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Х. Башев, д-р екон. наук, проф.
1нститут аграрной економ1ки, Соф1я, Болгар1я
ЕМП1РИЧНЕ ДОСЛ1ДЖЕННЯ ЗВ'ЯЗКУ М1Ж УПРАВЛ1ННЯМ ТА СТ1ЙК1СТЮ В БОЛГАРСЬКОМУ С1ЛЬСЬКОМУ ГОСПОДАРСТВ1
Застосовано мiждисциплiнарну структуру новоУ iнституцiональноi' економши, визначено рiзнi ринковi, приватн, колективн, гро-мадськ та гiбриднi способи управлiння й оцнено Ухнш вплив на аграрну стiйкiсть у БолгарУУ. Викладено методологiчну основу досль дження, визначено домшуючi режими управлiння в болгарських господарствах рiзного юридичного типу, розмiр, спецiалiзацiю, еколо-гiчне й географiчне розташування та оцшено Ухнш вплив на стiйкiсть сльського господарства i його економiчнi, соц/'альн/' та екологI-чн основи. Представлено висновки для подальших дослiджень, удосконалення державноУ полiтики та формування приватноУуправлiн-ськоУ стратегГУ. Сльськогосподарськ виробники рiзного призначення у своУй дiяльностi й вiдносинах використовують абсолютно не схожi комбшацУУ ефективних ринкових, приватних, колективних i гiбридних способiв управлiння. Окремi фактори i способи, як найбльше сприяють полiпшенню аграрноУ стiйкостi на сучасному етап розвитку, - це особистi переконання та iнiцiативи менеджера, ресурси фермерських господарств, iнновацшний потенцал, стратеги майбутн iх прибуткв i вигод, рiвт та динамка ринкових цн, субсидУУ на основi регiонiв, а також i неофiцi йнi угоди.
Ключовi слова: аграрне управлiння, стiйкiсть, ринок, приватний, колективний, гiбридний режими, Болгарiя
Х. Башев, д-р экон. наук, проф.
Институт аграрной экономики, София, Болгария
ЭМПИРИЧЕСКОЕ ИССЛЕДОВАНИЕ СВЯЗИ МЕЖДУ УПРАВЛЕНИЕМ И УСТОЙЧИВОСТЬЮ В БОЛГАРСКОМ СЕЛЬСКОМ ХОЗЯЙСТВЕ
Применена междисциплинарная структура новой институциональной экономики, определяются различные рыночные, частные, коллективные, общественные и гибридные способы управления и оценивается их влияние на аграрную устойчивость в Болгарии. Изложена методологическая основа исследования, определены доминирующие режимы управления в болгарских хозяйствах различного юридического типа, размер, специализация, экологическое и географическое расположение и оценено их влияние на устойчивость сельского хозяйства и его экономические, социальные и экологические основы. Представлены выводы относительно дальнейших исследований, совершенствования государственной политики и формирования частной управленческой стратегии. Сельскохозяйственные производители различного назначения в своей деятельности и отношениях используют совершенно не похожие комбинации эффективных рыночных, частных, коллективных и гибридных способов управления. Отдельные факторы и способы, наиболее способствующие улучшению аграрной устойчивости на современном этапе развития, - это личные убеждения и инициативы менеджера, ресурсы фермерских хозяйств и инновационный потенциал, стратегии будущих прибылей и выгод, уровни и динамика рыночных цен, субсидии на основе регионов, и неофициальные соглашения.
Ключевые слова: аграрное управление, устойчивость, рынок, частный, коллективный, гибридный режимы, Болгария.
Bulletin of Taras Shevchenko National University of Kyiv. Economics, 2018; 3(198): 29-34 УДК 340
JEL classification: F21, F23
DOI: https://doi.org/10.17721/1728-2667.2018/198-3/2
L. Belascu, Doctor of Sciences (Economics), Professor ORCID iD 0000-0002-7711-3746 Lucian Blaga University of Sibiu, Sibiu, Romania, А. Horobet, Doctor of Sciences (Economics), Professor, ORCID iD 0000-0001-5821-0244 O. Popovici, Doctor of Sciences (Economics), Assistant Professor
ORCID iD 0000-0002-0457-9190 Bucharest University of Economic Studies, Bucharest, Romania
LOCATION ATTRIBUTES OF EMERGING ECONOMIES: AN ANALYSIS USING PRINCIPAL COMPONENTS
Our paper investigates the location attributes of a large sample of emerging economies from the perspective of foreign direct investments and multinational companies' presence abroad. We use several macroeconomic variables that take into account the relevant location attributes for the decision of multinational companies to invest abroad and include them in a Principal Components Analysis to reveal the most relevant locational attributes or combination of such attributes that influence the decision of multinational companies to invest abroad. We find that only four variables had the most important contributions to the principal components: GDP per capita, international reserves, mobile phones subscriptions and labour force. Labour force is the variable that contributes the most to the first factor and its contribution grows in importance as we depart from 1994. At the same time, GDP per capita has become less important in recent years.
Key words: foreign direct investments, emerging economies, principal components analysis.
Introduction. The interest that emerging markets developed for attracting foreign direct investments (FDI) is based on the latter being perceived as drivers of sustained economic growth, through various channels - increased employment (Santos-Paulino A. and Wan G. [22], Inekwe J. [14]), higher factor productivity (Nair-Reichert U. and Weinhold D. [21], Zhou D. et al. [25]), technological spillovers (Balasubramanyam V. et al. [2], Borensztein E. et al. [6]), human capital development (Miyamoto K. [18], Majeed M. and Ahmad E. [19]) and export markets (Zhang K. and Song S. [24], Kneller R. and Pisu M. [16]). On the other hand, when one observes multinational enterprises' preference for foreign direct investments instead of exports or other internationalization forms, these companies tend to delo-
calize in foreign markets only if their so-called "internalization advantages" allow them to do so (Dunning J. [9]).
In this framework one should not be surprised that a permanent competition between emerging and developed markets, on one hand, and between emerging markets, on the other hand, has surfaced in the last decades. This competition is based on two types of attractiveness factors for multinational enterprises: on one hand, one can identify a number of "genuine" attributes of emerging markets, and, on the other hand, we may refer to various types of incentives and stimuli offered by emerging markets governments to multinational enterprises, in their attempts to attract higher volumes of foreign direct investments.
The "genuine" factors were divided in two main categories, according to existing academic literature: (1) the "tra-
© Belascu L., Horobet А., Popovici O., 2018
ditional" factors, and (2) the "new" factors. Among the "traditional" factors the literature makes reference to: (1) market size and growth potential, as indicators of a country's potential to absorb outputs and/or to benefits from large production volumes in the form of economies of scale (Bevan A. and Estrin S. [4]); (2) low costs and higher availability of production factors, particularly in terms of labour and raw materials (Kinda T. [15]); (3) low and/or immature competition, which might provide a "first-mover" advantage to the multinational company (Boeri T. and Brucker H. [5], Liang Y. [17]); (d) infrastructure, as transportation and communication networks (Mollick A. et al. [20]). The "new" factors advanced by the existing literature and that have the potential to increase the emerging economies' attractiveness for foreign direct investments are: (1) macroeco-nomic stability, which encompasses transparent institutions (Campos N. and Kinoshita Y. [7]), the extent of private property (Carstensen K. and Toubal F. [8]), the conditions related to profit repatriation, general legislation and tax regimes applicable to foreign companies (Wei S. [23]), or the corruption level (Al-Sadig A. [1]); (2) geographic distance, particularly the establishment of closer links between industrial locations and marketplaces, with influence on transport and communication costs (Benassy-Quere A. et al. [3]); (3) the level and dynamics of country risk (Holland D. and Pain N. [11], Hayakawa K. et al. [10]).
The goal of the present paper resides in investigating the dynamics of location attributes of emerging markets by taking into account several of the above mentioned factors and in identifying the changing importance of these factors in time foreign direct investments. The main contribution our paper has to the academic literature in the field is to characterize in dynamics the shifting location attributes of emerging economies from the FDI perspective, thus providing support for local institutions regarding the decision-making process in favor of multinational enterprises' presence in these economies.
The paper is organized as follows: Section 1 describes the data and methodology used in the empirical analysis, Section 2 presents the most important results of our research, while Section 3 concludes and indicates directions for further research.
1. Data and research methodology. The research is undertaken on a large sample of emerging countries (41), from different regions and with various levels of development, but included in this category by BBVA Research. The emerging countries employed in our analysis are classified in three categories, based on countries' absolute growth, as follows: (1) Emerging and Growth Leading Emerging Economies (EAGLE) - these countries have an expected incremental GDP that will surpass the average g7 economies' GDP (except USA) in the next ten years: China, India, Indonesia, South Korea, Brazil, Mexico, Russia, and Turkey; (2) NEST - these are countries with an expected incremental GDP lower than the average G7 economies' GDP (except USA) in the next ten years, but higher than Italy's: Argentina, Bangladesh, Chile, Colombia, Egypt, Malaysia, Nigeria, Pakistan, Peru, Poland, Thailand, South Africa, Ukraine and Vietnam; (3) Other Emerging Markets: Bahrain, Bulgaria, Czech Republic, Estonia, Hungary, Jordan, Kuwait, Latvia, Lithuania, Mauritius, Morocco, Oman, Romania, Slovakia, Sri Lanka, Sudan, Tunisia, UAE and Venezuela.
The empirical analysis uses ten macroeconomic variables that illustrate the relevant locational attributes for MNE's decisions to invest abroad, grouped in five categories: (1) Market size and potential attributes - this category includes three variables: (i) GDP per capita in US dollars (GDPC); (ii) domestic credit to private sector as percentage of GDP (DC); and (iii) the percentage of urban population
in total population (UP); (2) Country risk attributes - this category includes two variables: (i) the inflation rate (INF), as the annual percentage change of the Consumer Price Index; and (ii) international reserves including gold (IR), denominated in US dollars; (3) Infrastructure and access to information attributes - one variable, as the number of mobile cellular subscriptions per one hundred people (MS); (4) Labour market attributes - this category includes two variables: (i) the labour force (LBF), as the number of people aged 15 and older who represent economically active population, according to the International Labour Organization, and (ii) labour force participation rate (LBP), calculated as percentage of total economically active population above 15 years, who work for the production of goods and services during a specified period; (5) World economic integration attributes - two variables: (i) trade balance in US dollars (TB) and (ii) trade openness (TO), calculated as sum of imports and exports and divided by GDP. Data on location attributes is collected for the period 1994-2011 with annual frequency from various data sources: Eurostat, Organization for Economic Co-operation and Development (OECD) Database, World Trade Organization (WTO) Database and World Bank.
Principal Components Analysis (PCA) is a multivariate data analysis method that aims at converting a set of observations belonging to probably correlated variables into principal components that represent a set of linearly uncorrected variables. PCA was developed by Hotelling (1933) and Hotelling (1936) and is typically used as an exploratory data analysis tool and for constructing predictive models. The technique finds a set of weighted linear composites of the original variables where each composite (a "principal component") is uncorrelated with the others. The weights are identified through eigen-decomposition that generates eigenvalues (these represent the amount of variation accounted for by the composite) and eigenvectors (they give the weights for the original variables).
In our paper, PCA is used with the aim of reducing the number of variables that supposedly influence the location decision of multinational companies (MNEs), thus allowing us to identify of a smaller number of country or location attributes that are relevant for the decision of MNEs to invest abroad. PCA leads to this set of factors by developing an analysis in the N-dimensional space defined by P variables (countries' location attributes) and N cases (each emerging country is a particular case), which presumes diagonalizing of a symmetric matrix - a covariance or correlation matrix. The result consists of a set of factors that represent a linear combination of original variables and that are uncorrelated. Also, their number is reduced, while their contribution to total data variance is maximal.
In performing the PCA we are interested in identifying the countries' grouping based on the two most important factors and in observing the contribution of each variable to the first most important factor, as a way of determining which variables contribute the most to the grouping. PCA is undertaken for each year and for the entire time frame of our analysis. This approach provides us with a dynamic perspective on the shifting country attributes that influence the MNEs' decisions to delocalize abroad.
2. Results. The first step in our empirical analysis involves calculating the eigenvalue for each factor and of retaining only those factors that had a eigenvalue which is higher than 1. As Table 1 shows, the number of principal components (factors) that are significant varies between two to four, depending on the year, as well as over the entire period. At the same time, the first two factors explain more than 50% of the total variance, while the first factor is
always the most significant, regardless of the time frame of the analysis - annual or over the entire time frame.
The second step is an analysis of how much each variable has contributed to the first factor, annually (see Table 2) and overall. Only four variables have the most important contributions to the principal components, regardless of the year for which the analysis is undertaken: GDP per capita (in 1994, 1995 and 1996), international reserves (in 1999 and 2000), mobile subscriptions (in 2004, 2009 and 2010) and labour force (in 1997, 1998, 2001, 2002, 2003, 2005, 2006, 2007, 2008 and 2011). By far, labour force is the variable that contributes the most to the first factor and its contribution becomes more important as we move from 1994 to 2011. At the same time, GDP per capita becomes less important towards 2011. International
reserves is a variable that displays an interesting pattern over the years, with important contributions between 1997 and 2003, and afterwards with diminishing importance in 2004 and with increasing values of its contribution until 2010. The fact that labour force is the most important variable that explains the differences between countries should not come as a surprise, given the various sizes of emerging countries' populations and, consequently, their labour force. Interesting enough, GDP per capita does not play a significant role in terms of differentiation, except for 1994 to 1996. At the other end, a number of variables hold small explanatory power for the differences between countries: domestic credit, trade balance, trade openness, percentage of urban population and labour participation.
Table 1. Eigenvalues and total variance explained by the significant principal components
Year Value Eigenvalue % Total Cumulative Cumulative Year Value Eigenvalue % Total Cumulative Cumulative
Number Variance Eigenvalue % Number Variance Eigenvalue %
1 2.681 26.812 2.681 26.812 1 3.014 30.139 3.014 30.139
1994 2 2.515 25.154 5.197 51.966 2006 2 2.500 25.001 5.514 55.140
3 1.417 14.171 6.614 66.138 3 1.427 14.267 6.941 69.407
4 1.100 10.999 7.714 77.136 1 3.137 31.373 3.137 31.373
1 2.751 27.511 2.751 27.511 2007 2 2.433 24.331 5.570 55.704
1995 2 2.309 23.088 5.060 50.599 3 1.337 13.369 6.907 69.073
3 1.512 15.117 6.572 65.715 1 3.045 30.448 3.045 30.448
1 2.770 27.700 2.770 27.700 2008 2 2.438 24.376 5.482 54.825
1996 2 2.384 23.835 5.154 51.535 3 1.309 13.091 6.792 67.916
3 1.315 13.149 6.468 64.684 4 1.031 10.310 7.823 78.226
4 1.149 11.489 7.617 76.173 1 3.021 30.212 3.021 30.212
1 3.073 30.733 3.073 30.733 2009 2 2.925 29.255 5.947 59.467
1997 2 2.453 24.527 5.526 55.260 3 1.347 13.470 7.294 72.937
3 1.198 11.981 6.724 67.241 1 3.038 30.378 3.038 30.378
4 1.107 11.075 7.832 78.316 2010 2 2.777 27.771 5.815 58.149
1 2.852 28.521 2.852 28.521 3 1.464 14.642 7.279 72.791
1998 2 2.718 27.184 5.571 55.705 1 2.790 27.900 2.790 27.900
3 1.325 13.245 6.895 68.951 2011 2 2.528 25.282 5.318 53.182
1 2.678 26.777 2.678 26.777 3 1.565 15.655 6.884 68.836
1999 2 2.510 25.099 5.188 51.876 4 1.030 10.299 7.914 79.135
3 1.385 13.855 6.573 65.731 1 48.753 27.085 48.753 27.085
4 1.170 11.697 7.743 77.428 2 43.365 24.092 92.118 51.176
1 2.554 25.543 2.554 25.543 3 22.636 12.575 114.753 63.752
2000 2 2.496 24.956 5.050 50.499 4 15.130 8.405 129.883 72.157
3 1.356 13.561 6.406 64.060 5 10.323 5.735 140.206 77.892
1 2.673 26.733 2.673 26.733 6 7.253 4.029 147.458 81.921
2001 2 2.491 24.914 5.165 51.646 7 6.010 3.339 153.468 85.260
3 1.304 13.045 6.469 64.691 1994-2011 8 4.675 2.597 158.143 87.857
4 1.093 10.925 7.562 75.616 9 3.469 1.927 161.613 89.785
1 2.876 28.757 2.876 28.757 10 3.016 1.676 164.629 91.461
2002 2 2.578 25.784 5.454 54.541 11 2.305 1.280 166.934 92.741
3 1.444 14.445 6.899 68.985 12 2.075 1.153 169.009 93.894
1 2.813 28.127 2.813 28.127 13 1.522 0.845 170.531 94.739
2003 2 2.610 26.097 5.422 54.225 14 1.373 0.763 171.904 95.502
3 1.596 15.964 7.019 70.188 15 1.255 0.697 173.158 96.199
1 2.864 28.643 2.864 28.643 16 1.084 0.602 174.243 96.802
2004 2 2.605 26.054 5.470 54.697
3 1.523 15.230 6.993 69.927
1 2.910 29.101 2.910 29.101
2005 2 2.651 26.505 5.561 55.606
3 1.514 15.141 7.075 70.747
Source: Authors' calculations.
For the entire period, the results uncovered by the annual analysis are confirmed: cumulatively, labor force contributes to the first component by 29.20%, followed by international reserves (22.25%) and the percentage of urban
population (11.45%). The variables with the lowest cumulative importance are inflation rate (0.31%), domestic credit (1.13%) and labour participation (3.63%).
Table 2. Contributions of variables to first factor (principal component) - annual 1994-2011
Year DC TE TO GDPC IE UP DT MS LBP LEI"
1994 0O02O 0.0263 0.1170 0,2049 0.0692 0.1632 0.0203 0.1531 0.0500 0.1336
1995 0.0143 0.0001 0.1319 0,2667 0.0131 0.1330 0.0133 0.2626 0.0034 0.1011
IMS O.OOS S 0.0071 0.1203 0.2454 0.0393 0.1372 0.0136 0.2444 0.0073 0.1257
1997 0.0069 0.1213 00604 01039 0.1920 0.1241 0.0017 0.0956 0.0514 0.2427
199B 0.0730 0.1631 0.OO97 0.0346 0.2663 0.0763 0.0016 0.0043 0.1002 0.2654
1999 0.1415 0.2017 0.0013 0.0056 0.2975 0.0050 0.0036 0.O235 0.1121 0.2O32
2000 0.1311 0.1430 0.0002 0.0120 0,2940 O.0O37 0.0445 0.0090 0.1396 0.2223
2001 0 0167 0.0593 0.0696 0.OS46 0.1933 0.1236 0.0000 0.1230 0.0377 0.2767
2002 0.0413 0.1412 0.0492 0.0409 0.2431 0.0654 0.0003 0.0715 0.03 34 0.2 B32
2003 0.0073 0.0543 0.1060 0.1119 0.1759 0.1047 0.0110 0.1663 0.0266 0.2354
2004 0.0013 0.0103 0.1305 0.1739 0.0370 0.146 S 0.0441 0,2453 0.0070 0.1523
2005 0 0011 0.0926 0.OS23 0.0762 0.1961 0.0954 0.0193 0.1521 0.0414 0.2430
2006 0.0113 0.1913 0.0376 0.0213 0.2717 0.0440 0.0013 0.0759 0.0661 0.2789
2007 00040 0.1996 0.0450 0.0376 0.2532 0.0516 0.0002 0.0936 0.0453 0.2700
2 DOE 0.0002 0.1675 0.0625 0.0445 0.2577 0.05 5 B 0.0015 0.1047 0.O312 0.2744
2009 0.0453 0.0063 0.1S21 0.1759 0.O6O3 0.1355 0.03 63 0,215B 0.0021 0.1195
2010 0.0529 0.0014 0.1596 C.1B54 0.0224 0.1409 0.1094 0.2430 0.0143 0.0706
2011 0.0012 0.0031 O.OS16 0.1563 0.1463 0.1333 0.O407 0.2107 0.0033 0.2175
Arera ge 0.O313 0.OBB7 0.OBO4 0,1101 0,1719 0.102B 0.O21B 0.1392 0.0443 0.2095
Source: Authors' calculations.
When we project countries on the plane defined by the first and second factor identified in the PCA, a number of results are noteworthy. First, China is individualized in all years, as it always seem to cluster separately from all the other emerging markets. Second, there is an agglomeration of countries around the intersection of the two factors that show small differences among them depending on one or the other of the two principal components. Third, some coun-
tries do not seem to belong to the main cluster of countries, as they depart from the main agglomeration, mainly depending on the second principal component (Malaysia, Bahrain, Kuwait, South Korea, Thailand, UAE, and India). For the overall period (see Figure 1) this clustering pattern is maintained, and we also observe that the second principal component is able to provide more differentiation between countries compared to the first principal component.
15
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Fig. 1. Projections of countries on factor 1 against factor 2 plane, 1994-2011
Source: Authors' calculations.
10
3. Conclusions and discussions. Our paper investigates the location potential of 41 emerging countries from the perspective of MNEs' decision to invest abroad, by analyzing some of their attributes that may be considered location attractiveness factors between 1994 and 2011.
The empirical approach considered attributes that are prox-ied for market size and its potential, country risk, infrastructure and ease of access to information, labour market features and world economic integration level of host countries. The methodology employed was Principle Compo-
5
0
nents Analysis, with the purpose of identifying of a smaller number of location attributes that are relevant for MNEs' decision to delocalize abroad.
The empirical analysis revealed that a number of only four variables had the most important contributions to the principal components, regardless of the year for which the analysis is undertaken: GDP per capita, international reserves, mobile subscriptions and labour force. By far, labour force is the variable that contributes the most to the first factor and its contribution becomes more important as we depart from 1994. At the same time, GDP per capita became less important in recent years.
Further research on this topic is intended, as follows. First, a larger panel of emerging markets is to be included in the analysis, differentiated according to their development level and volume of inward FDI. Second, the number of variables employed in the analysis needs to be enlarged, as to reflect in a more comprehensive and accurate manner the characteristics of emerging host economies. Third, these attributes need to be connected with the existing level of foreign direct investments and other investigation methods to be used in order to reveal the connection between those attributes and the multinational enterprises' decision to invest abroad.
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Received: 30/01/2018 1st Revision: 10/05/2018 Accepted: 01/06/18
Author's declaration on the sources of funding of research presented in the scientific article or of the preparation of the scientific article: budget of university's scientific project
ЛОКАЛЬН1 АТРИБУТИ КРА1Н, ЩО РОЗВИВАЮТЬСЯ: АНАЛ1З ВИКОРИСТАННЯ ОСНОВНИХ КОМПОНЕНТ
Дослiджуються атрибути розташування великоУ вибiрки кран з економiкою, що розвиваеться, в аспект/' прямих iноземних iнвес-тицш та присутностi мiжнародних компанiй за кордоном. Використовуються клька макроекономiчних змiнних, як е гпотезами, щоб люструвати, як саме вiдповiднi атрибути мiсцеположення включати в аналiз основних компонент для рiшень мiжнародних компан/'й, щоб виявити найбльш релевантнi географiчнi характеристики або комбiнацiю таких атрибутiв, як впливають на рiшення багато-наЦональних компанiй твестувати за кордон. Виявлено, що основт компоненти складаються лише iз чотирьох змi нних: ВВП на душу населення, мiжнароднi резерви, тдписки на мобiльнi телефони та робочу силу. Робоча сила - це зм/'нна, яка найбльшою мiрою зале-жить в/'д першого фактора, i УУ внесок зростае, оскльки початковою е позиця 1994 року. Разом iз тим, ВВП на душу населення остан-нм часом став менш важливим.
Ключовi слова. Прямi iноземнi твестицУУ, перехiдна економ/'ка, аналiз основних компонент.
Л. Беласку, д-р экон. наук, проф. Университет имени Лучиана Блага, Сибиу, Румыния, О. Хоробет, д-р экон. наук, проф., О. Поповичи, д-р экон. наук, доц.
Бухарестский экономический университет, Бухарест, Румыния
ЛОКАЛЬНЫЕ АТРИБУТЫ РАЗВИВАЮЩИХСЯ СТРАН: АНАЛИЗ ИСПОЛЬЗОВАНИЯ ОСНОВНЫХ КОМПОНЕНТ
Исследуются атрибуты расположения большой выборки стран с развивающейся экономикой с точки зрения прямых иностранных инвестиций и присутствия международных компаний за рубежом. Используется несколько макроэкономических переменных, которые являются гипотезами, для иллюстрации того, как именно соответствующие атрибуты местоположения включать в анализ основных компонент для решений международных компаний, чтобы выявить наиболее релевантные географические характеристики или комбинацию таких атрибутов, которые влияют на решение многонациональных компаний инвестировать за границу. Установлено, что основные компоненты состоят лишь из четырех переменных: ВВП на душу населения, международные резервы, подписки на мобильные телефоны, рабочая сила. Рабочая сила - это переменная, которая в наибольшей степени зависит от первого фактора, и ее вклад растет, поскольку исходной является позиция 1994 года. В то же время, ВВП на душу населения в последнее время стал менее важным.
Ключевые слова. Прямые иностранные инвестиции, переходная экономика, анализ основных компонент.
Bulletin of Taras Shevchenko National University of Kyiv. Economics, 2018; 3(198): 34-39 УДК 330
JEL classification: F02, F1, F2, F6
DOI: https://doi.org/10.17721/1728-2667.2018/198-3/3
S. Burnete, PhD, Professor Lucian Blaga University, Sibiu, Romania
TRANS-ATALANTIC INTEGRATION: A CRUCIAL PACE TOWARD A GLOBALIZED WORLD
In this paper we deal with the much "touted and taunted" upcoming transatlantic economic integration. We scrutinize the issue through the prism of both economic theory and historical development, in an attempt to contend that economic integration is no less lucrative on a transcontinental level than it used to be on a regional level. We use theoretical and historical arguments to emphasize the necessity and opportunity of a trade and investment agreement between the United States and the European Union, which is likely to turn the Atlantic into a redoubtable economic pole. We show that Europe and America are full readiness to enter into this paramount agreement.
Key words. trans-Atlantic integration, international trade, regional blocs, investment, partnership.
Introduction. Contrary to popular belief, globalization did not speed up but slowed down the advance toward freer international trade. Mobility of capital across national borders and the attendant increase in the power of multinational companies are acting rather as disincentives to nations' willingness to further open their markets to international trade. Moreover, conventional trade in merchandise and services is increasingly being blamed for many evils of today's world such as environment degradation, labor standards infringement, domestic firms' exposure to unfair competition from abroad etc. As a consequence, regional and bilateral agreements have been proliferating lately, not as surrogates but as interim solutions to the halting progress in multilateral negotiations aimed at fully liberalizing international trade. In this context, trans-Atlantic integration should be perceived as a natural and necessary step toward a truly globalized world. The mere fact that two huge trade blocks are involved therein makes it appear as one of the most prominent challenges the world has been facing in its entire history.
Trans-Atlantic integration is by no means a sui generis phenomenon even though it involves the union of two tectonic plates, separated by an ocean. Yet the geographic gap is ever less a barrier to trade and investment flows between the two. In fact, integration has been steadily advancing on both sides of the Atlantic after World War 2: the European Union (EU) is more than sixty years old, while North American Free Trade Agreement (NAFTA) has a quarter of a century of existence. In all this time, the bonds between the two blocs have kept tightening. Now they are poised for a big step forward: the Trans-Atlantic Trade and Investment Partnership (TTIP).
1. Economic integration: a few theoretical and historical considerations
From among the founders of economic integration theory, perhaps Jacob Viner and Bela Balassa are most noteworthy. Viner's "Customs Union Issue" as well as Balassa's "Theory of Economic Integration" was a solid bedrock on which the first integration organizations were built after World War II. The former is preoccupied by (even concerned about) the conditions under which customs unions are compatible with basic principles of international trade such as most-favored nation treatment [8], which lead him to the conclusion that free trade areas are a second best optimum. Balassa follows the same logic, defining economic integration as "abolition of discrimination within an area" [13]. Clearly, both scholars are focused upon the discriminatory nature of regional blocs: customs unions eliminate discrimination within the trade among member-countries on the one hand, yet they discriminate against outside nations on the other hand. Lipsey R. [19] tackles "empirical evidence relating to the gains from European Economic Union", asking to what extent customs uniontype arrangements are welfare-improving. The 1990s bring about a slight change of approach in that scholars (e.g. Panagariya A. and Findlay R. [25]) begin to take into consideration an important dimension of the economic integration issue, which is the endogeneity of trade policies. The idea is also emphasized by Krugman P. [16] according to whom, "in a fundamental sense, the issue of the desirability of free trade areas is a question of political economy rather than of economics proper. In practice, the reputed Nobel prize-winner economist contends, the move toward free trade zones will continue because the benefits of freer trade within regions largely outweigh the discrimination against third parties (aka trade diversion) downside. "The real objection is a political judgment: fear
© Burnete S., 2018
