determined the socio-economic decisions, which were affected by internal transformations and interactions that occurred within the EU and globally, and consequently, complications set in to reach the work’s goals. The politicians are also aware of this condition, since they are assessed – especially criticized because of their achievements by the political opponents, who received the opposite selective achievements during their party’s governance period, in relation to the achievements of the opposition, which causes an accumulation of the arbitrary interpretation of the facts in discussions about the economy. This problem is extremely important for social awareness that will make a decision to entrust the ruling formation in the process of free elections and allow them to continue the job or force them to implement changes. The fact is that during the 20–year period of transformations, the government has never received a discharge in the assessment of society. It sufficiently illustrates the importance of the problem. The volition of society, which is virtually settled in the election, forces certain decisions of the political parties’ coalition. It should be taken into account, including a maximum of objectified quantitative indicators to assess the pro-development activities of the governing parties. The prerequisite that occurs during the development of this type of project is to guarantee the objectivity of the expert who sets up the assessment of the ruling political formation.
One of the pre-conditions related to the research team is its political neutrality and the absence of any party affiliation. The second –and equally important condition – is the choice of maximum standardized and possibly automated analysis methods, which fall within the class of tools known as Business Intelligence.
The application of these tools within public administration is cited by [20], [13] and [4] according to the wide computerization of the sector.
The most commonly used and tested tools are the methods for OLAP (On-Line Analytical Processing) and data mining. According to the authors, these methods should provide the source for the problem’s solution.
The knowledge acquired in this way plays a key role in the knowledge management process within public administration [22]. Another example of the wide possibilities of data mining applications in the field of economy is as follows: investing risk assessment in different countries based on economic indicators [3], or the detection of fraudulent financial statements of companies [12].
It is also a tradition of the Polish Association for Knowledge Management (PSZW) to publish the Studies and Materials of the dynamic range of socio-economic changes and development in the context of globalization, the processes of regional development and application of knowledge management in the administration, as well as the analysis of issues related to voter decision-making determinants in the next election presented in [10] and [11].
The traditions of the consideration of socio-economic development problems, and the analysis that was undertaken by the methods of knowledge management support in the economy, justified the exploration for objectified approaches in order to analyze the economic development of the
The purpose of this action is to provide the assessment for the progress of those processes and the implementation of automated methods used in model solutions. The next chapter will present the state of knowledge, which was adopted as a starting point for research task.
7.1 Conditions of substantive and methodological comparative analysis of economic development including the impact of processes dynamics
The starting point for the benchmarking of the country’s governance processes, implemented in stages, became a need to identify the requirements for the acquisition, processing and codification of statistical data sets, adapted to the tools of research and was for investigational purposes. An excellent starting point for identifying the country’s information infrastructure in the global economy is presented in [14] which allow one to designate criteria for the selection of research requirements to be analysed. This paper [5] also corresponds with these issues, exposing the problems of education and training of staff in building a knowledge-based economy. A natural complement to this work constitutes a publication [19], which carefully presents the step-change processes that involve the implementation of digital technology only to obtain information on the statistical reporting of economic operators in the CSO system in Poland.
Paper [1] has presented a very important substantive experience of conducting a comparative analysis of the determinants of economic development in relation to the EU member states. The wider context in the analysis of the impact of various aspects of contemporary reality of the strategic processes of creativity and innovation, education and the impact of developmental challenges and crises was presented in the following four publications [15], [16], [17], [18]. The concept of knowledge management in the conditions of the financial crisis occurrence, as a methodology to assist decision-makers, representing the elected people of central and local governments, is discussed in [7].
Paper [21] demonstrates an analysis of the role of Bernard Madof and the ethical attitude in the progression of the crisis that is representative of many pathological occurrences in the very important financial services sector. This publication attempts to analyze the socio-economic and moral conditions of crisis, which are responsible for the significant affection on the effects of violation verification of basic economic rights and social norms. As a result of the critical analysis of the unethical conduct of the financial services sector elites, legislation was introduced by an emergency procedure, in cases of criminal investigation of fraud detection in all European Union countries. The methodological conditions of the research implementation have allowed one to obtain the standardized databases which characterized in Chapter 3.3 the economic parameters of the EU countries.
In order to make a comparative analysis of the dynamics of their collected development processes, certain databases have been processed with the use of Business Intelligence technology. The comparative analysis methods that were used in the research are designed to create the possibility of effective assessment of the government obtained in different years by the government with the application of the solutions that compensate the cyclical changes occurring in the global economy. This method can provide more objective comparisons of the implemented economic policy effectiveness, as well as the provisions of the models of the parallel determination (rank) of Polish position, which is calculated for each of the studied parameters in relation to the specific parameter values obtained in individual countries. The application of Business Intelligence tools, and particularly OLAP technology, seems to be a good solution because of the digital character of the analyzed source data in the proposed test method.
An additional advantage of this approach states a very extensive layer of graphic presentation of the calculation results that can facilitate and illustrate the version of the multi-dimensional relationships in the dimension characteristics of tested items, the type of the features and functions of the time. The authors are aware that the themes of this research, regardless of the extremely turbulent macroeconomics changes, are further complicated by the complex processes of the economic transformation in the countries of Central and Eastern Europe during the research period. Those circumstances relate to the systemic changes which have been formed by over more than fifty years of operation of the command system in the market economy system. After this extremely difficult time for all of the Central and Eastern European countries, the adjustment process had to take place in these countries towards EU integration in the decision to extend the fifteen countries by ten new countries on the May 1, 2004 and by Bulgaria and Romania on the January 1, 2007. These socio–economic phenomena, that are unique in World Economic History, introduce a number of factors which are needed in the interpretation of the qualitative development processes of each of these countries.
If we accept the assumption that the sequence of regulations and international agreements that were passed by parliament has a significant impact on the results of these activities in the economy, which illustrates the results of Polish position in the ranking of comparator, we can use it for comparison. The parallel set of ranked characteristics can be an objectified basis for the evaluation of parliament and government in subsequent years in the term of the ruling coalition. Although the change of government that occurred in cabinet changes and made it difficult to assess was the consequence of decisions which have been taken by previous governments. It was the sum of points that were obtained in the standardized value (ascending or descending) position in each year which provided more objective comparisons. On the one hand, this application, which is created for the assessment of individual years, seems to be equivalent to comparisons carried out during different years and, on the other hand, it provides a comparative assessment of the adequacy of the government parties and processes of economic development.
The source data, which is available and standardized on a global scale, can be acquired from EUROSTAT sources, but it provided limited results in terms of the completeness of data sets for individual countries in the studied years. Because of the significant gaps in the database that were obtained from this source, these data were used mainly for the initial verification of the correctness of the calculations results, data model and calculation algorithms. The application of verified data from the World Bank database increased the effectiveness of the research since this bank is a major source of financial and technical assistance for developing countries. One of its main tasks is to eliminate poverty by sharing resources and expertise.
An applied approach to disseminate information enables databases, such as the WDI database (World Development Indicators), the GDF (Global Development Finance) and the ADI database (African Development Indicators). Whereas ADI contains indicators related to the African continent, GDI consists of more than 200 indicators of financial flows and debt indexes for over 128 countries. The largest open-access database of the World Bank is WDI which provides information for more than 900 indicators (financial, economic, etc.). It consists of the information which is collected for 210 countries and 18 international groups (including the European Union). The data of the World Bank WDI indicators are acquired from the official international sources and represent current and accurate information about various development indicators. WDI includes national, regional and global estimates of selected indicators. The information in the WDI database has been available since 1960; it was updated in 2010 and it contains data up to 2009.
Development Indicators) and GDF (Global Development Finance). The indicators that have been downloaded from WDI summaries are able to be transformed into the OLAP cubes. After the analysis of the diagnostic value of the applicable parameters, certain indicators have been proposed to be included in the data structure of the OLAP cube:
1. Annual GDP growth rate in the surveyed countries (%), 2. GDP per capita (including purchasing power),
3. Amount of unemployed in relation to the labor force (%), 4. The dynamics of trade balance per capita,
5. Foreign investment to the GDP ratio (%), 6. Governmental debt to the GDP ratio (%),
7. The dynamics of the value of fixed assets in the industry, 8. The inflation rate in the economy (%).
7.2 Pre-treatment data model development, the codification and multi-dimensional database preparation
The main goal of the preparation process of model solutions was to provide a wide application of standard tools associated with the MS SQL Server and a complementary environment for the software tools. The essential activities which occurred in the process of solution creation with the application of the aforementioned tools can be divided into:
Part I – includes the parameterization of the source OLAP cube database, according to a country’s parameters, economic performance and data origin date. For the proper determination and analysis, the exported data (indicators) from MS Excel have been transferred into the individually-prepared MS SQL Server database. The database can use MS SQL Server from 2005 versions.
The construction of the database includes the ability to create unlimited analysis from the presentation of descriptive information in different languages. MS Excel sheets have a flat data structure (index, country and the values for different periods) which have been separated into tables for each type of data (indicators, countries, periods, the values of the indicators for individual countries and periods) and indexed. The total number of imported indicators was 1,160 and the total number of imported values of these indicators were 590,217 for each country and period. All fields which contain the names, i.e. country name, index description, include tables that allow one to name them in different languages. The structure of the database was presented on Figure 7.1.
This certain interface was prepared in MS Access to define a specific analysis based on the data collected in the database. The interface allows for the creation of different analysis of indicators for certain country and time periods and the determination of the leading country. It allows one to select only these indicators for which the certain values for determined countries are stored in the database for specified periods of time. In addition, the possibility of execution is scheduled for the specified arithmetic operations for the analysis of the indicator through the action of another pointer or constant. It allows the separation of the indicator value by the population value indicator or the value inversion (i.e. from negative to positive) by multiplying by -1, or the substitution of another indicator in the certain period.
All of these elements are important in the determination of country’s position in the ranking, i.e. the unemployment ranking must be sorted in the opposite way from the index values. The positioning of the indicators of the defined analysis is performed with the application of
a specially-created “stored procedure”. Based on the data and arithmetic operations, which are indicated in the definition of analysis, the stored procedure creates the final ranking that gives the individual countries a deposit for the individual indicators within the certain period of time. Due to the lack of data for some countries in certain periods, the relative deposit has been applied that proportionally calculates the deposit value of the positioned country to the total number of countries defined in the analysis. The data prepared in that way is the basis for the OLAP cube creation which can provide the analysis of the components’ value, deposits and relative deposits.
Part II – includes a software simulation of the model calculation’s variant process and printing the summaries (reports) also in the form of graphic illustrations. The standard tools which were used for this purpose allowed one to construct and present pivot tables and charts based on OLAP cubes. The results were presented in the form of printouts and published on the websites which allowed both off-line and on-line communication with the database. The following tools (software) were used: MS Excel, MS Access and MS Visual Studio that can be replaced by other software which have features to use data sources such as MS SQL Server Crystal Reports, InfoMaker, or more advanced Business Intelligence presentation tools.
The model solutions, discussed above, have been designed to support the strategic analysis of economic development which was implemented with the consideration of the cyclical changes of the country’s economic development, buffered by achievements obtained by the 27 EU countries. The application of the proposed model solutions should be an ongoing effort to allow one to evaluate the results used to objectify the economic development strategy arising from the strategy of the governing coalition. The developed modeling tools for economic development at the international level can be used to control the results of the economic cycle annually – quarterly – monthly in the section of the 16 provinces.
Figure 7.1 Basic structure of database tables NUDMH NUDMBLG NUDMBLGQ F]ORQHNBXH NUDMHBQD]Z\ NUDMQD]BLG NUDMBLG MH]\NBLG QD]ZD RNUHV\BDQDOL]\ RNUBLG RNUHVBLGQ W\SRNUBLG RNUHV\BDQDOL]\BQD]Z\ RNUQD]BLG RNUBLG MH]\NBLG QD]ZD W\S\BRNUHVRZBDQDOL]\ W\SRNUBLG W\SRNUBLGQ W\S\BRNUHVRZBDQDOL]\BQD]Z\ W\SRNUQD]BLG W\SRNUBLG MH]\NBLG QD]ZD ZVND]QLNL ZVNBLG ZVNBLGQ ]URGORBLG ZVND]QLNLBQD]Z\ ZVNQD]BLG ZVNBLG MH]\NBLG QD]ZD ]URGODBZVND]QLNRZ ]URGORBLG ]URGORBLGQ ]URGODBZVND]QLNRZBQD]Z\ ]URGORQD]BLG ]URGORBLG MH]\NBLG QD]ZD MH]\NL MH]\NBLG MH]\NBLGQ QD]ZD RSHUDQG\ RSHUBLG RSHUDQG NROHMQRVF RSHUDQG\BQD]Z\ RSHUQD]BLG RSHUBLG MH]\NBLG QD]ZD ZVND]QLNLBZDUWRVFL ZDUWZVNBLG ZVNBLG RNUBLG NUDMBLG ZDUWRVF DQDOL]\ DQDOL]DBLG DQDOL]DBLGQ NUDMBZLRGBLG W\SRNUBLG RNUHVBSRF]BLG RNUHVBNRQBLG DQDOL]\BNUDMH DQDNUDMBLG DQDOL]DBLG NUDMBLG DQDOL]\BQD]Z\ DQDOL]DQ]BLG DQDOL]DBLG MH]\NBLG QD]ZD DQDOL]\BRNUHV\ DQDRNUBLG DQDOL]DBLG RNUBLG DQDOL]\BZVND]QLNL DQDZVNBLG DQDOL]DBLG ZVNBLG DQDOL]\BZVND]QLNLBQD]Z\ DQDZVNQD]BLG DQDZVNBLG MH]\NBLG QD]ZD DQDOL]\BZVND]QLNLBSR]\FMH DQDZVNSR]BLG DQDZVNBLG RSHUBLG ZVNBLG VWDOD ]DVWBRNUBRGBLG ]DVWBRNUBGRBLG
This tool should support a comparative analysis of the operational effectiveness of the regional economy and its evaluation on a national scale. This proposal may extend the concepts, presented in paper [6], which focused on the analysis of the performance of the utilities provided by local authorities for implementation within the country.
7.3 The results interpretation of model calculations of the OLAP data structure’s application for the comparative analysis of EU development
The results of model calculations, carried out iteratively, have been summarized in the following six tables, whose content was determined by certain criteria of the representativeness of the source data that allowed one to obtain the calculation. The particular difficulties have occurred in the representativeness of the source data which were verified and published by the World Bank from 1991–1993 and data from 2009. Data from 1991–1993 were eliminated due to the inability to obtain at least 24 countries which would fulfill the criteria of representativeness to over 95%.
Due to the lack of completion of the statistical data for 2009 in the full range of the 8 features, the data were limited to the five characteristics (% increase in the level of GDP in each year in the surveyed countries (%), GDP per capita, the number of unemployed in relation to the total workforce (%), inflation within the economy (%), the volume of foreign investment relative to the GDP (%) and the dynamics of fixed assets in the industry). The source data that were summarized in Table 7.1 show the level of GDP per capita and illustrate the dynamics of this parameter in a period of 16 years and the level of differentiation of this parameter in the mutual relations.
This conclusion may provide a synthetic analysis of the fact that during the period of the Polish economy transformation a significant reduction in the gap between this country and the group of developed countries in the EU (from the 4 times lower rate to a level of 2 times lower rate, excluding the Luxemburg parameters) was observed. Table 7.2 presents the results of the comparative analysis of the Polish position in the value of GDP per capita in comparison to other countries, which have been settled, from 1994–2009. It is important to note that from 1994–2001 the 22nd position in the ranking of the Polish GDP per person has remained. During of 2002–2008 the values 23rd, 24th have occurred and in 2007, the worst position -25th- has been observed. In 2008, the position was 24th and in 2009 it was 21st- that represents the most favorable level during the transformation of the Polish economy.
It is worth noting that in 2009 the two countries ¬ Estonia and Hungary had only a few hundred dollars higher level of GDP per capita than Poland, which can determine the potential opportunities for advancement in this ranking when provided with the effective implementation of national development strategies.
Table 7.1 GDP per capita (including purchasing power) in the 27 EU countries during the period of 1994–2009
Table 7.2 Polish position according to the 27 EU countries from 1996–2009 for the GDP per capita value (including purchasing power)
G D P p e r c ap it a r an k ( in c lu d in g p u r c h as in g P ow e r ) Poland 22 22 22 22 22 22 22 22 23 24 23 24 24 25 24 22 Italy 8 8 8 8 9 10 11 9 12 12 12 12 13 13 13 14 Bulgaria 25 25 27 27 26 26 26 26 26 26 26 26 27 27 27 27 Czech Republic 19 19 18 19 19 19 19 19 19 19 19 19 18 17 17 17 Estonia 24 23 23 23 23 23 23 23 22 22 21 21 20 20 20 21 Ireland 12 12 11 11 8 6 5 3 2 2 2 2 2 2 2 2 Greece 15 15 15 15 15 15 15 15 14 14 14 14 14 14 14 15 Spain 13 13 13 13 13 13 13 13 13 13 13 13 12 12 12 12 France 9 9 10 10 11 11 12 11 9 11 11 11 11 11 11 11 Latvia 26 27 26 25 25 25 25 25 25 25 25 25 25 24 23 24 Lithuania 23 24 24 24 24 24 24 24 24 23 24 23 23 23 22 23 Hungary 20 20 20 20 20 20 20 20 20 20 20 20 22 22 21 20 Holland 7 7 6 4 4 3 2 2 3 3 3 3 3 3 3 3 Austria 2 2 2 3 3 2 4 5 5 4 4 4 4 5 4 4 Portugal 17 17 16 16 16 16 17 16 17 17 17 17 17 18 18 18 Sweden 6 6 7 7 5 5 6 7 7 6 5 7 6 4 5 5 Belgium 5 5 5 5 6 7 7 6 6 7 8 8 8 10 9 9 Denmark 3 3 3 2 2 4 3 4 4 5 6 5 5 6 6 6 Cyprus 14 14 14 14 14 14 14 14 15 15 15 15 15 15 15 Romania 27 26 25 26 27 27 27 27 27 27 27 27 26 26 25 25 Luxemburg 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Malta 16 16 19 18 18 18 16 18 18 18 18 18 19 19 Slovenia 18 18 17 17 17 17 18 17 16 16 16 16 16 16 16 16 Slovakia 21 21 21 21 21 21 21 21 21 21 22 22 21 21 19 19 Finland 11 11 12 12 12 12 10 12 11 10 10 10 10 7 7 10 UK 10 10 9 9 10 9 8 8 8 8 7 6 7 8 8 7 Germany 4 4 4 6 7 8 9 10 10 9 9 9 9 9 10 8
Table 7.3 Polish position according to the 27 EU countries from 1994–2009 for the GDP per capita growth (%) Indic ator Country 19 94/ SL D 19 95/ SL D 1996 /SLD 1997/ SLD/ AWS 1998/ AWS 1999/ AWS 2000/ AWS 2001/ AWS/ SLD 2002 /SLD 2003 /SLD 2004 /SLD 2005/ SLD/ PIS 2006 /PIS 2007 /PIS/ PO 200 8/P O 200 9/P O G D P p e r c ap it a gr ow th ( % ) Poland 7 3 3 5 9 9 14 23 20 10 6 13 8 6 4 1 Italy 22 18 24 23 25 22 19 20 25 24 25 27 26 26 23 16 Bulgaria 24 17 27 26 10 21 7 7 6 5 5 5 7 8 2 15 Czech Republic 18 6 6 25 26 23 22 15 17 11 11 6 6 9 8 10 Estonia 26 8 5 2 4 25 1 2 1 2 3 2 2 4 27 24 Ireland 4 1 1 1 1 1 2 4 3 8 10 7 11 10 24 20 Greece 23 23 17 16 21 16 11 6 11 4 9 20 12 13 10 2 Spain 17 19 16 14 12 5 9 10 12 12 17 14 16 18 15 6 France 19 22 23 21 19 18 18 19 22 19 21 23 25 24 18 4 Latvia 21 27 9 3 11 6 4 1 4 3 1 1 1 2 25 27 Lithuania 27 13 4 4 2 26 24 3 2 1 4 3 5 3 7 25 Hungary 15 25 25 12 5 10 6 8 8 9 8 12 17 27 16 19 Holland 14 14 13 13 15 7 15 18 26 23 23 22 21 17 11 8 Austria 20 20 18 22 17 17 21 26 19 20 20 18 18 16 9 7 Portugal 25 9 10 11 6 11 16 17 23 27 24 25 27 23 20 3 Sweden 9 11 20 19 14 8 12 21 14 15 14 16 14 19 21 17 Belgium 13 21 22 15 24 14 20 24 21 21 18 24 24 20 12 5 Denmark 5 15 15 18 22 19 23 25 24 22 22 19 20 25 22 12 Cyprus 2 5 19 20 7 4 10 9 15 17 15 11 15 14 5 Romania 10 2 7 27 27 27 26 5 5 6 2 10 4 11 1 23 Luxemburg 11 26 21 7 3 2 3 13 9 18 12 8 10 7 19 9 Malta 3 4 8 10 20 12 5 27 13 26 27 15 19 15 Slovenia 6 12 12 9 18 3 13 12 10 13 13 9 9 5 6 21 Slovakia 1 7 2 8 13 24 27 11 7 7 7 4 3 1 3 18 Finland 12 10 11 6 8 13 8 16 18 16 16 17 13 12 14 22 UK 8 16 14 17 16 15 17 14 16 14 19 21 23 22 17 14 Germany 16 24 26 24 23 20 25 22 27 25 26 26 22 21 13 11
Table 7.4 Polish position according to the 27 EU countries from 1996–2009 for foreign investment value to the GDP ratio (%)
F or ei gn in ve st m ent s va lue t o G D P r at io (% ) Poland 11 10 9 12 17 16 19 20 21 17 9 18 14 16 16 8 Italy 27 27 26 27 27 25 25 25 24 22 21 25 20 25 23 12 Bulgaria 19 24 19 6 15 11 12 10 11 5 3 4 3 3 4 3 Czech Republic 9 4 11 16 11 6 10 6 4 16 11 6 16 13 15 11 Estonia 3 5 8 5 3 13 15 7 15 6 5 3 6 8 7 4 Ireland 13 13 7 11 2 5 3 5 2 3 27 27 27 10 27 2 Greece 23 20 23 24 27 26 27 26 26 22 26 21 27 20 19 Spain 12 16 16 22 20 19 16 14 9 11 17 21 19 19 9 20 France 17 15 17 21 22 18 23 15 16 15 18 16 18 22 17 10 Latvia 4 8 4 3 12 14 20 23 17 13 10 14 9 11 10 21 Lithuania 25 19 15 9 7 15 22 16 12 25 13 15 12 17 11 18 Hungary 8 2 3 2 10 10 17 8 14 14 12 10 4 2 2 25 Holland 10 9 6 13 4 7 5 3 10 9 24 9 24 6 24 5 Austria 20 22 14 23 21 22 21 18 25 12 20 2 26 5 19 9 Portugal 16 25 20 17 19 24 18 13 23 8 23 22 15 26 18 14 Sweden 7 3 13 8 9 4 9 12 13 23 14 19 10 14 6 7 Belgium 5 7 5 7 6 2 2 2 7 4 2 7 5 4 3 27 Denmark 6 12 25 20 16 8 4 11 19 27 26 13 25 21 21 17 Cyprus 21 11 10 4 18 9 8 4 5 7 6 11 7 9 5 Romania 18 17 24 10 14 20 24 21 20 10 4 12 8 15 8 6 Luxemburg 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Malta 2 6 2 15 8 3 6 9 27 2 7 5 2 7 Slovenia 24 23 22 19 25 26 27 22 6 24 16 24 23 23 12 24 Slovakia 15 18 18 25 23 23 14 3 21 8 17 13 20 14 23 Finland 14 21 21 18 5 17 13 19 8 18 19 20 17 18 25 22 UK 22 14 12 14 13 12 11 17 22 19 15 8 11 12 13 16 Germany 26 26 27 26 24 21 7 24 18 20 25 23 22 24 22 15
Table 7.5 Polish position according to the 27 EU countries from 1994–2009 for the number of unemployed to the labor force ratio (%)
Indic ator Country 199 4/S LD 199 5/S LD 1996 /SLD 1997/ SLD/ AWS 1998/ AWS 1999/ AWS 2000/ AWS 2001/ AWS/ SLD 2002 /SLD 2003 /SLD 2004 /SLD 2005 SLD/ PIS 200 6/PI S 2007 /PIS/ PO 200 8/P O 200 9/P O N u m b e r of u n e m p lo ye d t o lab or f or c e r at io (% ) Poland 21 22 20 18 17 21 25 25 27 27 27 27 27 26 19 Italy 18 18 19 20 20 17 19 19 19 17 14 13 11 13 17 Bulgaria 25 24 22 23 22 24 26 27 25 25 25 24 23 18 10 Czech Republic 3 2 2 2 8 14 16 16 14 15 16 15 15 10 5 Estonia 6 15 14 12 14 20 21 22 21 21 19 15 10 9 8 Ireland 22 20 18 17 10 7 4 3 4 4 1 1 3 7 13 Greece 11 12 12 14 18 18 20 20 20 19 20 23 23 23 24 Spain 27 27 27 27 27 25 22 21 22 23 23 22 21 23 27 France 19 19 20 21 20 19 18 17 17 16 18 21 22 21 20 Latvia 25 25 24 23 22 23 23 22 21 20 11 12 21 Lithuani a 24 25 24 24 24 22 24 24 24 24 24 17 8 5 11 Hungary 16 16 16 10 12 10 11 10 10 8 8 10 17 19 25 Holland 4 4 4 4 2 2 2 2 2 3 4 5 2 1 1 Austria 2 3 3 3 5 4 6 4 6 5 7 5 5 6 4 Portugal 5 6 8 7 3 3 3 5 8 10 11 12 19 22 23 Sweden 13 12 14 16 11 11 9 9 9 8 10 13 14 13 15 Belgium 13 14 11 10 14 13 11 12 12 14 13 18 20 20 18 Denmark 7 4 6 4 3 5 5 7 5 7 6 3 1 2 2 Cyprus 6 7 5 3 2 3 7 4 3 3 Romania 9 9 5 6 7 9 13 13 15 12 15 9 16 15 11 Luxemb urg 1 1 1 1 1 1 1 1 1 1 4 2 5 4 7 Malta 10 14 12 13 12 11 13 16 13 Slovenia 12 6 7 9 9 12 14 10 11 11 9 8 9 7 5 Slovakia 20 21 17 19 23 27 27 26 26 26 26 26 26 27 26 Finland 23 23 23 21 19 16 17 18 18 18 17 18 18 17 16 UK 15 11 9 8 6 8 8 8 7 5 2 3 7 10 9 Germany 10 10 10 15 16 15 15 15 16 20 22 25 25 25 21
Table 7.6 Polish position according to the 27 EU countries from 1996–2009 for the inflation level (%)
In fl at ion i n d ic a tor i n t h e e c on o m y (% ) Poland 22 22 22 24 24 24 24 21 8 3 21 9 1 12 16 24 Italy 12 16 16 10 10 12 8 13 15 16 11 8 10 6 6 14 Bulgaria 26 27 27 27 26 20 25 24 25 15 24 25 27 26 26 22 Czech Republic 17 18 19 21 23 14 18 19 6 2 16 7 13 18 22 16 Estonia 24 24 23 23 22 22 19 22 21 7 20 24 23 24 24 6 Ireland 6 9 5 6 14 11 21 20 23 21 10 12 22 22 11 1 Greece 18 17 18 17 18 21 15 15 22 22 17 22 18 17 14 18 Spain 15 15 15 9 8 17 17 16 17 19 19 21 19 15 13 4 France 2 4 7 3 2 2 4 2 9 13 9 5 7 2 4 9 Latvia 23 21 21 20 17 18 10 9 10 18 23 26 25 27 27 23 Lithuania 25 26 25 22 19 5 1 1 1 1 3 16 20 23 25 26 Hungary 20 23 24 25 25 25 23 26 24 24 25 23 21 25 21 25 Holland 10 6 8 12 11 15 5 17 18 14 5 4 2 3 1 17 Austria 11 8 6 4 3 3 7 11 7 8 7 11 4 8 5 13 Portugal 16 13 14 11 15 16 11 18 20 20 14 10 15 16 2 2 Sweden 4 10 1 1 1 1 2 7 12 10 2 1 3 7 9 5 Belgium 7 2 10 7 5 7 9 8 5 9 8 18 8 5 17 7 Denmark 3 7 11 13 9 19 12 6 14 12 4 6 9 4 7 19 Cyprus 14 11 13 16 12 10 20 4 16 23 13 15 12 11 19 12 Romania 27 25 26 26 27 27 27 27 27 27 27 27 26 21 23 27 Luxemburg 5 5 3 5 6 6 14 12 11 11 12 14 14 10 8 11 Malta 13 14 9 14 13 13 6 14 13 6 15 20 16 1 15 21 Slovenia 21 20 20 19 21 23 22 25 26 25 22 13 11 19 20 15 Slovakia 19 19 17 18 20 26 26 23 19 26 26 17 24 14 18 20 Finland 1 1 2 2 7 8 16 10 3 4 1 2 5 13 12 8 UK 8 12 12 15 16 9 13 3 4 17 18 19 17 20 10 3 Germany 9 3 4 8 4 4 3 5 2 5 6 3 6 9 3 10
Table 7.7 Polish position according to the 27 EU countries from 1994–2009 for the selected indicators
Country 1994 / SLD 1995 / SLD 1996 / SLD 1997 / SLD/ AWS 1998 / AWS 1999 / AWS 2000 / AWS 2001 / AWS/ SLD 2002 / SLD 2003 / SLD 2004 / SLD 2005 / SLD/ PIS 2006 / PIS 2007 / PIS/ PO 2008 / PO 2009 / PO Poland 15 15 14 15 17 18 21 23 22 19 18 18 15 17 16 13 Italy 17 16 18 17 18 16 16 16 17 18 17 18 19 18 17 13 Bulgaria 21 20 23 21 17 18 17 17 18 16 16 16 17 16 14 18 Czech Republic 12 9 11 17 17 15 16 14 12 13 14 12 13 12 13 11 Estonia 13 15 13 11 11 19 13 13 13 10 14 12 11 13 17 15 Ireland 11 10 9 9 7 6 7 8 7 7 8 7 11 10 14 9 Greece 20 19 18 18 18 19 17 17 18 17 19 22 18 20 19 15 Spain 19 19 20 19 18 17 17 16 17 17 19 18 18 19 18 15 France 14 14 16 15 15 13 15 13 15 14 17 17 18 18 16 10 Latvia 16 16 15 14 15 17 14 15 14 14 14 15 13 15 20 23 Lithuania 21 19 15 14 13 18 19 12 12 13 13 14 12 13 16 21 Hungary 16 19 18 15 15 16 15 15 15 17 15 16 18 19 17 19 Holland 9 9 7 9 8 6 7 9 11 10 11 8 9 6 6 8 Austria 12 13 12 14 13 12 13 15 13 10 13 11 13 10 8 9 Portugal 15 15 15 12 12 14 14 15 17 17 18 18 19 20 16 12 Sweden 7 7 10 11 9 6 7 11 10 11 8 9 8 10 9 10 Belgium 13 13 14 12 14 12 12 13 13 13 11 14 15 13 14 11 Denmark 5 7 11 10 10 10 8 11 12 13 11 8 9 12 11 13 Cyprus 14 12 13 14 13 11 13 9 11 13 10 13 10 11 9 17 Romania 14 14 15 16 19 18 19 15 17 15 15 18 16 16 14 19 Luxembur g 9 12 10 8 9 6 9 8 8 10 10 10 10 7 10 10 Malta 13 13 12 15 15 13 11 16 17 14 16 15 15 14 16 18 Slovenia 14 13 14 13 16 14 18 16 13 15 13 13 12 13 11 18 Slovakia 15 17 13 16 18 22 21 17 16 20 17 15 16 15 14 18 Finland 14 12 12 11 10 13 11 13 12 12 12 12 13 11 13 14 UK 14 15 13 14 13 13 14 12 13 14 13 14 14 15 14 14 Germany 12 13 14 14 14 12 12 14 14 15 16 16 14 16 12 9
Figure 7.2 The average value of Poland compared to 17 EU countries from 1996–2009 for selected indicators
Source: Own research.
Table 7.3 presents the results which have been obtained from the calculated position of the 27 compared countries during 1994 – 2009 according to the % increase in the level of GDP in each year for the surveyed countries. This indicator that is positioned in the scale of other EU countries is the most representative parameter to indicate the potential capacity of the country to develop a gap reduction in relation to the most developed EU countries. Poland was classified based on the following positions in the GDP per capita growth ranking: once in 1st position, twice in 3rd position, once in 4th, 5th, 7th, 8th and 10th and twice in 6th and 9th position. The worst – 23rd position – occurred during the period of government transfer from AWS to SLD in 2001. The 20th position was obtained in 2002 during the reign of SLD and 14th position was obtained in 2000 during the reign of AWS. The best place in the ranking was obtained by PO formation (1st place in 2009 and 4th in 2008).
Considering the fact that preliminary data from 2010 with a GDP growth of 3.8% and a probable second position among the EU countries, it is reasonable to state that during the period of PO governance, Poland had the highest dynamic in the economic gap reduction in relation to the most developed European Union countries. The results from the PIS governance period were 13th position in GDP growth in 2005 after the governmental transfer of the SLD, 8th position in 2006 and 6th position after the governmental transfer of PIS/PO in 2007.
The statements of Table 7.4 contain the value of foreign investment in relation to the GDP (%), which illustrates the relative scale of economic development support represented through the investment of international capital in Poland during 1994–2009. This indicator ranged from 8th to 21st positions in the ranking of EU countries and received the following values: 11th, 10th, 9th in subsequent years during the SLD governance period and 12th in 1997 during the governmental transfer from SLD to AWS.
The years discussed should be regarded as an important period in which this influencing factor determined the dynamic of economic development for the country. The next 6 years of AWS and SLD governance were characterized by the occurrence of an unfavorable position in the ranking, and the position ranged from 17th in 1998 and 16th, 19th, 20th, 21st and, again, 17th in 2003. A preferred value of this parameter was obtained in 2004 –9th position during the SLD governance
period and has deteriorated twice to 18th position in 2005 (SLD / PIS), to 14th in 2006 (PIS) and 16th position in 2007 (PIS/PO). The 16th position has been maintained in 2008 (PO) and revised twice to 8th in 2009.
Table 7.5 contains a summary of the EU countries’ ranking of unemployed in relation to the total workforce (%). In the three years of the SLD I governance, the unemployment ranged from 19th in 1994, 20th in 1995 and 19th in 1996. The governance transfer from SLD to AWS in 1997 had taken place with 17th position which has been revised to 16th in 1998 and then compounded in 1999 to 21st position. In 2000, the position fell to 25th and it was transferred to the SLD II governing period at 27th (last place). This result has been maintained during the SLDII governing period and also upon the transfer of governance from SLDII / PIS and during the first year of the PIS governance period.
During the governmental transfer of PIS/PO, 26th was observed which has been revised to 19th during the PO governing period – with the expectations of maintenance of this position in 2009 (there is a lack of source data to assess this statement) It was observed that the presence of a constant trend, obtained by several ratios during the governmental transformation period, has been associated with the ranking’s decline or stagnation. Table 7.6. contains the Polish position in the ranking of inflation in the economy (%) where only four times a single-digit value has been achieved, twice the value of 12th, 16th and 21st. The 22nd position has been obtained three times and 24th has been obtained five times.
The quoted statistics of the Polish position in the ranking of inflation level indicate a clear trend in the maintenance of its relatively high rate. It is reasonable to recall the experience of hyperinflation which was noted in the late eighties and early nineties, which can be considered as an historical burden. One can also observe the relation between the level of inflation and the high degree of dollar exchange rate according to other currencies with different consequences of these changes within the certain period of time. The most preferred level of inflation and 1st position in the ranking had occurred in 2006 during the reign of PIS. The 3rd position occurred in 2003 and 8th position in 2002 during the governance period of SLDII. The 9th position occurred in 2006 during the governance period of SLDII/PIS. The most preferred level of inflation occurred during the reign of PIS and the SLD: 3rd for PO, 4th and 5th achieved by SLDI and AWS.
Table 7.7 presents a synthetic summary of the average position of 8 features for the entire European Union countries from 1994 – 2008, and the average value for the five characteristics for 2009. The research contained representative data for at least 24 countries and for one of the values represented by 21 countries in 2009. The analysis of synthetic indicators of the country’s position in the attributes ranking that illustrate the economic development level has been presented in a version of the integer values in Table 7.7 and in the rational number which has been calculated with regards to the OLAP procedures and presented in Figure 7.2 (it has caused differences in the length of the bars for the same values in the array). The fact that should be considered as if the synthetic indicator of economic development had a much higher profile in relation to the individual indicators in the previously-analyzed charts.
A synthetic measure of economic development (illustrated in Figure 7.2) for certain governments allows one to conclude that during the comparable periods in relation to the availability of representative data during 1994–2009, it is possible to specify the following conclusions: The high rate and stable indicator of the Polish position in relation to other EU countries have occurred in the 3-year period from 1994 to 1997, coinciding with the 3-year governing period of SLD and during the governmental transfer to AWS (positions 15th, 15th, 14th
1. It can be surprising that a stable direction of a decreasing development dynamic has been observed during the governing period of AWS. The synthetic indicator value has been ranked in 17th in 1998 and then it decreased to 18th, 21st and 23rd positions during the governmental transfer to SLD. It didn’t have reasonable justification (even by undertaken reforms), and the verdict of the voters, which has led to the liquidation of AWS after losing the election, seemed to be fully justified.
2. During the SLD governing period, the gap in Polish economic development has been reduced, proven by 22nd position in 2002, 19th and 18th that were reached in subsequent years, and 18th obtained in 2005 (during the governmental transfer of PIS). All these data should be regarded as positive achievements.
3. In the first year of the PIS governing period, the results had improved and in 2006, 15th position was obtained, which has decreased by 2 points during the governmental transfer to PO.
In the first year of the PO government, the synthetic value of the indicator had improved by 1 point to 16th in 2008 and to 13th in 2009. Due to the lack of verified data of the index value for 2010 with regards to the levels of the parameters that have been published in the national statistical sources, it is reasonable to expect that the Polish position in the ranking should be between 13th – 16th.
The confrontation of this estimation with the data that has been contained in Figure 7.2 shows that the result of the economic development of the synthetic rankings during the PO governing period has been beneficial during the 17 years which have been discussed in this survey.
A small defect of the assessment method of recent governing parties can be observed, being determined by a limited number of statistical data. The political aspect of the interpretation, which has been contained in the above-formulated conclusions, is an inevitable consequence of convention and the need for research topics. According to the author’s opinion, the need for the creation of interpretation should be one of many possible options of the results’ evaluation of methodology that has been presented in the study.
7.4 The results interpretation of model calculations of OLAP data structures application for the comparative analysis of the development of the EU in the context of environmental concerns
The problem of environmental protection seems to be a crucial one which determines and will determine, in the near future, further development of our civilization on a global scale. Rapidly increasing consumption results both in demographic processes of population growth and in an increasing usage of resources per capita- both are the reasons for the appearance of shortages in water, energy and land as available resources. The destroyed natural environmental balance over the exceeded emissions of carbon dioxide is probably a cause of the undesired climatic changes that have an essential impact on the fall of agricultural output and the intensifying extremes of nature.
Average global temperatures have risen by roughly 0.13°C each decade since 1950. An even faster pace of roughly 0.2°C each decade of global warming is expected over the next 2–3 decades. Climate variability and extremes will presumably increase with climate change. Short-term natural extremes such as storms and floods, inter-annual and decadal climate variations as well as
large-scale circulation changes such as the El Niño Southern Oscillation (ENSO) all have important effects on crop and pasture production. The extreme drought and heat wave that hit Europe in the summer of 2003 caused a large decline in crop and green fodder production. The risk of having summers as warm as 2003 may increase by two orders of magnitude in the next 40 years in Europe. Increased droughts and heat waves, especially during the summer, are likely to dominate and impact Southern and Eastern Europe, while increased flooding and water logging in winter may dominate and impact Northern Europe (IPCC, 2007).
The rising atmospheric carbon dioxide (CO2) concentration is likely to promote crop and grassland production in the future; however, it will have possible knock-out effects on crop and fodder quality. Nevertheless, pasture and crop failures from drought are likely to become progressively more common. Agricultural irrigation will be limited, as the need is likely to occur during times of water shortage. Such climate trends may be large enough in some countries to offset a significant portion of the increases in the average yields that arise from genetic change, agronomical practices, CO2 fertilization and other factors. For instance, the last two decades have witnessed a decline in the growth trend of some cereals like wheat in many European countries. The projected increase of world population to approximately 9 billion people by the mid-century leads to an increasing demand for food, feed, fiber and bio-fuels. This will have to happen in a changing climate and under more stringent environmental regulation. Thus, it requires continuous assessments of the changes in yield potentials and gaps for addressing ways to close the latter and reduce negative externalities.
Therefore, it is essential for Europe to contribute to sound assessments of the national, European and international dimensions of the agriculture and food security issues under climate change. A systemic understanding should be gained by developing and integrating a large range of disciplines from climatology to agronomy and socio-economy through soil, plant, and animal sciences that must be strongly connected to a foundation of agro-ecological and socio-economic modeling. An effective policy regarding environment protection is also important, not only because of food security, but in general for sustaining fresh and healthy live organisms’ in water, land and air and especially for the entire population of the world, including the EU countries and Poland, also seeing a climatic problem regionally [9]
.
It is also obvious that environmental problems cannot be analyzed and solved from only perspective of one country, because those phenomena have an essentially wider impact. So, both the policy of United Nation, the EU and domestic government agendas responsible for the environment should shape such legal regulations to press global societies and their differing structures (households, enterprises, public institutions) to choose the most effective environment protection activities and technologies.
Since the last time, there have been some literature items located in [2] about the effects of carbon dioxide growth and also in the meaning of this phenomenon in the context of a differentiated food supply chain in Poland and some other Middle-Eastern European countries, being that EU members have come into a rapid manner of development after breakdowns in socialism. As a consequence, on the one hand, they quickly start to change their economic structures towards growth in services and some consumer-oriented industries, and on the other hand, towards the declining agricultural output and its share in creating the GDP. Hence, consumption per capita of goods has increased rapidly, but what has caused a rapid growth in
As a result, the environmental protection problem in Poland remains still unresolved. Devastated and degraded land requiring reclamation and management is land which has completely lost its utilitarian value (devastated land) as well as land where the utility value has declined due to a worsening in natural conditions or environmental changes and industrial activity as well as fallen to inappropriate agricultural practices (degraded land). The area of devastated and degraded land requiring reclamation and management at the end of 2009 totaled 62 thousand ha. (0.2% of the country’s total area). The degree of soil degradation and devastation processes does not show considerable differentiation by voivodship (from 0.1% of the total area in the podkarpackie voivodship to 0.4% in the śląskie voivodship) but do account for an important problem in highly industrialized regions. In 2009, whereas only 1,412 ha. of land were reclaimed, 490 ha. were managed, primarily for forest and agricultural purposes.
The total emission of the main air pollutants in Poland is one of the highest (in absolute levels) among the European Union countries. The largest quantities and most toxic pollutants are created in the generation and distribution of electricity, steam and hot water, and overall in the production of metals and chemicals. Because of the growth in the automotive sector (particularly cars and lorries in Poland – over 20 million in 2011), road transport is also one of the main sources of air pollutants, specially nitrogen oxides, carbon monoxide and the total pollutant particles (Main Statistical Office Data Report, Warsaw, 2010). On the other hand, the period before accession and after accession (especially for Poland from 2004) was an adjustment time to change the means and methods in order to decrease the negative effects of consumption growth and also the negative effects of the former socialist period where the environmental protection sector was completely neglected.
Many investments in industry and in public administration units (especially in rural areas) were made to absorb different types of gases as well as filter sewage. During this time, there were several sewage treatment plants built on territories in urban and rural communities. So, as presented in figure table 7.8, the Polish index position shows that CO2 emission was relatively unchanged during the surveyed period from 1991–2008 versus other EU countries, because Poland still occupies the 27th position. In annual freshwater withdrawals, as in agriculture and industry as domestic usage, the situation improved from 2000 to 2007 since Poland had improved its ranking from 23rd to 21st place. If we analyze water pollution in different industries, it should be stressed that the situation from 1992 up to 2004 deteriorated, while in 2005 the Polish position in this area improved because the relevant index had dropped down from 27th to 26th position (see Table 7.9). Similarly, in the last years of Polish transformation it can be observed by paying attention to some other indexes, e.g. adjusted net savings including particle emission damage (current US$), because it had dropped down from 26th in 2007 to 23rd in 2007 (see Table 7.9). The same situation had taken place with the index of net savings energy depletion (% of GNI).
Table 7.8 Report on some indexes showing a change in position for Poland between 1991 and 2008 in the area of environmental protection versus the position of other EU countries
Source: Own research based on OLAP analysis made in the UTP, Faculty of Management, and Department of Computing in Management.
