Changing economic conditions of mediterranean region

Abstract

At recent time the Mediterranean region became more important in the world economy, as important centre for the international trade, international economic development, therefore, the economies of surrounding areas have got wider side relations either among themselves in this region or with other economies in different farer regions. In this region the EU member states have wide side economic and cultural connections and relations with developing world and some other developed regions. Study analyses main EU-member states, some developing and developed economies in Mediterranean region. Study analyse 21 countries, as Germany, France, Italy, Austria, Hungary, Spain, Portugal, Greece, UK, Israel, Algeria, Morocco, Tunisia, Egypt, Turkey, Iran, Saudi Arabia, Nigeria, China, India, South-Africa. Study analyses economic conditions of economies in main fields of GDP growth, FDI net inflows, Gross capital formation, High-technology exports, Investment in agriculture, Share of gross fixed capital formation of agricultural sector in Total one, Agriculture in share of GDP. Based on SPSS statistical system seven economic variances are classified into three components.

The study provided proof in cases of 21 selected countries in 2010-2018 that the FDI net inflow has such influences on the investment in value added agriculture and share of gross fixed capital formation of agricultural sector in total gross fixed capital formation. But this correlation of the FDI net inflow is not strong with these other two economic variances. Solution is to strengthen cooperation to stimulate FDI net inflow among countries to develop investment in agricultural sector and increase high-technology exports.

Keywords: Agricultural sector, Cooperation, FDI inflow, Germany, Investment, Statistical analyse

JEL code: E00, E01, E22, F00, F02, F20, O40, Q10

Introduction

At recent time the Mediterranean region became more important in the world economy, as important centre for the international trade, international economic development, therefore, the economies of surrounding areas have got wider side relations either among themselves in this region or with other economies in different farer regions. In this region the EU member states have wide side economic and cultural connections and relations with developing world and some other developed regions. Therefore, the study analyses some main EU-member states and some developing and developed economies concerning the Mediterranean region. The analyse of the study includes 21 countries, which are as follows: Germany, France, Italy, Austria, Hungary, Spain, Portugal, Greece, as EU-member states, UK (United Kingdom) former EU-member state, in Mediterranean region Israel developed economy, and developing economies, as Algeria Morocco, Tunisia, Egypt, Turkey; other crude oil exporting economies as Iran, Saudi Arabia, Nigeria, most of them in Middle East and North-Africa; other important economies of the world economy as China, India, South-Africa. All of these economies from three continents can meet together in economic relations across Mediterranean region.

Generally, the analyse only directly for economies of Mediterranean region cannot be enough to overview the economic importance of this region. Therefore, the analyse should be extending for those economies, which have strong or important connections with this region. Also, this region has basically strategic geographical importance emphasized by Suez channel, international road of crude oil transport. At recent time China has important economic activities to build new trading roads on the sea-trade and train line.

The study analyses economic conditions of the economies concerning this region in main fields of GDP growth, FDI (Foreign direct investment, net inflows), Gross capital formation (% of GDP), High-technology exports, Investment in value added agriculture, Share of gross fixed capital formation of agricultural sector in Total gross fixed capital formation, Agriculture, forestry, and fishing, value added in % of GDP. The economic development is mostly measured by the GDP growth, which depends on the FDI and gross capital formation based on the national financing bases. The high-technology plays role in strengthening economies, from where its export goes. The agricultural sector is strategic one for food-supply at the national and international food demand and providing jobs for human resources in countries, which are either exporting or importing economies in fields of food and agricultural products.

Some experts emphasized the exports and gross value added products and they declared that the most significant contribution to the formation of gross value added on average across the countries of Europe is provided by trade (including food products and food), transport and storage, hotels and catering, in second place – financial and insurance activities, transactions with real estate, professional, scientific, technical, administrative activities in the third place is industry. They also emphasized for example that the Russian Federation, in turn, has the lowest share of trade, public administration, defence, social security, education, health and social services in gross added value compared to other countries. Comparison with some countries shows that Russia is at a stage of its development, which requires significant state participation in the formation of the new economy, its structural restructuring Guzel et al, 2020). The example of Russia also emphasizes the completely economic growth to obtain competitiveness in the international trade.

The other author Lentner (2018) described by the example of Hungary the importance of the bank-sector, as he declared that his paper describes and analyses the regulatory dynamics of the role of the central bank in Hungary through the changes in the legislative framework, with special regard to the central bank’s monetary policies and the government’s economic policies. The processes before the economic transition, especially the 1987 set-up of the two-tier banking system, laid the foundation for a successful and effective central bank. This paper highlights major changes in key pieces of legislation between 1987 and 2013. As a conclusion, the National Bank of Hungary has been successfully integrated into the European System of Central Banks, and its history may serve as a blueprint for countries still in the accession period (Lentner, 2018). Some authors emphasized the importance of competitiveness in cases of economic conditions of regions in Central-East European economies (Lentner et al, 2018). The authors emphasized the importance of the consequent fixed bank and financial activities for strengthening the performance of economies to create the strong competitiveness for their economies. Other experts emphasized the risk management, which is one of the most important internal process, not only in large companies but also in small and medium-sized enterprises (SMEs). To identify the source of risk can be crucial in all companies. The primary objective of this study is to analyse and compare the economic and financial risk sources in SMEs of the V4 (Visegrád Group: Czech Republic, Hungary, Poland and Slovakia) and Serbia, in the context of the business environment of the countries analysed (Oláh et al, 2019) (see in detailed in Szabó – Zsarnóczai, 2004). The risk management concerns also one of the most important financial issues, which needs for considerable attention from side of either large and small-medium scale companies or national economic policies.    

Some other authors emphasized some examples for the negative prosses that the negative trends were observed for output and the ratio of direct payments to the output for the Baltic States. This implies these countries became more similar in regards to these values. As regards the output, the trend coefficient is close to zero. This suggests Latvia and Estonia approached the scale of Lithuanian agricultural output to a certain extent over 2008-2017. Also, they declared that the convergence in relative direct payment rate was observed as Lithuania approached the other Baltic States (note that slightly negative stochastic growth rates are observed for Latvia and Estonia). As regards the absolute value of the direct payments, the convergence did not occur as the positive trend was observed for the corresponding correlations. This suggests that dynamics in the amount of the direct payments do not correspond to the dynamics in the agricultural output to a full extent thus creating the aforementioned misalignments in the rates of growth (Vaida et al, 2019). From point of view of their opinion that the lack of the dynamics in the amount of the direct payments can create the unfavourable economic conditions in field of agriculture. Other authors focus on the importance of the institutional investors to strengthen the economic prosperity of their owned firms or corporations and their preferences and also, at regional level (more detailed in Sadaf et al 2019 and Vekic et al, 2020).   

Additionally, to the financial background, the economic development needs for developing the human resources, as some experts declared that the human resources should continuously be skilled to be relevant for using the highly developed technological development. Some authors emphasized the importance and development of human labour resource at the international level and they emphasized the collaboration among universities and enterprises for realising this strategic development of employees (Berková et al, 2019), (Garrido et al, 2017), (Griffin -Coelhoso, 2019). In order that any country can develop its performance and high technology exports, the human resources should be skilled and relevant to apply highly developed technologies in the production processes.

Also, in order that countries can develop their performances and even agricultural sector, they have to pay attention for the environmental conservation, as experts declared that new forms of preventive environmental strategies and especially Green Marketing are being introduced helping to solve environmental problems and environmental motivation of producers. Many producers face great attention of the public regarding their approach to the environment. (Rusko, 2015). Hungary, this small and open economy with limited natural resources, tries to build its future on creativity and innovation. At the same time, the country has declared in its basic law a categorical prohibition on the application of genetically modified organisms (Popp et al, 2018).

At the international wide-side level the Mediterranean region concerning also some other main economies can provide an important example for developing trends how they can realise their economic growth concerning the GDP growth, gross capital formation, role of FDI and strengthening their high-technology exports in order that they could successfully contribute to development of the world economy. The study would like to discover some main economic correlations among some economic features or economic variances of these economies analysed (earlier Zsarnóczai, 2018) (Gál et al, 2016).

Material and Method

The analyse for the 21 countries based on different economic features needs a complete statistical overview, therefore, the SPSS (Statistical program for social sciences) can be mostly adequate. The SPSS statistical program in detailed includes factor analyses, descriptive statistics, principle components, dimension reduction, correlation matrix, rotated solution, factor score, graphs, cluster membership and dendrogram/ (IDRESC, 2020; Keith McCormick eat al, 2017).

The factor analyse means that the economic variances as economic features should be used relevant to 21 selected countries researched in this study. Because of 21 countries participate in this study, the SPSS needs for them to use seven economic variances, namely

AgrVA1 =
Agriculture, forestry, and fishing, value added (% of GDP) in 2018

GrossCapF4 =
Gross capital formation (% of GDP) average level, 2010-2018

InvAgr6 =
Investment in Value added agriculture between 2010-2016 at current price level of 2010 in percent in million US dollar

FDInetin2 =
Foreign direct investment, net inflows (BoP, current US$)2010-2018, 2010 = 100

ShGFCapF7=
Share of gross fixed capital formation of agricultural sector in Total gross fixed capital formation in million current US dollar by the end of 2016

GDPgrowth3 =
GDP growth (annual %), average level, 2010-2018

HighTExp5 =
High-technology exports (% of manufactured exports) average level, 2010-2018.

Based on the SPSS statistical system these seven economic variances are classified into three components, of which the first component including three variances is lying at the principle line “X” of the coordinate system and the other two components are lying at the line “Y”, which are as follows:

Line “X” = Component-1: AgrVA1, GrossCapF4, InvAgr6

Line “Y” = Component-2: FDInetin2, ShGFCapF7

Line “Y” = Component-3: GDPgrowth3, HighTExp5

The factor analyses based on the dimension reduction means the analysing correlations among different components including economic variances. When this analyse will be demonstrated in the coordinate system, this becomes within graphs. Because of the countries connect with their economic variances included in principle components at principle lines “X” and “Y” in coordinate system, the differences among countries can be followed in four classes of all countries included in the research in the space of the coordinate system. Descriptive statistics include coefficient and significance levels. These descriptive statistics provide values of the economic variances, which are needed for determining the correlations among the economic variances. The Table-1 provides the main statistical data coming from the World Bank and FAO basic statistics originating from the national statistical offices, and the Table-2 provides Correlation Matrix concerning the values of the economic variances for giving the correlations among economic variances, which can also be given in percent. For example, AgrVA1 has correlation with InvAgr6 by value as 0,538 or 53,8%. The correlations have different levels, as very strong correlations between 0,800 and 1,000; strong correlations between 0,600 and 0,800, middle correlations between 0,500 and 0,600; but weak correlations between 0,400 and 0,500 values, as 40% and 50%. The values of correlations are not important under level of 0,400 (40%).

The correlations show how much compare is among economic variances, in this case the value of the correlation should be closed to 1,000, as 100% in order to be most comparably between each-others. While the significance in the down part of the Table-2 should be closed to “0” value in order to be not considerable difference among variances. Mostly two kinds of measures for comparing economic variances have similar function for comparing: large compare or less difference. Therefore, the correlation among variances is only enough, because the values of significance provide mostly the same results.

The Table-3 provides rotated component matrix, as three components including economic variances, in which the component-1 is lying at principle line “X”, the other variances of components-2 and 3 lying at principle line “Y” of coordinate system as by the other word “score”,

In the Table-3 each economic variance has the biggest value at its line, which shows its component, in which it is included. For example, AgrVA1 belongs to component-1, because its value is 0,739, as the biggest one at its owned line.

The Table-4 shows the cluster membership for calculating the clustering the countries into different numbers of clusters, which include different number of countries in case of each number-cluster. This means that the 21 countries can be clustered into even 21 clusters or each country can be clustered into only one cluster. In this study the optimal number of cluster is five one, which can be seen in the column of 5 Clusters of this table. Also, this can be followed in the Figure-3, namely, “Dendrogram using Ward Linkage. Rescaled Distance Cluster Combine”:

Cluster-1: China, Hungary, Iran, Algeria

Cluster-2: France

Cluster-3:
Germany, Italy, Saudi Arabia, Spain, UK, Austria, Israel, Portugal, South-Africa

Cluster-4: India, Turkey, Nigeria, Morocco, Egypt, Tunisia

Cluster-5: Greece

The SPSS system helps to understand and analyse easily the correlations among economic variances or features of researched countries and to get overview how their similar are closed or fare among each-others. The difference of the countries based on this system is important to create ordering for them to determine the difference of qualified and developed levels of different countries. This difference among countries is important for the international cooperation among them in cases of high-technical and financial aids or FDI inflow-outflow from one country to the other one.

Results and Discussion

In this chapter the study analyses the economic features of the researched countries based on the statistical data coming from World Bank and FAO (World Development Indicator, 2020, World Bank, 2020) summarised in Table-1. In this table the main data concern the value added of agriculture in share of GDP, Foreign Direct Investment (FDI) net inflow, GDP growth, Gross capital formation, High-technology exports, Investment in value added agriculture, Share of gross fixed capital formation of agricultural sector in Total in percent between 2010-2018.

AgrVA1 =
Agriculture, forestry, and fishing, value added (% of GDP) in 2018

GrossCapF4 =
Gross capital formation (% of GDP) average level, 2010-2018

InvAgr6 =
Investment in Value added agriculture between 2010-2016 at current price level of 2010 in percent in million US dollar

FDInetin2 =
Foreign direct investment, net inflows (BoP, current US$)2010-2018, 2010 = 100

ShGFCapF7=
Share of gross fixed capital formation of agricultural sector in Total gross fixed capital formation in million current US dollar by the end of 2016

GDPgrowth3 =
GDP growth (annual %), average level, 2010-2018

HighTExp5 =
High-technology exports (% of manufactured exports) average level, 2010-2018

Line “X” = Component-1: AgrVA1, GrossCapF4, InvAgr6

Line “Y” = Component-2: FDInetin2, ShGFCapF7

Line “Y” = Component-3: GDPgrowth3, HighTExp5

Cluster-1: China, Hungary, Iran, Algeria

Cluster-2: France

Cluster-3:
Germany, Italy, Saudi Arabia, Spain, UK, Austria, Israel, Portugal, South-Africa

Cluster-4: India, Turkey, Nigeria, Morocco, Egypt, Tunisia

Cluster-5: Greece

Note:

Agriculture, forestry, and fishing, value added (% of GDP) 2010-2018, 2010= 100

Agriculture corresponds to ISIC divisions 1-5 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3 or 4. At basic price. World Bank national accounts data, and OECD National Accounts data files. Economic Policy & Debt: National accounts: Shares of GDP & other.

Foreign direct investment, net inflows (BoP, current US$)

Foreign direct investment refers to direct investment equity flows in the reporting economy. It is the sum of equity capital, reinvestment of earnings, and other capital. Direct investment is a category of cross-border investment associated with a resident in one economy having control or a significant degree of influence on the management of an enterprise that is resident in another economy. Ownership of 10 percent or more of the ordinary shares of voting stock is the criterion for determining the existence of a direct investment relationship. Data are in current U.S. dollars. International Monetary Fund, Balance of Payments database, supplemented by data from the United Nations Conference on Trade and Development and official national sources. Economic Policy & Debt: Balance of payments: Capital & financial account.

GDP growth (annual %)

Annual percentage growth rate of GDP at market prices based on constant local currency. Aggregates are based on constant 2010 U.S. dollars. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. World Bank national accounts data, and OECD National Accounts data files. Economic Policy & Debt: National accounts: Growth rates.

Gross capital formation (% of GDP)

Gross capital formation (formerly gross domestic investment) consists of outlays on additions to the fixed assets of the economy plus net changes in the level of inventories. Fixed assets include land improvements (fences, ditches, drains, and so on); plant, machinery, and equipment purchase; and the construction of roads, railways, and the like, including schools, offices, hospitals, private residential dwellings, and commercial and industrial buildings. Inventories are stocks of goods held by firms to meet temporary or unexpected fluctuations in production or sales, and “work in progress.” According to the 1993 SNA, net acquisitions of valuables are also considered capital formation. World Bank national accounts data, and OECD National Accounts data files. Economic Policy & Debt: National accounts: Shares of GDP & other.

High-technology exports (% of manufactured exports)

High-technology exports are products with high R&D intensity, such as in aerospace, computers, pharmaceuticals, scientific instruments, and electrical machinery. United Nations, Comtrade database through the WITS platform. Infrastructure: Technology. The method for determining high-technology exports was developed by the Organisation for Economic Co-operation and Development in collaboration with Eurostat. It takes a “product approach” (rather than a “sectoral approach”) based on R&D intensity (expenditure divided by total sales) for groups of products from Germany, Italy, Japan, the Netherlands, Sweden, and the United States.

According to the up-half part of the Table-2 the correlations of the economic variances are very strong if the values of the correlations are between 0,800 and 1,000; strong between 0,600 and 0,800, middle strong between 0,500 and 0,600. The correlations are weak between 0,400 and 0,500, but if the values under 0,500, in this case the values are not important for the analyses based on the SPSS (Statistical program for social sciences). When the value of the correlations among the economic variances is 1,000 – by the other word 100% – or closed to 1,000, which means that the variances are very considerably or wholly similarly to each other. By the other words, there is no difference among them, which means the difference is zero, as wholly significance. This means that the similarity or difference can be demonstrated by correlation or significance in the same time. In case of up-half part ofTable-2 the correlation values of the economic variances are 1,000 in the whole diagonal line, because one variance is equal with its owned value. Also, in this session of the Table-2 the values of the correlations in up-right from diagonal line are the same to one of the down-left of this table (see Table-1-3; World Development Indicator, 2020).

Also, significance values of the economic variances are the same in up-right part of the down session of the Table-2 from diagonal line to their one in left-down part. According to the down-half part of the Table-2 the values of significance among the economic variances should be less 0,050 in order that the significance of the economic variances is important for these statistical analyses. But in the other cases the significance of the economic variances is not important. The best value of the significance is in that case, when this is zero or very closed to zero, which means that the difference among economic variances is not important, therefore, these economic variances are very similar to each-others.

There are strong correlations between GrossCapF4 (Gross capital formation in % of GDP) and InvAgr6 (Investment in Value added agriculture) economic variances by value of 0,724 (72,4%). In this study the most important and strongest correlations are between these two variances, because the gross capital formation calculated in share of GDP can make considerable positive growing influences on the increase of the investments in value added agriculture. Naturally the increase of the gross capital formation includes the growing rate in field of investments in agricultural sector, mostly the mechanization process for crop production and animal husbandry. This economic process and growing trend are valid for the Mediterranean region and some parts of the world economy connecting to his region.

There are middle strong correlations by value as 0,538 between AgrVA1 (Agriculture value added in % of GDP) and InvAgr6. When the investment in value added agriculture increases, this can lead to increase contribution of this sector to the GDP, namely the agriculture value added in % of GDP increases. Naturally in the highly developed economies the increase of the investment in this sector will not always accompany with the increase of the agriculture value added in % of GDP, because the other economic sectors can realise more highly developed investment with considerable mechanization growth. Therefore, the industrial sectors and even the advanced service sectors can increase their shares in the GDP more than the share of the agriculture in value added in share of GDP in spite of its development. Therefore, this can be reason of the middle strong correlations between AgrVA1 and InvAgr6.

There are weak correlations between AgrVA1and ShGFCapF7 (Share of gross fixed capital formation of agricultural sector in Total gross fixed capital formation), but these are mostly closed to the level of middle strong correlations by the value as 0,485 (48,5%). If the share of gross fixed capital formation of agricultural sector increases in the total gross fixed capital formation at the national economic level, this does not mean that in any case the agriculture value added can increase in percent of GDP. Because its increasing share in percent of GDP depends on the increasing output, production efficiency and input efficiency of gross fixed capital formation of the agricultural sector, not only on the quantitative increase of gross fixed capital formation of this sector. From point of view of this increasing share of agriculture value added in GDP (AgrVA1) the efficient use of the agricultural machines has considerable influence on increase of agriculture in percent of GDP. Also, the efficiency of other economic sectors can make share of agriculture be decreasing its share in GDP. Naturally the share of gross fixed capital formation of agricultural sector in total gross fixed capital formation (ShGFCapF7) increases can contribute to increase share of agricultural sector in percent of GDP, but this depends on that how much other economic sector are efficient, more than one of the agricultural sector or less. Also, the economic policy of national governments can provide priority for agricultural sector by increasing import of advanced machines from highly developed economies to developing economies, which can change the sector structure of GDP of countries for interest of agricultural sector.

There are weak correlations between FDInetin2 (Foreign direct investment, net inflows) and ShGFCapF7. Actually, many nations and national governments compete in contest with each other for more investment provided by foreign investors within FDI in order to realise more yield of the agricultural sector and increase competitiveness of the national producers and agricultural production at national and international markets. But this increasing trend of the FDI cannot result more share of gross fixed capital formation of agricultural sector in total gross fixed capital formation (ShGFCapF7) in many cases, because generally the FDI net inflow is not so considerable into the agricultural sector comparably into the other economic sectors. The FDI mostly preferred investment in heavy industry, manufacturing industry for the basic products even coming from the agricultural sector, and service sectors, as banking sectors and large-scale retailed network, as stores and warehouses. Mostly the FDI is not interested to invest in agricultural production, as basic production, therefore the FDI net inflow cannot increase share of this agricultural sector in the total fixed capital formation. Therefore, the correlation is weak between two economic variances, namely FDInetin2 and ShGFCapF7.

There are also weak correlations between FDInetin2 and InvAgr6 (Investment in Value added agriculture) by value minus 0,410, which means that the correlations are contradict, by the other words the FDInetin2 is in inverse ratio to the changes of the InvAgr6. This means that when the FDInetin2 increases or less decreases and the InvAgr6 decreases or less increases. Also, when the FDInetin2 decreases or less increases and the InvAgr6 increases or less decreases. But because the correlation between these two variances is weak, therefore, these contradict changes in cases of two variances are not going on in every case, but sometimes. Generally, the FDI net inflow, as similarly the above mentioned, is not interested in increasing investment into the agricultural sector, therefore, if the FDI net inflow increases at national economic level of a given country, mostly the investment in value added agriculture decreases, because the FDI net inflow goes into the other sectors, or if the FDI net inflow decreases at national economic level, the investment in value added agriculture increases, because the domestic-national investors have more share in agricultural sectors comparably to share of the FDI. In essence the investment in value added agriculture depends on the national-domestic producers or investors and not FDI net inflow.

According to the Figure-1 in the first session of the coordinate system there are India, Turkey, Nigeria, Morocco, Egypt, Tunisia. In this session at line “X” the component-1 is consisting of three economic variances, namely AgrVA1 (Agriculture value added in % of GDP), GrossCapF4 (Gross capital formation in % of GDP) and InvAgr6 (Investment in Value added agriculture). In the same session at line “Y” composes the component-2 consisting of FDInetin2 (Foreign direct investment, net inflows) and ShGFCapF7 (Share of gross fixed capital formation of agricultural sector in Total gross fixed capital formation).

Generally, when in countries of this session the AgrVA1 (Agriculture value added in % of GDP), GrossCapF4 (Gross capital formation in % of GDP) and InvAgr6 (Investment in Value added agriculture) increases and at line “Y” composes the component-2 consisting of FDInetin2 (Foreign direct investment, net inflows) and ShGFCapF7 (Share of gross fixed capital formation of agricultural sector in total gross fixed capital formation) also are increasing. In these countries the agriculture value added has considerable share in the GDP, most of them has agricultural share more than 10%, and gross capital formation in GDP was also considerable between 15% and 35%, while the investment in value added-agriculture is at highly level. In Tunisia this index by 38,3% increased, which was the third at highly level in all of EU-28 after China by 66% and Algeria by 43%. This large share in field of the investment in agriculture was resulted by increasing gross capital formation in percent of GDP and increasing trend of the agricultural value added. While these economic variances increase in these countries, at line “Y” also other two economic variances increased namely FDI net inflow accompanying with increasing share of the agricultural gross fixed capital formation in the total one.

This means that the agricultural sector has a considerable role in performance of this country -group in GDP, investment and total gross fixed capital formation by agricultural production and capital formation. The FDI net inflow increased by six and half times more as 655% in Greece and increase by 192% in Morocco after Italy by 299% and Israel by 200%. Naturally in those mentioned countries, where the FDI net inflow was at the highest level, the foreign investment did not flow into the agriculture, but into the light industry and bank sector and little into mining sector. (Table-1; World Development Indicator, 2020).

In the second session of the coordinate system there are Greece, Italy and Israel, where the FDI net inflow was at highest level in the selected 21 countries, and AgrVA1, GrossCapF4 and InvAgr6 only moderately increased, even in the field of gross capital formation in percent of GDP. This process emphasized that the role and importance of service sector were into destination of FDI.

In the third session of the coordinate system there are China, Hungary, Iran, Algeria and Saudi Arabia, where the agricultural sector has moderate share in GDP. The gross capital formation in GDP (GrossCapF4) has mostly been more than the average level of the 21 selected countries, namely 46% in China, 45% in Algeria and 37% in Iran, while the average level was 25,5% for the period of 2010 and 2018. The investment in value added agriculture was considerable, because in China this has been at the highest level by 66%, in Algeria by 43% and by 31,6% in Iran. In spite that the gross capital formation and investment in agriculture have considerable been successful for the same period, the FDI net inflow sharply decreased in these countries, namely by decreasing 215% in Hungary, 85,5% in Saudi Arabi and 35% in Algeria, but only in cases of China and Iran this was little decrease by 17% in China and 5% in Iran. The FDI outflow was considerable from Hungary, and in cases of the crude-oil countries the FDI outflow was strong to invest mostly into the manufacturing industry and transporting of crude-oil out of oil-exporting countries. The transnational corporations want to realise these investments in the owned countries, because these investments would be closed to final consumers of the manufactured products from crude oil and to strengthen the employment level in the highly developed economies. Additionally, to the decrease of the FDI net inflow, the agricultural gross fixed capital formation in total one also decreased to the level of 1,45% in China, 0,29% in Algeria and 1% in Saudi Arabia, while the average was at level of 3,83% of 21 selected countries.

In the fourth session of the coordinate system there are Germany, Spain, UK, Austria, Portugal, South-Africa and France. In these countries of this session generally the economic variances of both of components have declining trends or less or little increase. In the highly developed economies, the share of the agriculture value added in percent of GDP was 0,63% in UK (United Kingdom) and 0,8% in Germany to 1,2% in Austria and 1,6% in France.  In other countries this share was 2% in Portugal and South-Africa and 2,8% in Spain. In case of the gross capital formation in percent of GDP this share was moderate in all of this country-group, because its share in GDP was less than the average level of the 21 countries, namely 25,5%. The other developing economies have reached better results in this field, for example China, Iran, Algeria, Turkey, India and Morocco. The investment in value added agriculture has considerably decreased in some countries of this session, for example this decreased by 3% in France, by 5% in Germany, 1,2% in Portugal, and the other one of this group little increased by 8% in Austria, by 9,8% in UK, by 10,4% in Spain, but in South Africa this increased little by 0,4%. In cases of Germany and France this decline means that the other economic sectors more developed by more investment than in case of agriculture.

Generally, the FDI net inflow has moderately increased in some economies, for example in Germany by 22,4%, in France by 54%, in South -Africa by 48% and in Spain by 34%. But in other countries the decrease was 82% in Austria, 48% in UK and 28% in Portugal. In these countries the decrease of the FDI net inflow shows that the economic growth decreased and the FDI of international corporations was looked for other countries.

It can be summarised that the agricultural sector has less importance in the selected 21 countries, which can be proofed by the less agriculture value added in share of GDP, by average result 6,1%, investment in value added agriculture increased 15,5% and share of gross fixed capital formation of agricultural sector in total gross fixed capital formation increased by 3,83%. While the gross capital formation in percent of GDP has increased by 25,5% averagely in selected 21 countries, which this last one does not connect directly with the agricultural sector. The other economic results based on the agricultural sector decreased in the researched period. The FDI net inflow was only strong in direction to the less favourable economies, for example Greece, Italy, Israel and Morocco.

According to the Figure-2 at line “Y” the component-3 includes GDPgrowth3 (GDP growth) and HighTExp5 (High-technology exports in percent of manufactured exports) in the first session of the coordinate system there are China, Hungary and Iran. In these countries the GDP and the high-technology exports in percent of manufactured exports have averagely increased for the period of 2010 and 2018, while the economic variances of component-1 at line ”X” – as these were mentioned before in Figure-1 – the agricultural value added, gross capital formation and investment in value added agriculture also increased. This means that the considerable GDP growth could ensure for the high-technology exports to increase considerably in share of manufactured export, as value added product-export, namely by 31,5% in China, by 29% in Iran and by 21,5% in Hungary in this period.

Naturally the GDP growth could increase first because of increase of the manufactured industry and increase of agriculture, therefore these countries could increase their high-technology export. The average increasing rate of the high-technology exports in percent of manufactured exports in three countries has mostly been two and three times more than the average level of selected 21 countries for the same period. From this point of view of GDP growth, the high-tech industrial branches were developed as a qualified development process and not only simply quantitively development.

In the second session of the coordinate system at line “Y” there are Greece, Italy, Israel, Germany, UK and Austria, in the most countries of which the high-technology exports in manufactured exports were considerable comparably to the average level of the selected countries, but some of them have less share of the high-technology exports than in the cases of countries first quarter of the coordinate system, for example 8% in Italy, 13% in Austria and 16,4% in Germany. Also, the lower level of the GDP growth could contribute to the less rate of the high-technology export in manufactured exports, for example In Germany 2,8%, in Austria 2,1% in field of GDP growth. In spite that in UK the GDP growth was very less level by 1,7%, but this country could achieve a considerable share of the high-technology exports by 23%. Also, Germany could implement 2,8% GDP growth. This means that this two countries, UK and Germany could have realised an ambition economic growth for developing high-technology exports in inverse ratio to the low level of GDP growth in period of 2010 and 2018. Israel had little less GDP growth increase than in Iran and Hungary but same share of the high-technology, like in case of Hungary by 21,5%. In cases of Greece, Italy and Israel the FDI net inflow was at the highest level in selected countries, therefore the GDP growth could considerably increase by 12,5% in Italy as top level of selected countries, and by 4,5% in Israel, which also resulted increase by 21,5% in Israel and by 8% in Italy in field of high-technology exports. In Greece the GDP decreased but the high-technology exports increased by 12% because of the top FDI net inflow by 655%, which was two times more than one of Italy by 299% and more than the average level of 21 countries by 50,11% from 2010 to 2018.

In the third session of the coordinate system there are Algeria, Saudi Arabia, India, Turkey, Nigeria, Morocco, Egypt and Tunisia. In this country-group the gross capital formation in percent of GDP (GrossCapF4) at the average level between 2010-2018 was considerably at the high level to make influences on increasing the GDP growth. In spite that this GDP growth was considerable, but this one in most of these countries was closed to the average level of 21 selected countries, as 3,19%. GDP growth was 3% in Morocco, Egypt, Tunisia, 3,7% in Saudi Arabia, 2% in Algeria, but India had the third place by 7,7% in field GDP growth, and only after Italy and China within selected countries.

In case of Morocco the considerable FDI net inflow did not have important effect on its GDP growth, by 3% and its high-technology exports in manufactured exports by 3,8%. This means that the FDI net inflow invested in mainly in the mining sector, which did not provide high-technology exports.

In the fourth session of the coordinate system there are Spain, Portugal, South-Africa and France. Generally, in these countries most of the economic variances decreased or less or little more increased. Mostly economy of France declined considerably, which was resulted by decrease of the investment in value added agriculture, the FDI net inflow was not considerable, which was accompanying with drastically decline in GDP growth by 11,5%, as the worst result within the selected 21 countries. In France this GDP decline resulted the very low level in field of high-technology exports comparably to the highly developed economies, which was 2,65% in share of the manufactured exports, when in case of Germany this was 16,4%. Naturally the reason of the low level of high-technology exports of France can be resulted by that the German high-technology exports considerably went to France and Italy and partly to Spain and Portugal, therefore the high-technology exports could decrease in cases of these European economies. Also, in France the FDI net inflow mostly went into the service sectors and not producing high-technology exports. In case of South-Africa in spite that this country has low level increase in GDP growth, the high-technology exports can be seen by 5,1% as good comparably to the developing economies.

According to the Figure-2, generally, it can be declared that the considerable GDP growth is needed for the increasing share of high-technology exports in share of manufactured exports, but some countries could realise considerable increase the high-technology, by less GDP growth, as UK, Germany and Israel, even Austria. Italy could have achieved considerable GDP growth by 12,5% as top level within these selected 21 countries, but this country has very less share of the high-technology exports in all of the manufactured exports by 8% comparably to GDP growth. GDP growth of Greece decreased by 1,7%, but this country could realise considerable high-technology exports comparably to low level even decline in GDP growth, because of the FDI net inflow contributed to increase the high-technology exports by 12%.

In cases of the crude oil exporting countries the FDI outflow was considerable by 85,5% in Saudi Arabia, by 67% in Nigeria and by 35% in Algeria, which could not help to increase considerably the GDP growth and could keep the high-technology exports at low level between 0,5% in Algeria and 1,5% in Nigeria.

The Table-4 and the Figure-3 show the classification of the 21 selected countries based on the Dendrogram using Ward Linkage and rescaled distance cluster combine and concerning the economic variances or features of countries in this study. Based on the clustering system if 21 selected countries are most favourably clustered into five clusters, the countries are classified, which are as follows:

Cluster-1: China, Hungary, Iran, Algeria

Cluster-2: France

Cluster-3:
Germany, Italy, Saudi Arabia, Spain, UK, Austria, Israel, Portugal, South-Africa

Cluster-4: India, Turkey, Nigeria, Morocco, Egypt, Tunisia

Cluster-5: Greece

There are some options to classify countries from one cluster or five clusters. In the Figure-3 the country-groups can be cut by different levels from “0” to “25”, where at the level of “0” 21 countries are classified into 21 country-groups, and even at the level of “25” all of the countries are classified into one group. If the countries are classified between levels of “5” and “10” in this case 5 country-groups will be created as it is mentioned before. Each country-group contents those countries, of which economic features are closed to each-other or countries included in one given cluster or group are different from economic features of countries of all other country-groups. For example, France and Greece are completely different in fields of their economic variances as features from each-other and from countries of all of the other clusters, therefore, these two countries are two different clusters in this study based on the SPSS statistical system.   

Cluster-1: China, Hungary, Iran, Algeria

Cluster-2: France

Cluster-3: Germany, Italy, Saudi Arabia, Spain, UK, Austria, Israel, Portugal, South-Africa

Cluster-4: India, Turkey, Nigeria, Morocco, Egypt, Tunisia

Cluster-5: Greece

Conclusions

The study overviews the main economic difficulties and some favourable economic features of selected countries mostly closed to the Mediterranean region and some other economies either countries of the region or closed to this region or some of them farer from this region has economic importance of the world economy.

The study provided proof in cases of the 21 selected countries in the period of 2010 and 2018 that the FDI net inflow has such influences on the investment in value added agriculture and share of gross fixed capital formation of agricultural sector in total gross fixed capital formation. But this correlation of the FDI net inflow is not strong with these other two economic variances. But this investment in value added agriculture has stronger or middle strong correlation with agriculture value added in percent of GDP in cases of the selected countries.

Also, the mutual correlations are strong between gross capital formation in percent of GDP and investment in value added agriculture, because the investment realised in agriculture increased the gross capital formation at national level and in percent of GDP. This result is based on the general average of economic variances.

Neither FDI net inflow nor investment in value added agriculture and agriculture value added in GDP have influences on the changes of the high-technology exports in percent of manufactured exports in cases of these selected countries. In case of Greece the FDI net inflow increased by 655%, but the high-technology exports increased only by 12%, while in case of Hungary in spite that the FDI net inflow decreased by 215%, but the high-technology exports increased by 21,5% more than one of Greece. In case of Austria the FDI net inflow decreased by 82%, but the high-technology exports also increased by 13%.

The solution of this contradiction difficulty is to strengthen the cooperation to stimulate the FDI net inflow or FDI inflow among selected countries in order to develop investment in agricultural sector and increase the high-technology exports. The FDI inflow provides investment including the high -technology and possibility to increase the high -technology exports.

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Zsarnóczai, J. Sándor

Corresponding author, CSc, Habil, Lecturer,  Óbuda University, Rejtô Sándor Faculty of Light Industry and Environmental Engineering Institute of Environmental Engineering

Somogyi, Norbert PhD.

conseiller scientifique Embassy of Hungary