors began looking for new markets for investment on the background of stabilize the global financial markets. Because of the crisis in 2008 and the crisis of the
euro area, investors usually invest in a little risky countries such as Germany, Holland, Switzerland, Scandinavia, New Zealand, Hong Kong. The risks in these countries are low,
however, and return on such investment is also low. Now investors are looking for more profitable opportunities for investment.
In this case one of the opportunity is investment to the emerging economy markets. Of course, the risks for those countries are pretty high, but on the other hand, the
investment profit is also higher than elsewhere. In addition, the high risks also discourage potential competitors. In this paper, there will be research of the Easern Europe Region
countries that, according to studies of Ernst&Young (2011) is the thirds attractive region for FDI in the world [2]. In particularly it’s Ukraine (7,207billions US dollars of FDI in
2011 with 10,96% growth), Moldova(274mln US dollars, 38,8%) and Georgia (973,11mln US dollars of FDI in 2011, 19,15%) [3]. These countries are pretty similar in the
character of international economical relations and the structure of shadow economy.
Analysis of recent research and publications. There are plenty different tools to estimate approximate investment risks for each country. The problem for developing
countries is that investors want to be sure in their investments. To do this they need a tool for calculation of risk that would be better suited to the specifics of these countries.
Common to all developing countries is the presence of a significant amount of shadow economy. The thesis of the interdependence of FDI and the shadow economy is
confirmed by research, such as Professors F. Schneider and M. S. Habibullah[4]. Unfortunately only a small portion of the contemporary researchers (eg Roll, R., and J. Talbott
[5]) in the development of models for calculating risks include FDI into account the impact of the shadow economy. But even in these calculations, its role is low.
One reason for this is the thesis that the shadow economy is a consequence of the political system. Relationship between FDI and political risk exposure was confirmed
in empirical studies of American economists 40 years ago [6]. When predicting the risk in most models use their two factor option with regard to political and economic situation
[7]. But do not take into account the fact that the nature of the shadow economy in different countries is different. In some countries, the informal sector is significantly affected
by the policy and competes with the legal business (such as mafia in Italy and Albania or the Yakuza in Japan).
As a basis in our research was taken two dimensional model of country risk analysis for continuously format developing by american researchers C. McGowan and E.
Moeller (2009). This model based on the Foreign Investment Risk Matrix (firm) developed by Bhalla and include political and economical situation. The economic component
describes the processes and phenomena which taking place a long period of time in the past. The changes taking place in it over time usually expressed distinct seasons, cyclical
and trend component, as well as exposed to metric analysis. Political component is more volatile, processes are difficult to describe by statistical data. Often, it requires expert
opinion on a particular country.
Feature of Eastern Europe is that policy and shadow economy are closely linked, in many cases, the regulatory function of the state to adopt self mafia groups.
Uncertainty rules the markets are often deterred investors.
But the experience of multinational corporations like McDonald's, Shell, METRO Cash&Carry, IBM, Auchan, Nestlé shows that by properly measured risks can give a
good returns in these countries.
In this paper we use the method proposed Foreign Investment Risk Matrix using multiple discriminant analysis proposed by C. McGowan and E. Moeller in 2005. C.
McGowan and E. Moeller chose these political factors that influenced the firm: the government's attitude to fdi, the level of conflict and corruption in the country. For me
selected for the study of the evaluation of these factors are not significant for the following reasons:
The attitude of the governments of all three countries to fdi officially considered positive, there are several free economic zones, in practice, the legal framework and
monitoring Decree requires substantial revision. You can talk about the loyalty of governments, but a large amount of shadow economy and corruption undermine to Government
priorities.
The level of conflict also remains stably low. However in Moldova and Georgia are unrecognized self-declared republic, but the overall situation does not threaten the
stability of the state [9].
Formulation of purpose. Corruption in the region remains high, it is actually only one of three factors that really plays a significant role in predicting risk of investment
in these countries. I would like to extend this concept and model C. McGowan and E. Moeller that use a three factor: structure and economic factors and shadow. The purpose
of this study is to create a universal method, so data for your model I chose from online sources, which are available for free, so that if necessary, you can calculate this ratio for
any other country. I would like to emphasize that the presented method is most suitable for countries where the informal economy is clearly competing with the legal (ie, the
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level is 40% or more).
The main material. The risks connected with the shadow economy have following factors:
1) Dependence of political elite on business - indicates how political lobby affects the business of the country. The higher the ratio, the greater the likelihood is that
competitors will provide dirty game using the policy. The calculation of this parameter was performed using the ratio of wealth 10 richest politicians or civil servants in the
country to total GDP. The result was presented on a five-point scale (% of GDP divided by 2. If coefficient is > = 5 = 5 points). Information about the fortunes of Eastern
European politicians is received from mass media. Value of GDP was taken from World Bank Database (2012) [10]
So, we can say that:
Wealth of the richest politicians in Ukraine: $23.652 billion [11], GDP=$165.2 billion Coefficient= 14,3% GDP=5 points.
Georgia= $8.4 [12] (the date is just for prime minister, but it’s enough), GDP= $14.37 billion Coefficient = 58,4 %GDP = 5 points
Moldova = $0.538 billion [13], GDP= $7 billions, Coefficient = 7,7 %GDP = 3.85 points
The second index is the level of shadow economy in the country. This index we also get from the internet, most recurrent source publishes it annually. We analyze the
country, where the level of the shadow economy is more than 40%. The shadow economy by 90% is the maximum level for most countries, thus from the real level of shadow
economy we take of 40% and divide by 10 (Information about the level of the shadow economy is derived from reports of Chris Prentice (2010) and Diana Lungu (2012)[14].
The presence high shadow economy indicates the presence of highly organized crime. Having organized shadow economy can also have a positive effect (such as tax evasion or
illicit use of technology).
That should be taken into account by accomplishment the matrix weights.
Coefficient for Ukraine = (58.1%[15]-40)/10=18.1= 1.81 points
Georgia = (72.5% [15] – 40)/10= 32.5=3.25 points
Moldova = (45.08%[14]-40)/10=0.508=0.5 point.
The third indicator is indicator of corrupted officials and civil servants. It is subindicator of Transparency international (2012) [16]. It shows how commonplace for
employees is the concept of a bribe.
In researched countries, bureaucratic system often has a significant impact on economic decision-making, so this figure is important for our study.
Coefficient for Ukraine: 4.1
Coefficient for Georgia: 2.7
Coefficient for Moldova: 3.8
This ratio can also be taken as a plus sign and a minus. Usually for multinational corporations their international reputation are very important, so be embroiled in
bribery scandal would be desirable for them. But if, for example, they create a subsidiary that will operate in the domestic market of the country, bribing officials can greatly
simplify access to the necessary information, obtaining building permits or leases land to avoid unnecessary checks and TN
Risks related to such economic factors:
Among the relevant economic factors that affect the risks of investments we are focusing on gross domestic product per capita, the Inflation Rate (The World Bank
2012). and the index of complexity of doing business (The World Bank, International Finance Corporation 2012)[17]. A detailed explanation of why these are important
indicators presented in primordial (Carl B.McGowan, Jr. Susan E. Moeller (2009)). As far as the third parameter (state assistance to foreign investments), in this case it would be
better to replace it with the ease of doing business index [18]. This index is correlated with relatively little shadow economy and shows real opportunities for business, despite
the high level of corruption in the country. It will also be assessed on a five point scale. (183 positions in the ranking, the best position - 1 worst - 5 points). In order to clarify the
calculations in the tables this factor cited "the complexity of doing business"
Rate of GDP per capita for these countries is:
Ukraine = $3615.38 per capita = 127 place in the world = 3 points
Georgia = $ 3202.53 per capita = 136 place in the world = 3.3 points
Moldova = $ 1966.93 per capita = 148 place in the world = 3.6 points
Coefficient of the Inflation Rate is:
Ukraine = 7.96% = 129 place in the world = 3.7 points
Georgia = 6.64 %=118 place in the world = 3.4 points
Moldova = 6.11 %=112 place in the world = 3.2 points
Coefficient of the complexity of doing business:
Ukraine 137 place in the world = 3.7 points
Georgia 9 place in the world = 1 points
Moldova 83 place in the world = 2.2 points
So we've got all the necessary information for making an investment decision. Based on this data we formed FIRM with taking to account the matrix weights. To do
this, the corresponding data should be multiplied with matrix weights that are depend on the goals and conditions of enterprise.
As an example was shown coefficients that can be realistic for U.S. multinational corporations that produce the smartphones and wants to enter the European market.
The important criteria for it are: the costs of labor, a preferable contract for construction of plant and storage of waste, avoiding formalities in business and a stable exchange rate.
Given in this example information we will take into account when we choose coefficients to form model of country risk analysis.
For such company weighting coefficients might look like this:
For political risk factors:
Dependence of the business - 40%
The level of the shadow economy - 40%
Corrupt officials - (-) 20%
For economic risk factors:
GDP per capita - (-) 15%
Inflation - 40%
Ease of doing business - 45%
Those factors which are important for the company and can be used in specific cases (eg low GDP points to cheap labor, corrupt officials can be used in obtaining
benefits for the construction and maintenance of business). Of course, these features of the economic system should be used with caution engaging in this local consultants and
specialists.
In the end, you must specify the overall weighting coefficients for political and economic factors. In our example, it may be 40% to 60% of the political and the
economic factors.
Below we obtained data for the selected countries and to determine the optimal region for business:
Table 1.
Model of FDI risk in Ukraine
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Table 2.
Model of FDI risk in Georgia
Table 3.
Model of FDI risk in Moldova
As we see, Moldova has the lowest rate of risk, so investment in this country will be the most sure.
Results
The study was based on a two-dimensional model calculation of risk C. McGowan and E. Moeller for FDI. The study critical specified this model for countries with a
high level of shadow economy. The model has been update by measuring performances at 5 point scale and included the possibility of sub-zero coefficients. It was also taken
into account the possibility of sub-zero coefficients, that describe the possibility of use of specific economical features for an own purposes of enterprise. Most enterpriseses by
the country selection for investment are not focused on the whole world but on the particular region in which they, for whatever reasons, decided to invest. As example we chose
Eastern Europe because of its dynamic growth of foreign direct investments and advantageous strategically geographical location. In the study was used the data that are public
and free. With the proposed model it can be easily to spend required calculations for any particular region. It helps businesses to pre-determine the risks of investments in the
particularly region without spending money on employing of consultants and experts on this issue.
References:
1. United nations conference of trade and development(2012). World Investment Report 2012: FDI trends and prospects. United Nations New York and Geneva
2. Ernst&Young 2011. Ukraine FDI report 2011. Ukraine, Ernst & Young
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3. The World Bank (2012). GDP(current US$). Washington, dc: World Bank.
4. Habibullah S. Foreign Direct Investment and Shadow Economy: A Causality Analysis Using Panel Data /Habibullah S., Schneider F./ online under:
http://ideas.repec.org/p/pra/mprapa/14485.html
5. Roll R., Talbott J. Political Freedom, Economic Liberty, and Prosperity /Roll R., Talbott J./ - Journal of Democracy 14, 3. pp. 75-89, 2003
6. Scaperlanda A. determinants of US direct investment in Europe / Scaperlanda A., Balogh R./ - European economic journal pp. 381-390.
7. Jamuna A. Determinants of FDI, A Survey - Welwirtschaftliches Archiv, Vol. 116, p. 739-773. 1980
8. McGowan C. A Model for Making Foreign Direct Investment Decisions Using Real Variables for Political and Economic Risk Analysis / McGowan C, Susan Jr.,
Moeller E./ Managing Global Transitions Volume 7 Number 1, 2009.
9. Heidelberger Institut for international Konfliktforschung: Conflictbarometer, 2010
10. The World Bank Database, online under: http://www.worldbank.org/
11. The first Ukrainian Ranking of the «Forbes» - Nashi Groshi - online under: http://nashigroshi.org/2011/04/10/pershyj-ukrajinskyj-rejtynh-forbes/
12. Bidzina Ivanishvili – Forbes - http://www.forbes.com/profile/boris-ivanishvili/
13. TRIBUNA „TOP 10 the richest politicians of Moldova” 1 July 2012 11:00
14. Diana Lungu(2012) IDIS Study: Moldova, a prey of the underground economy and tax evasion. National Endowment for Democracy.
15. Chris Prentice(2010). Shadow Economies on the Rise Around the World. Bloomberg Businesweek. Shadow Economies
16. Transparency International(2012): CORRUPTION BY COUNTRY / TERRITORY Berlin, Germany: © Transparency International
17. The World Bank, International Finance Corporation (2012). “All Doing Business 2012. Washington, dc: World Bank.
18. “Doing Business 2012 Doing Business in a More Transparent World “ the world bank, international finance corporation – online under:
http://www.doingbusiness.org
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