Economic development of german regions

Abstract

The study analyses the main economic conditions of the 38 regions of Germany within the NUTS 2 region. In this study the most important eight regions are emphasized, as Oberbayern, Düsseldorf, Stuttgart, Darmstadt, Köln, Berlin, Karlsruhe, Hamburg, which provided 1 341 404 million euro, as 44,5% of all regional gross value added of 38 German regions. The economic development should narrowly relate with the highly educated level of more share of population and the highly developed techniques and production technology based on the adequate considerable purchasing power of consumers accompanying with the international competitiveness of producers. 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.

It was proofed that the EmpHiTe5, employment in high-tech sectors in share of total employment, has very strong correlation with TerEd25_648 as tertiary educational attainment, age group 25-64, by 0,870 and middle strong correlations with RGVA9, as regional gross value added at basic prices by 0,531.

Germany is to increase the employment in high-tech sectors in share of total employment, therefore, the tertiary educational attainment, in age group 25-64 in total should increase, which can lead to increase the regional gross value added. In order to achieve these aims, this needs for increasing human resources in science and technology, and R&D expenditure by increasing employment level accompanying with decreasing the unemployment level.

Keywords: High-tech sectors, Oberbayern, Research, Statistical analyses, Technology

JEL code: O11, O 14, O15

Introduction

The study analyses the main economic conditions of the 38 regions of Germany within the NUTS 2 region. The objectives of the study focus on the regional gross domestic product by regions in million EUR (RGDP1), and Regional gross domestic product (PPS, as purchase power standard per inhabitant, euro) by regions (PersRGDP2), disposable income of private households by regions, PPS (based on final consumption) per inhabitant (DisIncHous3). The Human resources in science and technology (HRST) by regions in percent of active population, (HuReSTPo4), employment in high-tech sectors by regions percent of total employment, (EmHiTe5), employment rate of the age group 15-64 by regions, in share of age group, (Empl15_646), unemployment rate by regions, in percent in 2018 (UnEmpl7), Tertiary educational attainment, as age group 25-64 by sex and regions, in percent of total, in 2018 (TerEd25_648), gross value added at basic prices at regions (RGVA9), intramural R&D expenditure (GERD) by sectors of performance (RandDExp10) at level of NUTS 2 regions. The main statistical data base comes from the Eurostat for the period of 2010 and 2018.

The German regions NUTS 2 including some main largest German towns are thirty-eight one, which are as Stuttgart, Karlsruhe, Freiburg, Tübingen, Oberbayern, Niederbayern, Oberpfalz,

Oberfranken, Mittelfranen, Unterfranken, Schwaben, Berlin, Brandenburg, Bremen, Hamburg,

Darmstadt, Gießen, Kassel, Mecklenburg-Vorpommern, Braunschweig, Hannover, Lüneburg,

Weser-Ems, Düsseldorf, Köln, Münster, Detmold, Arnsberg, Koblenz, Trier, Rheinhessen-Pfalz, Saarland, Dresden, Chemnitz, Leipzig, Sachsen-Anhalt, Schleswig-Holstein and Thüringen.

Generally, the relations among economic variances of ten economic variances of four components are applied for the 38 German regions in coordinate system (Figure-1; Figure-2 and Figure-3, Eurostat 2019), but in this study the most important eight regions are emphasized, because this study can be adequate only for eight regions. Oberbayern, Düsseldorf, Stuttgart, Darmstadt, Köln, Berlin, Karlsruhe, Hamburg, the eight NUT 2 regions – including two towns: Berlin, Hamburg – provided 1 341 404 million euro, as 44,5% of all regional gross value added of 38 NUT 2 German regions by the end of 2018. From this point of view the places of these eight regions in coordinate system can be analysed as the most important and developed regions of Germany in fields of ten economic variances.

Economy of Germany has important economic role in the prosperity of the European Union and has important considerable influences on the future further economic development trends of the EU member states. The study has reason to analyse the above-mentioned objectives the regional gross domestic product, as output of regions, with expressing GDP in PPS (purchasing power standards) avoiding of the differences among price levels. Importance of the study is to analyse the disposable income of private households based on the balance of primary incomes. From point of view of the human resources in science and technology as a percentage of total active population, the employment in high-tech sectors (code HTC). Aim of the study to analyse economic conditions concerning the employment issues in field of the population aged 25-64 completing tertiary studies. In case of analyses of the German regions the analysing gross value added as intramural R&D expenditure (GERD) by sectors of performance and NUTS 2 regions.

Material and Methods

The study completely overviews the main economic conditions and features of regions in Germany from point of view of the economic growth in fields of regional GDP, value added products, employment issue and economic development in fields of techniques, technologies, high-tech and improving innovation. The main aims of the study to analyse the correlations of the economic features and variances. Therefore, the study uses the SPSS (Statistical Program for Social Sciences), which provides possibilities to analyse the economic conditions of regions at the wide-side level.

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).

Based on the different steps of the statistical analyses the economic compare can be realised among the regions from point of view of correlation, namely how much economic variances or features of regions are comparing with each other or significance, namely how much less differences are among regions. Also, the statistical analyse provides factor analyses and factor score system by using graph presentation for local positions of the different German regions in order to show the distance among regions selected into four quarters of the coordinate system.

Based on the statistical analyse and determining the economic variances or features of regions

the German regions became selected into five main clusters, in which each one cluster include those regions which have similar economic features or variances and these economic features of each cluster are different from one of other clusters. The next chapters overview the results of the analyses and comparing economic features of German regions.

Results and Discussion

The study has importance of emphasizing the main economic developing trends of all German regions at NUTS 2 level, which could strengthen the performance and social economic development of Germany, which can be emerged at the EU-level and world-wide side level. The economic development should narrowly relate with the highly educated level of more share of population and the highly developed techniques and production technology based on the adequate considerable purchasing power of consumers accompanying with the international competitiveness of producers. From point of view of these economic conditions the successful example of Germany can be followed, which this study would like to demonstrate.

In order that the economic developing trend can be strengthened in Germany, this needs for strong financial conditions and internal control at company, corporation and bank levels in order to ensure the adequate financial flow. Also, some authors emphasized that the stricter guidelines could and can have a positive impact on banks’ operations stabilizing through the strengthening of internal control practices. (Lentner et al, 2019; Almási-Zéman, 2019 and Sadaf et al 2019). Also, some authors emphasized the importance of competitiveness in cases of economic conditions of regions in Central-East European economies (Lentner et al, 2018). 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, 2019).

From point of view of these economic ideas this means that the efficient performance of the society, even in case of Germany also needs for the well-managed regions including NUT 2 additionally to the efficient governance of the firms and corporations. Additionally, to financial issues, the technological development using know-how and scientific results should be applied by adequate qualified and educated human resources. Therefore, 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). Also, other authors focus on the creating successful business environment in order to strengthen competitiveness of entrepreneurship based on the national and regional economies (Grigore – Drăgan, 2015), (Habánik, et al, 2016).

In order to sell the manufactured products created by advanced high-tech of the companies, there is an important issue, as adequate purchase power parity of the consumers by increasing the disposable income of private households. This means that the corporations and companies are not enough to be competitive on the markets either national or international one, but their future development continuously needs for the markets accompanying with consumers based on their satisfactory purchase power. Therefore, this study also emphasizes the disposable income of private households additionally to regional GDP growth, human resources in science and technology, employment in high-tech sectors, tertiary educational attainment and gross value added with less unemployment rate than German regional level.

The important role of German regions needs for emphasizing their unified performance to contribute to the main economic position of Germany at EU-level and as well as wider-side international level. Therefore, the objective of the study is important. The Table-1 summarises the main economic figures relevant to the significant economic features as economic variances of the German regions at the NUT 2. Also, some main towns or cities are equally to some regions NUT 2 classification, for example Berlin, Bremen and Hamburg.

RGDP1= Regional gross domestic product by NUTS 2 regions – million EUR,

2010-2017, 2010= 100

PersRGDP2= Regional gross domestic product (PPS per inhabitant, Euro) by NUTS 2

regions, 2010-2017, 2010= 100

Short Description: GDP (gross domestic product) is an indicator of the output of a country or a region. It reflects the total value of all goods and services produced less the value of goods and services used for intermediate consumption in their production. Expressing GDP in PPS (purchasing power standards) eliminates differences in price levels between countries. Calculations on a per inhabitant basis allow for the comparison of economies and regions significantly different in absolute size. GDP per inhabitant in PPS is the key variable for determining the eligibility of NUTS 2 regions in the framework of the European Union’s structural policy.

Code: tgs00003 and tgs00005

DisIncHous3= Disposable income of private households by NUTS 2 regions,

PPS (based on final consumption) per inhabitant, Euro, 2010-2016, 2010= 100

Short Description: The disposable income of private households is the balance of primary income (operating surplus/mixed income plus compensation of employees plus property income received minus property income paid) and the redistribution of income in cash. These transactions comprise social contributions paid, social benefits in cash received, current taxes on income and wealth paid, as well as other current transfers. Disposable income does not include social transfers in kind coming from public administrations or non-profit institutions serving households. Code: tgs00026

(Minus) HuReSTPo4= Human resources in science and technology (HRST) by NUTS 2

regions % of active population, 2010-2018, 2010= 100

Short Description: Human resources in science and technology (HRST) as a share of the active population in the age group 15-74 at the regional NUTS 2 level. The data shows the active population in the age group 15-74 that is classified as HRST (i.e. having successfully completed an education at the third level or being employed in science and technology) as a percentage of total active population aged 15-74. HRST are measured mainly using the concepts and definitions laid down in the Canberra Manual, OECD, Paris, 1995. Code: tgs00038

EmHiTe5= Employment in high-tech sectors by NUTS 2 regions

% of total employment, 2018

Short Description: The data shows the employment in high-tech sectors (code HTC) as a percentage of total employment. The data are aggregated according to the sectoral approach at NACE Rev.2 on 2-digit level and is oriented on the ratio of highly qualified working in these areas. Code: tgs00039

Empl15_646= Employment rate of the age group 15-64 by NUTS 2 regions, in %, 2018

Short Description: Regional (NUTS level 2) employment rate of the age group 15-64 represents employed persons aged 15-64 as a percentage of the population of the same age group. The indicator is based on the EU Labour Force Survey. The survey covers the entire population living in private households and excludes those in collective households such as boarding houses, halls of residence and hospitals. The employed persons are those aged 15-64, who during the reference week did any work for pay, profit or family gain for at least one hour, or were not at work but had a job or business from which they were temporarily absent. Code: tgs00007

(Minus) UnEmpl7= Unemployment rate by NUTS 2 regions, in %, in 2018

Short Description: Regional (NUTS level 2) unemployment rate represents unemployed persons as a percentage of the economically active population (i.e. labour force or sum of employed and unemployed). The indicator is based on the EU Labour Force Survey. Unemployed persons comprise persons aged 15-74 who were (all three conditions must be fulfilled simultaneously): 1. without work during the reference week; 2. currently available for work; 3. actively seeking work or who had found a job to start within a period of at most three months. The employed persons are those aged 15-64, who during the reference week did any work for pay, profit or family gain for at least one hour, or were not at work but had a job or business from which they were temporarily absent.

Code: tgs00010

TerEd25_648= Tertiary educational attainment, age group 25-64 by sex and NUTS 2 regions, in % Total, in 2018

Short Description: The indicator is defined as the percentage of the population aged 25-64 who have successfully completed tertiary studies (e.g. university, higher technical institution, etc.). This educational attainment refers to ISCED (International Standard Classification of Education) 2011 level 5-8 for data from 2014 onwards and to ISCED 1997 level 5-6 for data up to 2013. The indicator is based on the EU Labour Force Survey. Code: tgs00109

RGVA9= Regional gross value added at basic prices by NUTS 3 regions, 2014-2018,

2014= 100, Eurostat, [nama_10r_3gva], million-euro NACE_R2: Total – all

NACE activities

RandDExp10= Intramural R&D research-development expenditure (GERD) by sectors of

performance and NUTS, regions 2013-2017, 2013= 100 [rd_e_gerdreg]

The Table-2 shows the correlations among the economic variances as features of the German regions. If the value of correlations is more than 0,800, in this case the correlation is very strong, but the value is between 0,600 and 0,800, in this case the correlation is strong. But if the value is between 0,500 and 0,600, in this case the correlation is middle strong. In those cases, when the value of correlations under 0,500 and not less than 0,400 level, the correlation can be titled as weak correlation. Under 0,400 level of values are not important for the SPSS analyse and research. The upper-half of the Table-2 is relevant to values of the correlations among the economic variances, from diagonal line of which to up-right side the data are the same as data in down-left side of this table.

The last figures of the economic variances provide ordering number of the economic variances. The “Minus” sign before the economic variances means that the given economic variance has correlations to be in inverse to ratio of other economic variances, than the original ratio relevant to the coordinate system. When the given “minus” signed economic variance is in the positive locations of the coordinate system, this economic variance has negative value in correlations with other economic variances, or when the given “minus” signed economic variance is in the negative locations of the coordinate system, therefore this economic variance has positive value in correlations with other economic variances. In this case if given minus variance increases the other variances can decrease.

From these points of view the EmpHiTe5 (Employment in high-tech sectors by NUTS 2 regions % of total employment) has very strong correlation with TerEd25_648 (Tertiary educational attainment, age group 25-64) by 0,870 and middle strong correlations with RGVA9 (Gross value added at basic prices) by 0,531. This means that in those German regions, where the employees with tertiary educated level increase, this can ensure more highly developed jobs as employment in high-tech sectors and more increase in field of regional gross value-added product. Also, this means that TerEd25_648, as economic variance has strong correlation with RGVA9 by 0,607. This means that the highly educated human resources can stimulate the increase of the regional gross value added and therefore to create more competitive production of the companies.

RGDP1 (Regional gross domestic product by NUTS 2 regions – million euro) has middle strong correlation with PerRGDP2 (Regional gross domestic product, PPS per inhabitant, Euro) by 0,598 and weak correlation with RGVA9 economic variance by 0,467. This last one means in cases of German regions NUT 2 that if the regional gross domestic product increases the regional gross value added can also increase, but sometimes this can also stagnate. This means that the correlation is not strong between regional gross domestic product and gross value added.

The PersRGDP2 (Regional gross domestic product on based on PPS, as purchasing power standards per inhabitant, euro) has main weak correlation with DisIncHous3 (Disposable income of private households by NUTS 2 regions, PPS as based on final consumption per inhabitant) by 0,425. Also, this last one, as DisIncHosu3 has weak correlation but in inverse ratio to TerEd25_648 by (Minus) 0,410 value. This last one means that if the disposable income of private households increases the tertiary educational attainment decreases, which means that the tertiary educated level, as BSC, MSc or more, increases for the employees, their incomes can increase. Naturally the highest educated level can ensure better jobs with higher salary for employees.

(Minus) Emp15_646 (Employment rate of the age group 15-64) has strong correlation but in inverse ratio to UnEmpl7 (Unemployment rate) economic variance by (Minus) 0,664. This means that if the employment between 15 and 64 aged people increases the unemployment rate decreases.

The very strong or strong significance among economic variances means that the difference among them is not considerable or there is no any difference among them, therefore it is closed to “zero”. In this case of “zero” the significance is the strongest. Mostly the significances are relevant to the correlations among the economic variances. The value of the significance cannot be minus, because it should be better to be “zero”, as no difference among economic variances, therefore, this is not minus.

The Table-3 shows the rotated component matrix and the classification of ten economic variances into four components, by the other words this table shows the set-up of the components by variances. The component-1 is consisting of three economic variances, namely

EmHiTe5 (Employment in high-tech sectors), TerEd25_648 (Tertiary educational attainment, age group 25-64) and RGVA9 (Gross value added at basic prices).

Table-3: Rotated Component Matrixa
	Component
	1	2	3	4
RGDP1	,143	,864	-,021	-,036
PersRGDP2	-,069	,850	,176	-,059
DisIncHous3	-,439	,546	,326	,278
HuReSTPo4	,013	,334	-,322	-,629
EmHiTe5	,922	-,104	,043	,118
Empl15_646	,208	,288	,852	-,034
UnEmpl7	,092	,000	-,875	,031
TerEd25_648	,951	-,050	-,052	-,086
RGVA9	,737	,454	,160	-,075
RandDExp10	-,008	,140	-,249	,768
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 5 iterations. Source: Owned calculations based on the Eurostat 2019 and SPSS statistical system Principle line “X”= Component-1: EmHiTe5, TerEd25_648, RGVA9 Principle line “Y”= Component-2: RGDP1, PersRGDP2, DisIncHous3 Principle line “Y”= Component-3: Empl15_646, (Minus) UnEmpl7 Principle line “Y”= Component-4: (Minus) HuReSTPo4, RandDExp10

The component-2 is consisting of three variances, namely RGDP1 (Regional gross domestic product), PersRGDP2 (Regional gross domestic product, PPS per inhabitant, euro) and DisIncHous3 (Disposable income of private households). The component-3 is consisting of two variances, namely Empl15_646 (Employment rate of the age group 15-64) and UnEmpl7 (Unemployment rate). The component-4 is consisting of two variances HuReSTPo4 (Human resources in science and technology) and RandDExp10 (Intramural R&D expenditure, GERD by sectors of performance).

Those economic variances of each component can be selected from 10 different variances, which each variance has the highest values in its owned lines in the Table-3. For example, the RGDP1 variance is selected to the second component, because in its line this variance has the highest-level value, as 0,864 in the second column, as second component. Each different column is each different component. Also, for example, RandDExp10 is selected into the fourth component, as fourth column of the Table-3, because its highest-level value is in the fourth column in fourth component by 0,768.

In case of this table it is true, similarly to case of the Table-2, that the given “minus” signed economic variance is in the positive locations of the coordinate system, this economic variance has negative value in correlations with other economic variances, or when the given “minus” signed economic variance is in the negative locations of the coordinate system, this economic variance has positive value in correlations with other economic variances. This issue is important, because based on the SPSS statistical system the economic variances of the component-1 are relating to the principle line “X”, but all of the other economic variances within their components not the component-1, are relating to the principle line “Y” (see detailed in the Table-1).

In order to understand how much different is among economic variances as features of different German regions, and how the correlations among them, even to be in inverse ratio to each-others, the most successful means for these aims are the coordinate system to describe the measures of difference and similarity among economic variances. Therefore, the further step in my study is to analyse measures of the correlations according to economic variances of the German regions.

In the Figure-1, the first coordinate system shows that at the principle line “X” the component-1 includes EmHiTe5, TerEd25_648, RGVA9 and at the principle line “Y” the component-2 includes RGDP1, PersRGDP2 and DisIncHous3. This means that in the first quarter when EmHiTe5, TerEd25_648, RGVA9 increase or less decrease, also, the RGDP1, PersRGDP2, DisIncHous3 increase or less decrease. In the second quarter, when EmHiTe5, TerEd25_648, RGVA9 decrease or less increase, also, at the principle line “Y” the RGDP1, PersRGDP2, DisIncHous3 increase or less decrease similarly to the first quarter. In the third quarter under the first quarter, when EmHiTe5, TerEd25_648, RGVA9 increase or less decrease, while at line “Y” the RGDP1, PersRGDP2, DisIncHous3 decrease or less increase. In the fourth quarter, when EmHiTe5, TerEd25_648, RGVA9 decrease or less increase, while at line “Y” the RGDP1, PersRGDP2, DisIncHous3 decrease or less increase.

Based on the Table-1 and Figure-1 (Eurostat, 2019) in the first quarter of the figure the employment in high-tech sectors in % of total employment (EmHiTe5) is considerable at the highly level by 7,5% in Berlin, which was the highest and by 7,3% in Oberbayern, which could be resulted from tertiary educational attainment, age group 25-64 by 42,1% and 40,6%, and these made considerable influences on the developing trends of the gross value added by 23,6% in Berlin and 18,4% in Oberbayern by the end of 2018. This last one can mostly ensure the competitiveness of the industry for German companies at the international and national levels. Also, the other regions in the third quarter of coordinate system, namely Stuttgart, Darmstadt, Köln, Karlsruhe and Hamburg, which have mostly closed levels to each-others in fields of the economic variances of the component-1. These regions have share of employment between 4,6% and 5,9% in fields of high-tech sectors (EmHiTe5) in percent of active population, which were less than one of two regions mentioned before. Naturally in those regions, where the employment in high-tech is highest or higher, the tertiary educational levels (BSc, MSc) and the gross value added also are at higher level. The correlations are very strong or strong (Table-2) among these three economic variances.

Because in the first quarter when EmHiTe5, TerEd25_648, RGVA9 increase in Oberbayern and Berlin, also, the RGDP1 (Regional gross domestic product), PersRGDP2 (Regional gross domestic product, PPS per inhabitant, euro) and DisIncHous3 (disposable income of private households) increase in these two regions.

In the third quarter under the first quarter, when EmHiTe5, TerEd25_648, RGVA9 increase or less decrease, while at line “Y” the RGDP1, PersRGDP2, DisIncHous3 decrease or less increase in five regions, namely in Stuttgart, Darmstadt, Köln, Karlsruhe and Hamburg.

In the fourth quarter, when EmHiTe5, TerEd25_648, RGVA9 decrease or less increase, while at line “Y” the RGDP1, PersRGDP2, DisIncHous3 decrease or less increase in Düsseldorf. Düsseldorf has moderate level in field of employment in high-tech sectors by 3,5% closed to averaged 3,8% level of 38 German regions, therefore this region has less tertiary educational attainment by 25,9% in all of the aged group between 25 and 64 and by 9,6% in gross value added at regional level (Table-1 and Figure-1). Therefore, Düsseldorf is in the third quarter of the coordinate system. In cases of three economic variances Düsseldorf has reached less values than the average level of 38 regions for the researched period.

In the Figure-2, the second coordinate system shows that at the line “X” the component-1 includes EmHiTe5, TerEd25_648, RGVA9 and at the line “Y” the component-3 includes Empl15_646, (Minus) UnEmpl7.

This means that in the first quarter when EmHiTe5, TerEd25_648, RGVA9 increase or less decrease, at the line “Y” the Empl15_646 increases or less decreases and the (Minus) UnEmpl7 decreases or less increases even in cases of Oberbayern, Stuttgart, Darmstadt and Karlsruhe. The employment rate between aged 15 and 64 employees increased to the level of 80% in Oberbayern, which is the top level in all of 38 German regions, for thanks to large share of employment in high-tech sectors and highly educated level as tertiary one accompanying with increasing regional gross value-added production. Generally, in all Germany the employment level is at average level of 76,2%, which means that all of the regions have more than 70% of the employment level, which is very favourable economic conditions. In spite that the unemployment rate increased little, but this was only 2,3% under 3,34% as the average level of Germany. Other three regions have also similar favourable conditions in these fields.

In the second quarter, when EmHiTe5, TerEd25_648, RGVA9 decrease or less increase, also, at the line “Y” the component-3 includes Empl15_646, which increases or less decreases, while (Minus) UnEmpl7 decreases or less increases similarly to the first quarter.

In the third quarter under the first quarter, when EmHiTe5, TerEd25_648, RGVA9 increase or less decrease, while at line “Y” the Empl15_646 decreases or less increases and the (Minus) UnEmpl7 increase or less decrease in Köln, Berlin and Hamburg. The unemployment rate has increased more than the other regions in the first quarter, but Berlin has implemented by the highest level of unemployment increase by 6,1%, mostly two times more than the German average level in this field.

In the fourth quarter, when EmHiTe5, TerEd25_648, RGVA9 decrease or less increase, while at line “Y” the Empl15_646 decreases or less increases and the (Minus) UnEmpl7 increases or less decreases similarly to the third quarter in cases of the economic variances of the componenet-3 in Düsseldorf. In Düsseldorf the unemployment rate has increased by 4,3% with less employment rate as 71,9% comparably to the other German regions. This could have reasons, because the share of the tertiary educational attainment, age group 25-64 in total was 25,9% and regional gross value added increased only by 9,6% between 2014 and 2018 in Düsseldorf. These last results were less than the average level of German regions in these two fields. The value of regional gross value added was at very highly level, but the production increase was moderate comparably to average level of Germany.

In the Figure-3, the third coordinate system shows that at the line “X” the component-1 includes EmHiTe5, TerEd25_648, RGVA9 and at the line “Y” the component-4 includes (Minus) HuReSTPo4 and RandDExp10. This means that in the first quarter, when EmHiTe5, TerEd25_648, RGVA9 increase or less decrease, at the line “Y” the (Minus) HuReSTPo4 decreases or less increases, but the RandDExp10 increases or less decrease in Stuttgart, Karlsruhe and Darmstadt.

In spite that the human resources in science and technology (HRST) in % of active population (Minus-HuReSTPo4) does not have important correlation with intramural R&D research-development expenditure (GERD) by sectors of performance (RandDExp10) in cases of 38 German regions between 2010 and 2018. Because the share of employment does not have relation with value of subsidies or expenditure for R&D (Table-2, Eurostat 2019), but also, they are not independent each other. In Stuttgart 3% of active population are employed in science and technology sectors, but the R&D research-development expenditure has very sharply increased by 44% as top level of all Germany for the period of 2013-2018. While the Oberbayern has 9,7% % of active population, in science and technology between 2010-2018, but the R&D research-development expenditure only increased by 4,6%. It seems that these two regions are very rival partners against each other. Therefore, Oberbayern decreased to the third quarter of the coordinate system with Hamburg, Köln and Berlin.

But also, other regions have considerable conditions more important than these two regions, for example Schwaben has share of (Minus) HuReSTPo4 by 13,3% and RandDExp10 increased by 43,2%. Even Rheinhessen-Pfalz has 6,7% in field of (Minus) HuReSTPo4 and 55% in field of RandDExp10, as the top level of this field.

In the second quarter, when EmHiTe5, TerEd25_648, RGVA9 decrease or less increase, also, at the line “Y” in the component-4 the (Minus) HuReSTPo4 decreases or less increases, but the RandDExp10 increases or less decrease similarly to the first quarter of the coordinate system in Düsseldorf. Düsseldorf has 9,3% as increasing trend in (Minus) HuReSTPo4, but moderate increase by 25,4% in field of RandDExp10.

In the third quarter under the first quarter, when EmHiTe5, TerEd25_648, RGVA9 increase or less decrease, while at line “Y” (Minus) HuReSTPo4 (Human resources in science and technology in % of active population) increases or less decreases, but also the RandDExp10 (Intramural R&D research-development expenditure) decreases or less increases in Hamburg Oberbayern, Köln and Berlin. Generally, these regions have less increase in field of (Minus) HuReSTPo4, but they covered not considerable expenditure for the R&D research-development comparably for the average 9,1% level of Germany. This is not favourable for the future economic development, because both of them, should increase for R&D research-development expenditure with more share of human resources in science and technology (HRST) in active population.

In the fourth quarter, when EmHiTe5, TerEd25_648, RGVA9 decrease or less increase, while at line “Y”, while at line “Y” (Minus) HuReSTPo4 (Human resources in science and technology in % of active population) increases or less decreases, but also the RandDExp10 (Intramural R&D research-development expenditure) decreases or less increases similarly to the third quarter of the coordinate system.

Also, the Table-4 and the Figure-4 show that, how the German regions can be clustered into different clusters, either from 1 cluster for all of the regions or to 38 clusters, as every-each region can be a cluster.

Conclusions

The eight NUT 2 regions Oberbayern, Düsseldorf, Stuttgart, Darmstadt, Köln, Berlin, Karlsruhe, Hamburg – including two towns: Berlin, Hamburg – are important because they provided 1 341 404 million euro, as 44,5% of all gross value added of 38 NUT 2 German regions by the end of 2018.

Also the development essence of the 38 German regions, finally for all of Germany is to increase the employment in high-tech sectors by NUTS 2 regions in share of total employment, therefore the tertiary educational attainment, in age group 25-64 in total should increase, which can lead to increase the regional gross value added. In order to achieve these aims, this needs for increasing human resources in science and technology, and R&D expenditure by increasing employment level accompanying with decreasing the unemployment level.

All of these achieved economic conditions can lead to increase regional gross domestic product, – by purchase power standard per inhabitant and finally to increasing disposable income of private households based on final consumption per inhabitant. Therefore, these realised economic aims and conditions can be relevant to interests at national economic and household levels.

It was proofed that the EmpHiTe5, employment in high-tech sectors by NUTS 2 regions in share of total employment, has very strong correlation with TerEd25_648 as tertiary educational attainment, age group 25-64, by 0,870 and middle strong correlations with RGVA9, as gross value added at basic prices by 0,531.

There is not a considerable expenditure for the R&D research-development at the averaged 9,1% level of Germany. This is not favourable for the future economic development, because, both of R&D research-development expenditure with more share of human resources in science and technology (HRST) in active population should increase in the same time.

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Dr. Sándor J. Zsarnóczai, CSc, Habil
Óbuda University, Rejtõ Sándor Faculty of Light Industry and Environmental Engineering Institute of Environmental Engineering