COPRAS BASED COMPARATIVE ANALYSIS OF THE EUROPEAN COUNTRY MANAGEMENT CAPABILITIES WITHIN THE CONSTRUCTION SECTOR IN THE TIME OF CRISIS

Construction industry and its impact on the national economy in different countries had been investigated. In general, it can be noted that development trends of the construction industry is almost the same as the development trends of the whole country economy itself. Effi ciency level of the construction and real estate industries depends on the specifi c quantities of the variables within micro, meso and macro context. Although factors of the macro level infl uence the effi ciency level of the whole economy this investigation analyses its infl uence on the effi ciency of the construction industry. Effi ciency of the construction industry operation depends on the complex impact of the macro level variable factors such as economic, political and cultural level of development, construction industry are effected by the regulating documents, market, taxation system, drawing possibilities and conditions, infl ation, local resources etc. (Kaklauskas et al. 2011). Construction industry development possibilities vary according to the effect of macro level factors. Crisis, spin up in 2008–2009, had differently affected the construction industry markets of the European Union countries. The general part of countries had faced the decrease of outputs, real estate transactions, and predictable reduction in employment of population and quantity of construction companies. Adverse conditions and huge deviations that had arisen due to the crisis encourage analysing the situation of the construction sector not only in the particular country but in other ones, it happens because of possibility to analyse the international experience and get the broader view of the construction sector issues and solve them correctly. Procedure, presented in the issue, provide the possibility to detect the one of 23 European countries which possesses the most effective construction sector market development according to the criteria set. Countries undergo the multi-criteria evaluation applying COPRAS methods (Zavadskas and Kaklauskas 1996), evaluation criteria relevance is determined via entropy method. The fi rst time using the entropy concept (Shannon and Weaver 1947; Shannon 1948) for maximizing the quantity of information contained in the dataset. The entropy is described as the casual value of the uncertainty which makes it more valuable in comparison with other factors. Thus, the main goal of the work is to group investigated European countries applying the COPRAS method and evaluating six criteria, describing the construction sector. In order to implement this goal, economy of the European Union countries, construction sectors, statistical economic data, valuables set according to the entropy method and priority of the European country construction sectors set by COPRAS method will be evaluated.


Introduction
Strategically important construction sector includes designing of buildings, formation of the infrastructure connecting the whole economic sectors. This sector is named as the most important employer, it makes the great contribution to the common capital of European countries. Construction is one of the biggest industries in Europe it takes 10 percents of GDP and requires 50.5 percents of the capital investment. The construction sector employes over 12 millions of European citizens and involves 26 millions to the particular sphere of the construction industry DKM Economic Consultants (2010).
In comparison with other economic sectors the construction importance depends on the different factors. According to Kaklauskas et al. (2011) construction effi ciency at most depends on certain number of variables at macro level and micro level. The costs of the ground area, design process, construction process, business competition, effi ciency level of enterprises etc. are possibly at micro level. At macro level economic, politic, legal, technological, cultural and natural environments have an impact on the construction. Thus, construction sector depends on the set global and local factors which cause certain swings in the economic activity both in construction and other industrial branches.
You can notice that the relationship between construction section and economy was studied by lots of scientists (Giang and Pheng 2010;Pellicer et al. 2009;Khan 2008;Wigrena and Wilhelmsson 2007;Chiang et al. 2006;You and Zi 2007). For example, Pellicer et al. (2009) analyzed the effect of decay on the construction sector in terms of macroeconomics. Authors used regression model for study and forecast of the situation in construction sector. Chiang et al. (2006) used the tables of the model "input-output (i-o)" for study of the construction sector in macroeconomics terms. This model is effi cient instrument for determination of effect of macroeconomic factors on the construction and construction forecasting. Giang and Pheng (2010) carried out theoretical study of construction role on the economy in accordance with three scenario: (a) Infrastructure is adequate for economic growth when the infrastructure is built ahead, then the business activities of other goods and services that the infrastructure helps create come into place; (b) Infrastructure becomes excessive when the infrastructure is constructed ahead as planned, but then an unexpected economic downturn occurs; and (c) Infrastructure is inadequate to support economic growth when there is an unexpected economic upturn. Khan et al. (2008) analyzed the relation between construction sector and GDP (gross domestic product) in 1950-2005. In Pakistan Granger causality test is used (Granger and Newbold 1974). To calculate the reliability of relations and results the Unit Root tests based on time series and Co-integration test were applied. Economic time series -is sequential array of the values of economic variables. Weekly, monthly, annual indexes of productions, costs, income, population size, labour power, gross domestic product (GDP) are examples of economic time series. Obtained results of the investigations show that there is a strong causal relation between economy and construction sector. Two types of econometric models (cointegration and error correction) were applied by Wigren and Wilhelmsson for investigations of construction market in the Western Europe (2007). After analyzing criteria characterized the construction sector of fourteen countries of the Western Europe it was concluded that investments in residential and non-residential building construction or in construction of other buildings have both direct and indirect impacts on economic growth. These investigations also detected that there is a strong relationship between infrastructure and economic productivity, particularly residential building construction has a long-term effect on the economic growth. You and Zi (2007) analyzed the construction industry development in Korea (in 1996Korea (in -2000 for various periods of the crisis using the method of data environment analysis (DEA). By this method some important factors which delay effi ciently of construction enterprises in crisis period were determined. The European construction sector at micro level was investigated by Proverbs and Holf (2000), Proverbs et al. (1999). Chateau (2007), Mymrin and Correa (2007), Knoepfel (1992) and other wrote about the market of construction products in Europe.
The European construction market is non-homogenous. The current situation of each member state and perspectives depend on state position taking into account needs, demographic trends, and main economic principles etc. Economic environment in the country affects directly on business. Economic environment is determined by the tax and fi nancial resources policy, capital fl ow, investment environment, loaning and rate of interest implemented by state bodies. The situation depends also on when corrections of immovable property market were performed and economic openness of individual country for the impact of the fi nancial and economic crisis. And fi nally it depends on what long-term measures of recovery will be selected and how successfully they will affect on the construction sector (Kaklauskas et al. 2010a). Certain industry branches are characterized by cyclical swing covered changes of production, work, and sales number. Among these branches there are such sectors as construction, steel industry, and aero industry. The ordinary swings of construction product values reach about 20 percent. It was noticed that cyclical swings in construction industry which appear as "booms" and recession are recurred (Kaklauskas et al. 2011). Clear and reliable access to statistic data is very important while performing the monitoring of construction sector market. In this document collected data characterize the construction sector market of 23 European states in 2009. The data obtained from Eurostat (2010) and European Federation of Building and Woodworkers (2010) database demonstrate percent change of construction indexes in comparison with ones in 2008. The data describe six criteria affecting the market of the construction sector: GDP change, index of growth rate of buildings and all construction products, number of issued construction permits for new residential buildings, price index change for civil engineering products, price index change for new residential building construction, total employment in construction sector. 23 European countries, for which specifi c indexes were found, were selected for evaluation.

Determination of important entropy-based criteria
For achievement of the goal specifi ed in this article, fi rst of all the calculations were performed in order to determine criteria importance by entropy method. The initiator of the method (Shannon 1948) gave the following numerical expression of entropy method (1) (quantity of information in dataset): 1 ln( ), here S -entropy matrix, N -number of criteria, x j -criteria value, j -criteria change limits ( j = 1... n).
This method was applied for deciding construction issues (Zavadskas 1987) also in other fi elds (Liu and Zhang 2011;Mamtani et al. 2006;Li 2009;Ye 2010;Taheriyoun et al. 2010;Hsieh et al. 2010). The algorithm block diagram for entropy method is presented on Figure 1.
In this case the importance of indices is determined. Their importance demonstrantes what criteria are the most important in comprising with other criteria. For determining criteria importance, the indices are transformed in such a manner that maximum value of each criteria would be the best. While preparing initial data for multi-criteria evaluation by the alternative decision, fi rst of all the list of criteria is made out. These criteria have an impact on the results of the most effective decision. Further in the article the following criteria will be analyzed: • Growth rate of GDP volume • Volume indices of production in all building and construction growth rates • Index of building permits -number of dwellings, new residential buildings • Index of production, civil engineering, growth rates • Index of construction costs, new residential buildings • Total Employment in construction sector Initial criteria for evaluation of 23 European countries and data are presented in the Table 1.  The values of analyzing criteria for each country are transformed by formula (2), thus the initial matrix without negative values are obtained: here x ij * -criteria values with negative values, x ij -criteria values without negative values Data obtained by formula (2) are given in the Table 2. Further the normalization of the initial matrix (table 2) was performed applying formula (3) and (4): The every element of decision matrix is divisible by sum of components from the column where it is located. Thus obtained matrix P 1 . While transferring decision matrix the indices are determined by formula (5): here p ij -matrix indices, x ij -criteria values.
Criteria obtained by formula (2) are divisible by criteria sum of each column and the fi nal criteria matrix is obtained P . Table 3 S. Kildienė et al. COPRAS   here k = 1:ln m.

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As is known, entropy index varies [1,0] by interval, so j -index change level in current tasks is determined: If all criteria are equally important i.e. there are no subjective or expert evaluations of their values, criteria importance is determined by formula: After determination of criteria importance the priority order for considered criteria can be specifi ed: 1. Volume indices of production in all building and construction growth rates. 2. Index of building permits -number of dwellings, new residential buildings.. 3. Total Employment in construction sector. 4. Index of production, civil en gineering. 5. Index of construction costs, new residential buildings. 6. Growth rate of GDP volume.
In order to evaluate the priority of each European country according to these criteria, COPRAS method is applied.

The determination of priority and importance of considered alternatives by COPRAS method
In 1996 COPRAS (Complex Proportional Assessment) method was created (Zavadskas and Kaklauskas 1996). In Lithuania this method is applied in construction, economy, immovable property and management. Zavadskas et al. (2010) evaluates the risks in construction projects in one of articles. The evaluation is based on different multipurpose evaluation methods. Risks evaluation indices are selected taking into account interests, purposes and factors of countries which affect on the construction process effi ciency and immovable property price increase. For describing and considering task model, TOPSIS grey and COPRAS-G methods are applied. In another article, using COPRAS, Zavadskas et al. (2009a) carried out the comparative analysis of the fi fteen housing enterprises according to 44 criteria taking into account needs of building owners. Kaklauskas et al. (2006) performed signifi cant investigation for Vilnius Gediminas Technical University in order to fi nd the best contractors for the window replacement in the central building. The contractors the best corresponding with the needs of the University were selected from many criteria (heat conductivity, light transmission, lifetime, sound conductivity etc.). Also Kaklauskas et al. (2010b)  In the article applying multipurpose evaluation, Chatterjee et al. (2011) studied the effi ciency of selection of production materials using three methods: COPRAS, multicriteria decision-making (MCDA) and evaluation of mixed data (EVAMIX) and made the conclusions that COPRAS method is the most effi cient and precise. Karbassi et al. (2008) used COPRAS method for investigations of energy effi ciency of the building and provision of energy effi ciency.
Under varied economic conditions, continuously increasing uncertainties for variety and size, at existence of competitive interactions and risks, it is more diffi cult to make decisions among set of alternatives therefore these multipurpose evaluation methods are very important and signifi cant under current conditions. According to Zavadskas et al. (2009b), the objective function is directly-proportional depends on indices characterized their alternative of values and weight of those indices. The multipurpose analysis is appropriate for decisions on economy, management, structural and other tasks. In COPRAS method the alternatives are described by values of discrete indices.
In the 3 stage. The relative importance of comparative variants is determined by their characterized positive S +j and negative S -j features. The relative importance of each project Q j is determined using the formula: and Estonia. The decrease of external demand has a high impact on construction sector of the Baltic States; particularly the internal demand wilted which aggravate changes, higher unemployment rate, salary cut and credit crisis (Ozols 2009).
In the international practice the different methods and models for analysis, forecasting, simulation and management of the crisis in construction and immovable property sectors are applied. In order to overcome the economic crisis, the governments of all member states take various measures, including the political, for stimulation the construction activity, particularly for increase of work programs by the state order and speedup of the implementation of planned investments. The heavy expenses for the infrastructure, for example, for roads and railways and nonresidential building construction, maintenance and repair. Some governments introduce tax concessions for demand in specifi c construction sector parts, particularly housing. Sometimes such measurements are supplemented by the subsidies for renovation and construction, including public building, road and bridge construction projects. Other countries, particularly in Southern Europe, for the purpose of increase of company liquidity partly changed the rules applied for works by the state order, reducing the time from submission of the accounts until their payment. For considering the cyclicality of construction sector and crisis management methods, this sector should be considered in all aspects taken in to account the impact of external and internal environment on it.

Conclusions
The progress of national economy and society is impossible without construction sector because construction products for various purposes is necessary for people life, work and satisfaction of social cultural and other requirements. Performed analysis of the literature confi rms that there are two main opinions on levels of study of construction sector market: macro economic and micro economic. During the crisis the needs in study and investigations of construction and economic branches are particularly increased; lot of scientists use various methods for analysis, evaluation, forecasting.
In the article six criteria refl ected construction sector and 23 European countries are selected according to available statistical data priority. The indices of 2009 refl ected percent difference in comprising with 2008 were used for study. The period was selected nonrandom. In the article it was intended to present the countries which most harm due to crisis and countries which develop construction activity well.
Multipurpose evaluation method COPRAS allows suffi ciently accurately performing math calculations and evaluate the priority of criteria. For provision data accuracy obtained by entropy method the criteria for weight are determined while evaluating values of minimizing and maximizing indices. After combining entropy and COPRAS methods as well as appropriately performing calculations, the useful information are obtained for further investigations and study.
Obtained results shows that following fi rst fi ve countries develop construction sector most effi ciently: Germany, Austria, Czech Republic, Finland, France, and the worst countries: Netherlands, Bulgaria, Lithuania, Estonia, Latvia.  (1987) in Building Technology and Management. He is a member of the Lithuanian and several foreign Academies of Sciences. He is Doctore Honoris Causa at Poznan, Saint-Petersburg, and Kiev. He is a member of international organisations and has been a member of steering and programme committees at many international conferences. E. K. Zavadskas is a member of editorial boards of several research journals. He is author and co author of more than 400 papers and a number of monographs in Lithuanian, English, German and Russian. Research interests are: building technology and management, decision making theory, automation in design and decision-support systems.