Evaluating situation of Lithuania in the European Union: structural indicators and MULTIMOORA method/Lietuvos situacijos Europos Sajungoje ivertinimas: strukturiniai rodikliai ir MULTIMOORA metodas.

Balezentis, Alvydas ; Balezentis, Tomas ; Valkauskas, Romualdas 等

1. Introduction

In the age of globalization more and more states as well as international organizations realize the importance of supporting region's or state's competitiveness against other participants of world economic system. This need caused creation and adoption of many various strategies. Nowadays such areas as sustainable development, knowledge economy and information society are among the most important issues discussed it those strategies. Strategies of sustainable development are analysed in-depth by Hass et al. (2002: 51-83) and Wolff (2004: 14-31). Implementation of every strategy is based on certain implementation policy. Statistical indicators identifying respective social, economic or environmental processes enable to perform policy evaluation and preparation functions. Thus, appropriate usage of statistical indicators is of high importance when preparing effective regional policy.

The European Union developed from institutions which were established in 1957 in order to promote integration of European countries in various areas. Among many other strategies of the European Union, so called Lisbon strategy was adopted in 2000, where means to achieve certain goals and thus become the most competitive region in the world are defined. Goals and their achieving means are identified by structural indicators or their sets. Therefore structural indicators represent situation of state among other states in specific area. They bear this name because they describe structures and key aspects within each domain. Structures are basic characteristics which do not in general change rapidly. Therefore structural indicators describe evolution in society in the long-term (Ragnarson 2007: 5).

Synthetic indicators (indexes) are calculated using various methodics (Tvaronaviciene et al. 2008). These indexes can help to evaluate economic, social and environmental situation and to compare states among themselves (to provide ranks).

The aim of this article was to describe main structural indicators identifying implementation of Lisbon Strategy goals and by using them evaluate Lithuania's position in the European Union. In order to achieve this aim, following tasks were raised: 1) to describe and classify structural indicators; 2) to overview main methods of quantitative analysis based on use of structural indicators; 3) to apply them when evaluating position of Lithuania in the European Union.

It is possible to evaluate state's progress in seeking goals of the Lisbon Strategy with help of structural indicators and to define problem areas. Appropriate identification of such problems is necessary for preparation of more effective regional policy means. Application of quantitative methods enables to evaluate states, regions or any other objects (Kedaitis and Vaskeviciute 2007: 5-7; Ginevicius and Podvezko 2009: 109-110; Ginevicius et al. 2004: 1-2; Brauers et al. 2007; Brauers and Ginevicius 2009: 124-125).

Structural indicators, their application areas and methods are overviewed in this article. Multi-Objective Optimization by Ratio Analysis (MOORA) method based on the ratio system and the reference point approach and MULTIMOORA (MOORA plus Full Multiplicative Form) were applied. Theoretical fundaments (Lisbon Strategy) of the usage of structural indicators and practice of the usage of structural indicators in Lithuania are defined in the second section of this article. Lithuania's position in the European Union is evaluated by quantitative methods in the last section of this article.

2. Structural Indicators: the European Union and development of its Member States

The Lisbon Strategy, which caused establishment of structural indicators practice, and its development history are overviewed in this section. In addition, main structural indicators used in the European Union and Lithuanian statistics practice are defined as well as their importance in identification of European development progresses. Structural indicators (as well as other indicators) are important in evaluating current policies and preparing new ones (Fig. 1).

[FIGURE 1 OMITTED]

2.1. The Lisbon Strategy

Main guidelines of the European Union development were drawn on March 23-24, 2000 in meeting of the spring European Council which was held in Lisbon. Hence, these guidelines are called the Lisbon Strategy. The main objective of the strategy was to become by 2010 the most competitive and dynamic knowledge-based economy in the world, capable of sustainable economic growth with more and better jobs and greater social cohesion (Zgajewski and Hajjar 2005: 1-3). Goals of the Lisbon Strategy were necessary in order to compete witch such countries as United States or China. In 2000 the greatest attention was paid to economy, social protection and environment. The Lisbon Strategy was extended in 2001 in Stockholm meeting.

The following European Union development directions are outlined in the Lisbon Strategy (Zgajewski and Hajjar 2005: 1-3):

1. Competitive, dynamic and knowledge-based economy:

1.1. The globalization and growing emergence of information and communications results in the need of European society transformation. To seize on these processes, necessary progresses must be launched. Information needs to be distributed to all, companies and citizens, to allow them to become credible actors in the knowledge economy. Thus, Internet, e-money, mobile telecommunication are necessary to be enhanced;

1.2. Research needs to be seriously coordinated at the European level. Development of research activities enables to improve at the same time the economic growth, the employment and social cohesion. One of the reasons, placing Europe far away from United States, was so called 'brain drain, which can be avoided by establishing European Area of Research and Innovation;

1.3. Europe has the objective to become the best competitive area in the world. To reach this goal, a friendly business climate helps to its implementation. By consequence, administrative rules leading to the creation of companies and especially small and medium enterprises ought to be simplified;

1.4. Full implementation of the internal market is required for the best functioning of the economy. Therefore, goods, persons, services and capital must circulate freely, all existing barriers being removed. Moreover, the financial markets integration benefits from the circulation of the euro, boosting the competition.

2. The modernization of the European Social model:

2.1. A better level of education and training is essential to revitalize the employment. In this view, the educational system must be re-organized to increase the knowledge of a higher number of persons, to enlarge the participation of women in the working society;

2.2. Unemployment is to be lowered down and an active employment policy should be developed;

2.3. Social exclusion and poverty should be eradicated by favouring the access of employment opportunities and knowledge to all.

3. The environmental perspective:

3.1. The climate change, greenhouse gas emissions are to be lowered down and clean technologies promoted;

3.2. The viable ecological transport;

3.3. The reduction of polluted means via the responsible administration of natural resources.

Every member state of the European Union adopted implementation programmes of the Lisbon Strategy, where goals and indicators identifying them are defined. In Lithuania Lisbon Strategy implementation programme was adopted in 2005 for the first time, currently National Lisbon Strategy implementation programme for 2008-2010 adopted by Government of the Republic of Lithuania (2008) is valid.

The practice of structural indicators statistics is dynamic process. In 2000, European Commission prepared list of 35 indicators, identifying progress in seeking Lisbon goals. In June 2001 Gothenburg European Council decided, that sustainable development and environmental protection should also be considered as parts of the Lisbon Strategy (Commission of the European Communities 2001) and involved appropriate structural indicators into annual reports. European Council of 2002 in Barcelona paid more attention for innovation and research activities and their importance to Lisbon Strategy (Commission of the European Communities 2002). High level group chaired by Wim Kok was established in 2004, which concluded that the Lisbon Strategy will not have been implemented by the year 2010 and proposed for paying more attention to labour market (European Commission 2004: 39-44). In addition, European Commission began preparing annual reports on growth and jobs. Structural indicators are unified in whole European Union, therefore it is possible to compare states among themselves and to evaluate their progress. Thus structural indicators help to identify and forecast implementation of Lisbon Strategy goals and to perform international comparison.

2.2. Indicators and documents of development processes

Implementation of goals, raised in the Lisbon Strategy and other documents, is evaluated by certain structural indicators. Expanded after Gothenburg Council list of structural indicators is divided into six groups (Hass et al. 2002: 48): 1) general economic background; 2) employment; 3) innovation and research; 4) economic reform; 5) social cohesion; 6) environment. In addition these indicators identify processes of sustainable development in the areas of environmental, economic and social development (Del Nacionalines ... 2003). In 2010 new strategy called Europe 2020 was prepared, where attention is paid to same aspects of development (European Commission 2010: 30).

Due to the limited volume of this article we will not analyse structural indicators themselves in-depth. They are overviewed in various publications (Commission ... 2001; Heinemann et al. 2004). Every indicator has its quality profile where quality grades are given according to technical assessment of the indicator based on accuracy and comparability. Methodology of purchasing power parities is interrelated with the practice of economic structural indicators and international comparisons in general (European Communities, OECD 2006).

Main document of the Republic of Lithuania on Lisbon Strategy is National Programme for Lisbon Strategy Implementation in 2008-2010 (Del Nacionalines ... 2008). It consists of three parts: I. Implementation of the macroeconomic policy, II. Implementation of the microeconomic policy, III. Implementation of the employment policy. There are 11 goals and 122 tools to seek them defined in this legal act. However, Tamosiuniene et al. (2007: 180) noticed, that implementation of many goals does not coincide with the Lisbon Strategy goals directly.

The most important directives of economic development are provided in Long-term Strategy of Lithuania Economy Development until 2015 (Lietuvos ... 2002, 2007). Main instruments for economic development of various sectors are proposed in updated strategy.

Environmental aspects of development are regulated by Lithuanian Environment Protection Strategy (Del valstybines ... 1996). Main objective of the strategy is to prepare assumptions for sustainable development of the country while keeping clean environment, biological and landscape diversity and optimization of environmental economics. Overview of other legal acts and recommendations for environmental protection are presented in Strategy of Economic Factors of Environmental Protection (Cekanavicius et al. 2002).

3. Lithuania and other European Union Member States

The practice of structural indicators is based on monitoring of indicators (OECD 1990: 7-9). Usually system (set) of indicators, identifying analysed area, rather than single indicator is monitored. Already researched systems of indicators identifying specific goals, indexes calculated according to them and universal multi-criteria methods of indicator analysis are overviewed in this section.

3.1. Specific indexes

It is possible to outline two main groups of composite indexes: 1) indexes, which identify Lisbon Strategy implementation processes; 2) indexes reflecting development of separate sectors or whole countries.

There are special indexes created for evaluation of Lisbon Strategy implementation processes, which are based on certain systems of indicators. World Economic Forum publishes The Lisbon Review (Blanke and Geiger 2008), where indexes of competitiveness of various states are announced. This index is based on statistical data (indicators) and survey performed by the forum. Survey helps to mine qualitative data about situation of education system etc. Statistical indicators are normalized and divided into scale of 7 points. Common index and separate indexes showing progress in seeking certain Lisbon goals are calculated.

Another index identifying implementation of the Lisbon Strategy is calculated on the basis of structural indicators and published in The Lisbon Scorecard (Tilford and Whyte 2009). This index shows progress of each state as well as common progress in specific areas, advanced and lagging countries in those areas.

One of the main goals of the Lisbon Strategy is promotion of innovations. Summary Innovation Index provides a comparative assessment of the innovation performance of EU Member States (Pro Inno Europe 2010). The index is based on set of 29 structural indicators and varies between 0 and 1. Innovation activities are analysed in three views: enablers, firm activities and outputs. Above mentioned indexes can be used when performing international comparison.

Common development of states can be identified by such indicators as Human Development Index (HDI), Human Poverty Index (HPI) and Gender-related Development Index (GDI), proposed by United Nations (United Nations Development Program 2009: 203-208). HDI is based on such indicators as adult literacy rate, GDP per capita, life expectancy at birth, education level. There are two types of poverty index: HPI-1 for developing countries and HPI-2 for OECD countries. HPI-1 is based on such indicators as probability of not surviving to age 40, adult illiteracy rate, population not using an improved water source and population below income poverty line. HPI-2 is estimated according to indicators of probability of not surviving to age 60, people lacking functional literacy skills, long-term unemployment, population living below 50% of median income. GDI is estimated by dissolving above mentioned indexes by gender.

Physical Quality of Life Index (PQLI) can also be used for international comparison (Ray 2008: 1-3). PQLI is based on illiteracy rate, infant mortality rate and life expectancy. Thus various composite indexes based on different methodics can be used for international comparisons (Karnitis and Kucinskis 2009: 5-12).

3.2. Universal multi-criteria methods

Differences between the regions can be analysed by mathematical--statistical methods. Such investigations can be based on econometric models, methods of factor analysis (Kedaitis and Vaskeviciute 2007: 12) or multi-criteria evaluation. Usually, in econometric models the dependent variable is GDP per capita and its dependencies from exogenous variables are analysed. Panel models are used for international comparisons over the time (Karagiannis 2008: 192-193). Factor analysis enables to extract factors causing differences between the regions and to classify the regions.

Application of multi-criteria evaluation methods is explored in field of decision making theory (Antucheviciene et al. 2010: 109-112). There are many multiple criteria decision making methods developed. Technique for the Order Preference by Similarity to Ideal Solution (TOPSIS) was proposed by Hwang and Yoon (1981). Zavadskas et al. (2010) developed practice of TOPSIS method application. TOPSIS applying Mahalanobis distance measure (TOPSIS-M) method is discussed by Antucheviciene et al. (2010). Application of of Analytic Hierarchy Process (AHP), proposed and developed by Saaty (1980; 1997), is discussed by Podvezko (2009). Methods of Complex Proportional Assessment (COPRAS) (Zavadskas et al. 2008; 2009; 2010), ELECTRE (Elimination Et Choix Traduisant la Realite) (Roy 1990; Zavadskas 1986), total rankings, Simple Additive Weighing (SAW) (MacCrimmon 1968; Ginevicius and Podvezko 2009), geometric mean of normalized values, criterion of proportional evaluation (Ginevicius et al. 2004: 8-9), summarizing indicator (Kedaitis and Vaskeviciute 2007: 29-31), Multi-Objective Optimization by ratio Analysis (MOORA) (Brauers and Zavadskas 2006; Brauers and Ginevicius 2009: 121) are also suitable for international comparison. The MOORA method was further developed into MULTIMOORA by Brauers and Zavadskas (2010: 5). These methods rely on normalization, conversion into dimensionless numbers and evaluation of deviation from optimum point. Therefore transition from ratio (or interval) to ordinal scale is performed. MOORA method enables non-subjective evaluation, because no weights should be necessarily given to objectives in analysis. Hence, MULTIMOORA method will be used in this article to evaluate Lithuania's position in the European Union.

3.3. The MULTIMOORA method

The fundaments of the MULTIMOORA method (i. e. ratio analysis, reference point theory, full multiplicative form, nominal group technique and Delphi) were laid by Brauers (2004). In order to cope with subjectivity problems arising from the usage of weights in previously known multi-objective methods (such as ELECTRE, PROMETHEE, AHP, TOPSIS etc.), Brauers and Zavadskas linked all these methods together with theories applicable for discrete optimization under the names of MOORA and MULTIMOORA. Rank correlation methods as well as outranking methods appeared to be quite inconsistent (Brauers and Ginevicius 2009: 137-138). Thus normalization of the data by Ratio System was proposed (Brauers 2004: 293-328). Reference Point method uses the ratios obtained from the Ratio System and in this way becomes dimensionless. Combination of the Ratio System and Reference Point method results as the MOORA method (Brauers and Zavadskas 2006). The first application of multiplicative function is reported by Miller and Starr (1969). Brauers (2004: 228-289) analyzed multiplicative forms in depth. Brauers and Zavadskas (2010: 13-14) proposed MOORA to be applied together with the Full Multiplicative Form and therefore the MULTIMOORA method was created. The structure of MULTIMOORA method is shown in Fig. 2. Thus, this section consists of three parts: 1) the Ratio System; 2) the Reference Point Approach; and 3) the Full Multiplicative Form. Nominal group and Delphi techniques can also be used to reduce remaining subjectivity.

[FIGURE 2 OMITTED]

The MOORA method was proposed by Brauers and Zavadskas (2006). MOORA method begins with matrix X where its elements [x.sub.ij] denote i-th alternative of j-th objective (i = 1, 2, ..., n and j = 1, 2, ..., m). In this case we have m = 13 objectives--structural indicators--and n = 27 alternatives--European Union Member States. MOORA method consists of two parts: the ratio system and the reference point approach.

3.3.1. The Ratio System of MOORA

Ratio System defines data normalization by comparing alternative of an objective to all values of the objective:

[x.sup.*.sub.ij] = [x.sub.ij]/[square root of [n.summation of (i=1)] [x.sup.2.sub.ij]] (1)

where [x.sup.*.sub.ij] denotes i-th alternative of j-th objective (in this case--j-th structural indicator of i-th state). Usually these numbers belong to the interval [-1; 1]. These indicators are added (if desirable value of indicator is maxima) or subtracted (if desirable value is minima) and summary index of state is derived in this way:

[y.sup.*.sub.i] = [g. summation over (j=1)] [x.sup.*.sub.ij] - [m.summation over (j=g+1)] [x.sup.*.sub.ij], (2)

where g = 1, ..., m denotes number of objectives to be maximized. Then every ratio is given the rank: the higher the index, the higher the rank.

3.3.2. The Reference Point of MOORA

Reference Point approach is based on the ratio system. The Maximal Objective Reference Point (vector) is found according to ratios found in formula (2). The j-th coordinate of the reference point can be described as [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] in case of maximization. Every coordinate of this vector represents maxima or minima of certain objective (structural indicator). Then every element of normalized responses matrix is recalculated and final rank is given according to deviation from the reference point and the Min-Max Metric of Tchebycheff:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (3)

3.3.3. The Full Multiplicative Form and MULTIMOORA

Brauers and Zavadskas (2010: 13-14) proposed MOORA to be updated by the Full Multiplicative Form method embodying maximization as well as minimization of purely multiplicative utility function. Overall utility of the i-th alternative can be expressed as dimensionless number:

[U'.sub.i] = [A.sub.i]/[B.sub.i], (4)

where [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], 1, 2, ..., n denotes the product of objectives of the i-th alternative to be maximized with g = 1, ..., m being the number of objectives (structural indicators) to be maximized and [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] denotes the product of objectives of the i-th alternative to be minimized with m - g being the number of objectives (structural indicators) to be minimized. Thus MULTIMOORA summarizes MOORA (i. e. Ratio System and Reference point) and the Full Multiplicative Form. Ameliorated Nominal Group and Delphi techniques can also be used to reduce remaining subjectivity (Brauers and Zavadskas 2010: 17-19).

3.4. Evaluation of Lithuania's position in the European Union applying MULTIMOORA method

Sets of certain indicators are needed to perform international comparisons. The analysis of this article is performed using Eurostat database of structural indicators. Various authors (Tarantola et al. 2004: 13; Munda and Nardo 2005) argue that the shortlist of structural indicators correctly represents all structural indicators. Two indexes for every country were calculated: one based on shortlist indicators and other--on full list of indicators. By testing hypothesis of their equality, F test showed that trendline of scatterplot between these two indexes did not differ from 45 degree line significantly. Thus structural indicators belonging to the shortlist (Table 1) of 2008 (latest available at 2010 March) are used for analysis. Data covers 27 Member States of the European Union. Therefore it can be concluded that application of MOORA and MULTIMOORA methods in general satisfies all the conditions of robustness given by Brauers and Zavadskas (2009: 354-356).

According to the above mentioned indicators, response matrix (see Annex A, Table 3a) was created. Elements of the matrix were converted by formula (1). Summarizing index for each state was calculated using formula (2). Ranks were given to each state according to the index. The results are shown in Fig. 3. According to this index, Lithuania is 17th country from 27 European Union Member States. In addition, Lithuania is the last country in the ranking with positive index value. Estonia is five places ahead of Latvia and Lithuania. It can be concluded that Lithuania performs well if compared with South European countries (PIGS states), some Middle Europe former socialist states and new members of the European Union--Bulgaria and Romania.

[FIGURE 3 OMITTED]

Ranking of the states was performed according to the reference point approach. Firstly, the reference point [r.sub.j] was found (Table 3d). Secondly, the response matrix was rearranged by calculating deviations of each element from the reference point (see Annex A, Table 3e). These deviations show state's position in certain area (for example, null value of the first indicator means that respective state has maximum GDP per capita among EU countries). Final ranks were given using formula (3). Comparison of results obtained from application of the ratio system and the reference point approach is given in Table 2. It can be concluded, that ranks did not differ significantly. It is possible to exclude three conditional groups of Member States: first nine--most advanced (Luxemburg, Ireland, Sweden etc.), 10th to 18th states and 19th to 27th--least advanced. Ranks of the states swift inside these groups, but do not tend to differ more significantly. Observed differences occur due to Min-Max Metrics: rank is given accordingly to the worst performing structural indicator. Lithuania has rank of 22 or 17. This difference is caused by low GDP per capita, showing low common development of the economy. This draw-back is uniform for all Baltic States.

In addition, analysis of the Baltic States' position in the European Union in 2008 was performed using the Full Multiplicative Form method. Matrix of responses (see Annex A, Table 3a) was used to estimate the utility of each alternative (i.e. development performance of each European Union Member State) by applying formula (4). This utility function is n-power form (Brauers and Zavadskas 2010: 14), therefore the results are given in logarithmic scale for better visualization (Fig. 4). Calculations are given in Table 4 (Annex B) while detailed data can be obtained by contacting the corresponding author.

Lithuania's position in the European Union can be analysed in-depth by using data from the Annex A, Table 3e. Deviations from maxima (minima in case of minimization) of every structural indicator of Lithuania are shown in Fig. 5. Larger deviation means that respective indicator is further from maximum value in the European Union.

[FIGURE 4 OMITTED]

As we can see in the diagram (Fig. 5), 1st, 2nd and 4th structural indicators in Lithuania are deviated from maximum values. This means, that GDP per capita, labour productivity and employment level of older people are relatively low in Lithuania. Low values of the first two indicators can be explained by assumption that Lithuania has not found its place in world economic (specialization) system yet. Hence its industry is oriented towards production of low demand goods and services using obsolete technologies. Low employment level of older people indicates that Lithuania is not prepared to cope with challenges of ageing society. Estonia copes best with this issue among Baltic States. Inevitable demographic changes should lead to increasing proportion of older people in labour force and whole population. Thus Lithuania's economy is not fully developed and does not meet The Lisbon goals. Further problems of intellectualization and development of Lithuanian economy are analysed by Melnikas (2008a: 115-119; 2008b: 61-64).

[FIGURE 5 OMITTED]

Diagram of deviations shows that 3rd, 5th, 8th and 11th indicators in Lithuania are close to maximum values. Thus Lithuania is among leaders in the European Union by employment level, youth education attainment level, comparative price levels and greenhouse gas emissions. Low comparative price levels mean that Lithuanian production can be competitive in European Union market due to lower costs. There are fewer companies of heavy industry in Lithuania, which pollute environment, thus greenhouse gas emissions are low.

The best situation is in innovation and research area in all Baltic States. Indeed, much more attention for R&D financing and business investments is needed. Lithuania has progressed in the spheres of employment, social cohesion and environment, but employment of older people should be increased and intensity of energy consumption should be lowered (by encouraging modern energetic technologies). Indicators of general economic background are among the lowest in the European Union, thus structural reforms for Lithuanian economy are needed. Furthermore, it can be concluded that Baltic region is quite homogenous in innovation and research as well as in economic reform areas (indicators 5 to 8), thus it can become attractive for investors (Table 3e in Annex A and Fig. 6).

Estonia has the lowest value of the index of inland freight transport volume, which means that Estonia does not relate its economic development with growing intensity of inland transport. Latvia has the lowest value of greenhouse gas emissions index. Thus it can be concluded that Latvia has advanced in producing environmentally friendly energy. As shown in Fig. 5, Latvia has highest deviation among Baltic States of 9th indicator--at-risk-of-poverty rate which indicates serious social problems.

Appropriate policy of administration of European Union financial support can help to accelerate innovation as well as R&D activities. European Union Regional policy is directed to reduction of social and economic differences between regions, cohesion and development of entire European Union (Dzemyda and Melnikas 2009: 34-37; Tamosiuniene et al. 2007: 178). Four structural funds as well as one Cohesion Fund were instituted to support development. Priorities and tasks for allotting European Union financial support are defined in Lithuanian Single Programming Document. More attention should be paid for mentioned problematic areas in this and other strategic documents.

Ranking by MULTIMOORA method was performed by combining results from MOORA and the Full Multiplicative Form (Annex C, Table 5). Application of MOORA and Full Multiplicative Form methods resulted in giving ranks of 17 (ratio system), 22 (reference point approach) or 16 (The Full Multiplicative Form) for Lithuania. Latvia was given ranks of 16, 23 and 23; Estonia--11, 20 and 20 respectively among 27 Member States. Thus MULTIMOORA method was applied in obtaining final ranks: 14 for Estonia, 18 for Lithuania and 20 for Latvia. These ranks were given by minimizing sum of ranks acquired by using Ratio Analysis, Reference Point and the Full Multiplicative form methods. In addition, authors computed these ranks into three groups according to progress in implementation of the Lisbon Strategy: best performance (holding ranks 1 to 9), medium performance (10-18) and low performance (19-27). In this way every state was classified in respective group according to Ratio Analysis, Reference Point and the Full Multiplicative form methods (Table 5, Annex C). Then MULTIMOORA method was applied, which resulted in obtaining final rank, showing dependency to one of the above mentioned groups. These results did not differ from those obtained by minimizing sum of ranks; therefore detailed calculations can be obtained only from the corresponding author. Hence Lithuania and Estonia could be assigned to medium performance group and Latvia is on the very limit of the low performance group.

4. Conclusions

1. Main goals of the Lisbon Strategy are: creation of competitive, dynamic and knowledge-based economy, modernization of the European Social model, effective environmental and sustainable development policy. Implementation of the Lisbon Strategy is identified by structural indicators, which are divided into six categories: 1) general economic background; 2) employment; 3) innovation and research; 4) economic reform; 5) social cohesion; 6) environment.

2. Implementation of the Lisbon Strategy in Lithuania is regulated by such main documents as National Programme for Lisbon Strategy Implementation in 2008-2010, Long-term Strategy of Lithuania Economy Development until 2015, Strategy of Economic Factors of Environmental Protection. Implementation of goals defined in these documents is identified by structural indicators.

3. Effective international comparisons based on structural indicators are possible. Many international organizations regularly provide specific composite indexes based on structural indicators: Lisbon Review and Lisbon Scorecard indexes of performance in seeking Lisbon goals, HDI, HPI, GDI, SII, PQLI. Structural indicators can also be analysed by applying econometric, factor analysis and multi-criteria evaluation methods.

4. Lithuania is among leaders in the European Union by employment level, youth education attainment level, comparative price levels and greenhouse gas emissions. Thus Lithuania does not have serious environmental problems and can successfully compete in international market because of relatively low production costs. The Baltic region is quite homogenous in innovation and research as well as in economic reform areas, thus it can become attractive for investors.

5. GDP per capita, labour productivity and employment level of older people are relatively low in Lithuania. In addition intensity of energy consumption should be lowered by encouraging modern energetic technologies. Therefore technological backwardness is characteristic to Lithuanian economy due to low labour productivity on the one hand and high energy consumption intensity on the other. This backwardness can be eradicated by promoting innovations and R&D activities. Hence significant proportion of European Union structural support should be allotted to these problematic areas.

6. The group of countries, namely Austria, Denmark, Finland, Germany, Ireland, Luxembourg, the Netherlands, Sweden and United Kingdom, can be considered as the best performing in implementing the Lisbon Strategy.

7. Member States of the European Union may be classified into three groups according to progress in implementation of the Lisbon Strategy: best performance (holding ranks 1 to 9), medium performance (10-18) and low performance (19-27). Lithuania and Estonia could be assigned to medium performance group and Latvia is on the very limit of the low performance group.

8. The study covers data only until 2008. Indeed the global economic crisis still continues and the whole situation is quite dynamic. Hence Ireland and even the United Kingdom do no more belong to Group 1 with doubts for Spain in Group 2. Such studies could be updated on a regular basis and presented to the European Union institutions.

doi: 10.3846/tede.2010.36

Annex A. Evaluation of European Union Member States positions by MOORA, 2008.

Table 3. Ratio System (3a to 3c) and Reference Point (3d-3e) of MOORA

3a. Matrix of Responses of Alternatives on
Objectives--Structural Indicators: ([X.sub.ij])

Indicators                            max

                      1          2          3          4

Austria             122.8      113.5       84.5       2.67
Belgium             115.1      125.5       82.2       1.92
Bulgaria             41.3       37.2       83.7       0.49
Cyprus               95.8       87.3       85.1       0.47
Czech Republic       80.3       71.9       91.6       1.47
Denmark             120.1      102.5        71        2.73
Estonia              67.4       63.8       82.2       1.29
Finland             116.8      111.6       86.2       3.72
France              107.9      121.6       83.4       2.02
Germany             115.6       107        74.1       2.63
Greece               94.3      102.2       82.1       0.58
Hungary              64.4        71        83.6        1
Ireland             135.4      130.2       87.7       1.43
Italy                102       109.7       76.5       1.18
Latvia               57.3       52.6        80        0.61
Lithuania            61.9        62        89.1       0.8
Luxembourg          276.3      175.8       72.8       1.62
Malta                76.3       87.4        53        0.54
Netherlands          134       114.5       76.2       1.63
Poland               56.4        62        91.3       0.61
Portugal              76        71.2       54.3       1.51
Romania              41.6       50.2       78.3       0.59
Slovakia             72.2       79.2       92.3       0.47
Slovenia             90.9       84.4       90.2       1.66
Spain               102.6      103.6        60        1.35
Sweden               120       110.6       87.9       3.75
United Kingdom      116.2       110        78.2       1.88

Indicators                            max

                      5          6          7          8

Austria              72.1        41        20.7      105.1
Belgium              62.4       34.5        21       111.1
Bulgaria              64         46        27.7       50.2
Cyprus               70.9       54.8       20.4       90.5
Czech Republic       66.6       47.6        19        72.8
Denmark              78.1        57         19       141.2
Estonia              69.8       62.4        24         78
Finland              71.1       56.5        19       124.3
France               64.9       38.2       18.7      110.7
Germany              70.7       53.8       17.5      103.7
Greece               61.9       42.8       16.5        94
Hungary              56.7       31.4       18.1       68.1
Ireland              67.6       53.7       16.5      127.6
Italy                58.7       34.4       18.7      105.6
Latvia               68.6       59.4       24.6       72.6
Lithuania            64.3       53.1       20.2       64.6
Luxembourg           63.4       34.1       15.8      119.1
Malta                55.3       29.2       13.2       78.8
Netherlands          77.2        53        16.9       104
Poland               59.2       31.6       17.5       69.1
Portugal             68.2       50.8       19.5        87
Romania               59        43.1       26.4       60.9
Slovakia             62.3       39.2        23        70.1
Slovenia             68.6       32.8       24.6       82.3
Spain                64.3       45.6        25        95.4
Sweden               74.3       70.1       16.2      114.5
United Kingdom       71.5        58        14.5       100

Indicators                                 min

                      9          10         11         12         13

Austria               12        0.9       111.3      140.73      97.9
Belgium               15        3.3        90.1      198.76      78.3
Bulgaria              21        2.9        57.2     1016.29     116.6
Cyprus                16        0.5       185.3      212.16      76.7
Czech Republic        9         2.2        77.6      553.16      86.4
Denmark               12        0.5        96.1      105.7        78
Estonia               19        1.7        51.7      580.71      67.1
Finland               14        1.2       110.3      229.19      77.3
France                13        2.9        94.2      165.38      88.5
Germany               15        3.8        77.6      151.48     111.9
Greece                20        3.6       123.2      181.79     106.8
Hungary               12        3.6        65.8      400.76     132.2
Ireland               16        1.6       124.5      103.13     102.1
Italy                 19        3.1       106.9      142.78      95.2
Latvia                26        1.9        46.6      306.6       95.2
Lithuania             20        1.2        50.1      432.5      121.5
Luxembourg            13        1.6        98.1      158.53      89.3
Malta                 15        2.5        149       198.18     106.8
Netherlands           11         1         97.4      177.12      88.7
Poland                17        2.4        70.8      400.1      121.7
Portugal              18        3.7       136.1      196.85     155.8
Romania               23        2.4        54.7      655.59     165.8
Slovakia              11        6.6        65.2      538.64      92.1
Slovenia              12        1.9       101.8      253.29     138.5
Spain                 20         2        152.6      184.19     133.1
Sweden                12        0.8        90.7      156.49      94.4
United Kingdom        19        1.4         82       115.46      90.1

3b. Sum of squares and their square roots

Indicators                            max

                      1          2          3          4

Austria            15079.84   12882.25   7140.25     7.1289
Belgium            13248.01   15750.25   6756.84     3.6864
Bulgaria           1705.69    1383.84    7005.69     0.2401
Cyprus             9177.64    7621.29    7242.01     0.2209
Czech Republic     6448.09    5169.61    8390.56     2.1609
Denmark            14424.01   10506.25     5041      7.4529
Estonia            4542.76    4070.44    6756.84     1.6641
Finland            13642.24   12454.56   7430.44    13.8384
France             11642.41   14786.56   6955.56     4.0804
Germany            13363.36    11449     5490.81     6.9169
Greece             8892.49    10444.84   6740.41     0.3364
Hungary            4147.36      5041     6988.96       1
Ireland            18333.16   16952.04   7691.29     2.0449
Italy               10404     12034.09   5852.25     1.3924
Latvia             3283.29    2766.76      6400      0.3721
Lithuania          3831.61      3844     7938.81      0.64
Luxembourg         76341.69   30905.64   5299.84     2.6244
Malta              5821.69    7638.76      2809      0.2916
Netherlands         17956     13110.25   5806.44     2.6569
Poland             3180.96      3844     8335.69     0.3721
Portugal             5776     5069.44    2948.49     2.2801
Romania            1730.56    2520.04    6130.89     0.3481
Slovakia           5212.84    6272.64    8519.29     0.2209
Slovenia           8262.81    7123.36    8136.04     2.7556
Spain              10526.76   10732.96     3600      1.8225
Sweden              14400     12232.36   7726.41    14.0625
United Kingdom     13502.44    12100     6115.24     3.5344

[m.summation
  over (i=1)]
  [x.sup.2.sub.
  ij]              314877.7   258706.2   175249.1   84.1448
[square root of
  [m.summation
  over (i=1)]]
  [x.sup.2.sub.
  ij]              561.1397   508.6317   418.6276   9.173047

Indicators                            max

                      5          6          7          8

Austria            5198.41      1681      428.49    11046.01
Belgium            3893.76    1190.25      441      12343.21
Bulgaria             4096       2116      767.29    2520.04
Cyprus             5026.81    3003.04     416.16    8190.25
Czech Republic     4435.56    2265.76      361      5299.84
Denmark            6099.61      3249       361      19937.44
Estonia            4872.04    3893.76      576        6084
Finland            5055.21    3192.25      361      15450.49
France             4212.01    1459.24     349.69    12254.49
Germany            4998.49    2894.44     306.25    10753.69
Greece             3831.61    1831.84     272.25      8836
Hungary            3214.89     985.96     327.61    4637.61
Ireland            4569.76    2883.69     272.25    16281.76
Italy              3445.69    1183.36     349.69    11151.36
Latvia             4705.96    3528.36     605.16    5270.76
Lithuania          4134.49    2819.61     408.04    4173.16
Luxembourg         4019.56    1162.81     249.64    14184.81
Malta              3058.09     852.64     174.24    6209.44
Netherlands        5959.84      2809      285.61     10816
Poland             3504.64     998.56     306.25    4774.81
Portugal           4651.24    2580.64     380.25      7569
Romania              3481     1857.61     696.96    3708.81
Slovakia           3881.29    1536.64      529      4914.01
Slovenia           4705.96    1075.84     605.16    6773.29
Spain              4134.49    2079.36      625      9101.16
Sweden             5520.49    4914.01     262.44    13110.25
United Kingdom     5112.25      3364      210.25     10000

[m.summation
  over (i=1)]
  [x.sup.2.sub.
  ij]              119819.2   61408.67   10927.68   245391.7
[square root of
  [m.summation
  over (i=1)]]
  [x.sup.2.sub.
  ij]              346.149    247.8077   104.5355   495.3703

Indicators                                 min

                      9          10         11         12         13

Austria              144        0.81     12387.69   19804.93   9584.41
Belgium              225       10.89     8118.01    39505.54   6130.89
Bulgaria             441        8.41     3271.84    1032845    13595.56
Cyprus               256        0.25     34336.09   45011.87   5882.89
Czech Republic        81        4.84     6021.76     305986    7464.96
Denmark              144        0.25     9235.21    11172.49     6084
Estonia              361        2.89     2672.89    337224.1   4502.41
Finland              196        1.44     12166.09   52528.06   5975.29
France               169        8.41     8873.64    27350.54   7832.25
Germany              225       14.44     6021.76    22946.19   12521.61
Greece               400       12.96     15178.24   33047.6    11406.24
Hungary              144       12.96     4329.64    160608.6   17476.84
Ireland              256        2.56     15500.25   10635.8    10424.41
Italy                361        9.61     11427.61   20386.13   9063.04
Latvia               676        3.61     2171.56    94003.56   9063.04
Lithuania            400        1.44     2510.01    187056.3   14762.25
Luxembourg           169        2.56     9623.61    25131.76   7974.49
Malta                225        6.25      22201     39275.31   11406.24
Netherlands          121         1       9486.76    31371.49   7867.69
Poland               289        5.76     5012.64     160080    14810.89
Portugal             324       13.69     18523.21   38749.92   24273.64
Romania              529        5.76     2992.09    429798.2   27489.64
Slovakia             121       43.56     4251.04     290133    8482.41
Slovenia             144        3.61     10363.24   64155.82   19182.25
Spain                400         4       23286.76   33925.96   17715.61
Sweden               144        0.64     8226.49    24489.12   8911.36
United Kingdom       361        1.96       6724     13331.01   8118.01

[m.summation
  over (i=1)]
  [x.sup.2.sub.
  ij]                7306      184.56    274913.1   3550555    308002.3
[square root of
  [m.summation
  over (i=1)]]
  [x.sup.2.sub.
  ij]              85.47514   13.58529   524.3216   1884.292   554.9796

3c. Objectives divided by their square roots ([x.sup.*.sub.ij])
and ranks given to member States by Ratio System

Indicators                                 max

                      1          2          3          4          5

Austria            0.21884    0.223148   0.20185    0.29107    0.208292
Belgium            0.205118   0.24674    0.196356   0.209309   0.180269
Bulgaria            0.0736    0.073137   0.199939   0.053417   0.184891
Cyprus             0.170724   0.171637   0.203283   0.051237   0.204825
Czech              0.143102   0.14136    0.21881    0.160252   0.192403
Republic
Denmark            0.214029   0.201521   0.169602   0.297611   0.225625
Estonia            0.120113   0.125435   0.196356   0.140629   0.201647
Finland            0.208148   0.219412   0.205911   0.405536   0.205403
France             0.192287   0.239073   0.199222   0.22021    0.187491
Germany            0.206009   0.210368   0.177007   0.28671    0.204247
Greece             0.168051   0.200931   0.196117   0.063229   0.178825
Hungary            0.114766   0.13959     0.1997    0.109015   0.163802
Ireland            0.241295   0.255981   0.209494   0.155891   0.195292
Italy              0.181773   0.215677   0.18274    0.128638   0.16958
Latvia             0.102114   0.103415   0.191101   0.066499   0.198181
Lithuania          0.110311   0.121896   0.212838   0.087212   0.185758
Luxembourg         0.492391   0.345633   0.173902   0.176604   0.183158
Malta              0.135973   0.171834   0.126604   0.058868   0.159758
Netherlands         0.2388    0.225114   0.182023   0.177694   0.223025
Poland             0.10051    0.121896   0.218094   0.066499   0.171025
Portugal           0.135439   0.139983   0.12971    0.164613   0.197025
Romania            0.074135   0.098696   0.18704    0.064319   0.170447
Slovakia           0.128667   0.155712   0.220482   0.051237   0.17998
Slovenia           0.161992   0.165935   0.215466   0.180965   0.198181
Spain              0.182842   0.203684   0.143325   0.14717    0.185758
Sweden             0.213851   0.217446   0.209972   0.408806   0.214647
United Kingdom     0.207079   0.216266   0.186801   0.204948   0.206558

Indicators                      max                   min

                      6          7          8          9          10

Austria            0.165451   0.198019   0.212165   0.140392   0.066248
Belgium            0.139221   0.200889   0.224277   0.17549    0.24291
Bulgaria           0.185628   0.264982   0.101338   0.245685   0.213466
Cyprus             0.221139   0.195149   0.182692   0.187189   0.036805
Czech              0.192084   0.181756   0.146961   0.105294   0.16194
Republic
Denmark            0.230017   0.181756   0.285039   0.140392   0.036805
Estonia            0.251808   0.229587   0.157458   0.222287   0.125135
Finland            0.227999   0.181756   0.250923   0.16379    0.088331
France             0.154152   0.178887   0.223469   0.152091   0.213466
Germany            0.217104   0.167407   0.209338   0.17549    0.279714
Greece             0.172715   0.157841   0.189757   0.233986   0.264993
Hungary            0.126711   0.173147   0.137473   0.140392   0.264993
Ireland             0.2167    0.157841   0.257585   0.187189   0.117774
Italy              0.138817   0.178887   0.213174   0.222287   0.228188
Latvia             0.239702   0.235327   0.146557   0.304182   0.139857
Lithuania          0.214279   0.193236   0.130408   0.233986   0.088331
Luxembourg         0.137607   0.151145   0.240426   0.152091   0.117774
Malta              0.117833   0.126273   0.159073   0.17549    0.184023
Netherlands        0.213875   0.161667   0.209944   0.128692   0.073609
Poland             0.127518   0.167407   0.139492   0.198888   0.176662
Portugal           0.204998   0.186539   0.175626   0.210588   0.272353
Romania            0.173925   0.252546   0.122938   0.269084   0.176662
Slovakia           0.158187   0.220021   0.14151    0.128692   0.48582
Slovenia           0.132361   0.235327   0.166138   0.140392   0.139857
Spain              0.184014   0.239153   0.192583   0.233986   0.147218
Sweden             0.282881   0.154971   0.23114    0.140392   0.058887
United Kingdom     0.234052   0.138709   0.201869   0.222287   0.103053

Indicators                      min                   Sum        Rank

                      11         12         13

Austria            0.212274   0.074686   0.176403     0.41        4
Belgium            0.171841   0.105483   0.141086     0.11        12
Bulgaria           0.109093   0.539349   0.210098    -0.46        27
Cyprus             0.353409   0.112594   0.138203     0.04        15
Czech              0.148001   0.293564   0.155681     0.08        14
Republic
Denmark            0.183284   0.056095   0.140546     0.46        3
Estonia            0.098604   0.308185   0.120905     0.11        11
Finland            0.210367   0.121632   0.139284     0.47        2
France             0.179661   0.087768   0.159465     0.16        9
Germany            0.148001   0.080391   0.201629     0.17        8
Greece             0.23497    0.096477   0.19244     -0.24        22
Hungary            0.125495   0.212685   0.238207    -0.21        20
Ireland            0.23745    0.054731   0.183971     0.15        10
Italy              0.203883   0.075774   0.171538    -0.10        19
Latvia             0.088877   0.162714   0.171538     0.02        16
Lithuania          0.095552   0.229529   0.218927     0.02        17
Luxembourg         0.187099   0.084132   0.160907     0.23        7
Malta              0.284177   0.105175   0.19244     -0.34        24
Netherlands        0.185764   0.093998   0.159826     0.33        5
Poland             0.135032   0.212334   0.219287    -0.21        21
Portugal           0.259574   0.104469   0.280731    -0.28        23
Romania            0.104325   0.347924   0.29875     -0.37        26
Slovakia           0.124351   0.285858   0.165952    -0.35        25
Slovenia           0.194156   0.134422   0.249559     0.10        13
Spain              0.291043   0.09775    0.239829    -0.10        18
Sweden             0.172985   0.08305    0.170096     0.63        1
United Kingdom     0.156393   0.061275   0.162348     0.28        6

3d. Co-ordinates of the reference point
equal to the maximal objective values

Indicators                            max

                      1          2          3          4

[r.sub.j]           0.492      0.346      0.220      0.409

Indicators                            max

                      5          6          7          8

[r.sub.j]           0.226      0.289      0.265      0.101

Indicators                                 min

                      9          10         11         12         13

[r.sub.j]           0.105      0.037      0.089      0.055      0.121

3e. Comparison of the European Union
Member States (reference point approach)

     Indicators                       max

States                1          2          3          4

Austria             0.274      0.122      0.019      0.118
Belgium             0.287      0.099      0.024      0.199
Bulgaria            0.419      0.272      0.021      0.355
Cyprus              0.322      0.174      0.017      0.358
Czech Republic      0.349      0.204      0.002      0.249
Denmark             0.278      0.144      0.051      0.111
Estonia             0.372      0.220      0.024      0.268
Finland             0.284      0.126      0.015      0.003
France              0.300      0.107      0.021      0.189
Germany             0.286      0.135      0.043      0.122
Greece              0.324      0.145      0.024      0.346
Hungary             0.378      0.206      0.021      0.300
Ireland             0.251      0.090      0.011      0.253
Italy               0.311      0.130      0.038      0.280
Latvia              0.390      0.242      0.029      0.342
Lithuania           0.382      0.224      0.008      0.322
Luxembourg          0.000      0.000      0.047      0.232
Malta               0.356      0.174      0.094      0.350
Netherlands         0.254      0.121      0.038      0.231
Poland              0.392      0.224      0.002      0.342
Portugal            0.357      0.206      0.091      0.244
Romania             0.418      0.247      0.033      0.344
Slovakia            0.364      0.190      0.000      0.358
Slovenia            0.330      0.180      0.005      0.228
Spain               0.310      0.142      0.077      0.262
Sweden              0.279      0.128      0.011      0.000
United Kingdom      0.285      0.129      0.034      0.204

     Indicators                       max

States                5          6          7          8

Austria             0.017      0.117      0.067      0.111
Belgium             0.045      0.144      0.064      0.123
Bulgaria            0.041      0.097      0.000      0.000
Cyprus              0.021      0.062      0.070      0.081
Czech Republic      0.033      0.091      0.083      0.046
Denmark             0.000      0.053      0.083      0.184
Estonia             0.024      0.031      0.035      0.056
Finland             0.020      0.055      0.083      0.150
France              0.038      0.129      0.086      0.122
Germany             0.021      0.066      0.098      0.108
Greece              0.047      0.110      0.107      0.088
Hungary             0.062      0.156      0.092      0.036
Ireland             0.030      0.066      0.107      0.156
Italy               0.056      0.144      0.086      0.112
Latvia              0.027      0.043      0.030      0.045
Lithuania           0.040      0.069      0.072      0.029
Luxembourg          0.042      0.145      0.114      0.139
Malta               0.066      0.165      0.139      0.058
Netherlands         0.003      0.069      0.103      0.109
Poland              0.055      0.155      0.098      0.038
Portugal            0.029      0.078      0.078      0.074
Romania             0.055      0.109      0.012      0.022
Slovakia            0.046      0.125      0.045      0.040
Slovenia            0.027      0.151      0.030      0.065
Spain               0.040      0.099      0.026      0.091
Sweden              0.011      0.000      0.110      0.130
United Kingdom      0.019      0.049      0.126      0.101

     Indicators                            min

States                9          10         11         12         13

Austria             0.035      0.029      0.123      0.020      0.055
Belgium             0.070      0.206      0.083      0.051      0.020
Bulgaria            0.140      0.177      0.020      0.485      0.089
Cyprus              0.082      0.000      0.265      0.058      0.017
Czech Republic      0.000      0.125      0.059      0.239      0.035
Denmark             0.035      0.000      0.094      0.001      0.020
Estonia             0.117      0.088      0.010      0.253      0.000
Finland             0.058      0.052      0.121      0.067      0.018
France              0.047      0.177      0.091      0.033      0.039
Germany             0.070      0.243      0.059      0.026      0.081
Greece              0.129      0.228      0.146      0.042      0.072
Hungary             0.035      0.228      0.037      0.158      0.117
Ireland             0.082      0.081      0.149      0.000      0.063
Italy               0.117      0.191      0.115      0.021      0.051
Latvia              0.199      0.103      0.000      0.108      0.051
Lithuania           0.129      0.052      0.007      0.175      0.098
Luxembourg          0.047      0.081      0.098      0.029      0.040
Malta               0.070      0.147      0.195      0.050      0.072
Netherlands         0.023      0.037      0.097      0.039      0.039
Poland              0.094      0.140      0.046      0.158      0.098
Portugal            0.105      0.236      0.171      0.050      0.160

Romania             0.164      0.140      0.015      0.293      0.178
Slovakia            0.023      0.449      0.035      0.231      0.045
Slovenia            0.035      0.103      0.105      0.080      0.129
Spain               0.129      0.110      0.202      0.043      0.119
Sweden              0.035      0.022      0.084      0.028      0.049
United Kingdom      0.117      0.066      0.068      0.007      0.041

                         [MATHEMATICAL
     Indicators         EXPRESSION NOT
                       REPRODUCIBLE IN
States                     ASCII]          Rank

Austria                    0.274             4
Belgium                    0.287            10
Bulgaria                   0.485            27
Cyprus                     0.358            19
Czech Republic             0.349            16
Denmark                    0.278             5
Estonia                    0.372            20
Finland                    0.284             7
France                     0.300            11
Germany                    0.286             9
Greece                     0.346            15
Hungary                    0.378            21
Ireland                    0.253             2
Italy                      0.311            13
Latvia                     0.390            23
Lithuania                  0.382            22
Luxembourg                 0.232             1
Malta                      0.356            17
Netherlands                0.254             3
Poland                     0.392            24
Portugal                   0.357            18
Romania                    0.418            25
Slovakia                   0.449            26
Slovenia                   0.330            14
Spain                      0.310            12
Sweden                     0.279             6
United Kingdom             0.285             8

Annex B. Evaluation of European Union Member States positions by the Full Multiplicative Form, 2008.

Table 4. The Full Multiplicative Form and ranks of Member States

                     Product           Product
                  of indicators     of indicators
                 to be maximized   to be minimized   [U.sub.i]

                        1                 2          3 = 1 / 2

Austria          192420644761.40    1740568082.07    110.5505
Belgium          103065917843.27    7711427839.49    13.36535
Bulgaria          5138449637.17    20722075001.77     0.24797
Cyprus           26513321121.51     2183096112.04    12.14483
Czech Republic   46826535455.51     5345924257.53    8.759296
Denmark          201821467189.59    671241694.03     300.6688
Estonia          47664315121.05     5075390857.86     9.39126
Finland          319028067075.67    4080666299.17    78.18039
France           102475986062.26    5753950642.30    17.80967
Germany          160455757962.17    7775005703.73    20.63738
Greece           20060977991.57    16188712120.63    1.239195
Hungary          12318044689.18    10255871610.25    1.201072
Ireland          132425068198.35    4282237506.37    30.92427
Italy            38140676526.87     9037769153.42    4.220143
Latvia           14743678629.25     4878188580.55    3.022368
Lithuania        18867175664.30     4081726258.20    4.622352
Luxembourg       195680195388.08    3440384816.64    56.87742
Malta             4068061538.49     9319118419.08    0.436529
Netherlands      131774912143.09    1750556791.53     75.276
Poland            6375558632.30     9719201761.46    0.655976
Portugal         29974576864.33    24185467100.62    1.239363
Romania           6476554584.90    19987595112.18    0.324029
Slovakia         13933617613.29    16461161131.11    0.846454
Slovenia         63584904295.97     6701164476.72    9.488635
Spain            63111421084.32    14276015243.58    4.420801
Sweden           369129166535.18    1472795985.20    250.6316
United Kingdom   112996620064.33    2269090581.52     49.7982

                                      Rank

                    The Full
                 Multiplicative
                      Form        Ratio System   Reference Point

                       4               5                6

Austria                3               4                4
Belgium                11              12              10
Bulgaria               27              27              27
Cyprus                 12              15              19
Czech Republic         15              14              16
Denmark                1               3                5
Estonia                14              11              20
Finland                4               2                7
France                 10              9               11
Germany                9               8                9
Greece                 21              22              15
Hungary                22              20              21
Ireland                8               10               2
Italy                  18              19              13
Latvia                 19              16              23
Lithuania              16              17              22
Luxembourg             6               7                1
Malta                  25              24              17
Netherlands            5               5                3
Poland                 24              21              24
Portugal               20              23              18
Romania                26              26              25
Slovakia               23              25              26
Slovenia               13              13              14
Spain                  17              18              12
Sweden                 2               1                6
United Kingdom         7               6                8

Annex C. Final ranks of the European Union member States according to MULTIMOORA, 2008.

Table 5. The MULTIMOORA method and final ranks of Member States

                                           The Full
                              Reference    Multiplicative
                Ratio System  Point        Form            MULTIMOORA
Member State
                Rank  Group   Rank  Group  Rank  Group     Rank  Group

      1          2      3      4      5     6      7        8      9

Austria          4      1      4      1     3      1        3      1
Belgium          12     2      10     2     11     2        11     2
Bulgaria         27     3      27     3     27     3        27     3
Cyprus           15     2      19     3     12     2        15     2
Czech Republic   14     2      16     2     15     2        13     2
Denmark          3      1      5      1     1      1        2      1
Estonia          11     2      20     3     14     2        14     2
Finland          2      1      7      1     4      1        4      1
France           9      1      11     2     10     2        10     2
Germany          8      1      9      1     9      1        9      1
Greece           22     3      15     2     21     3        19     3
Hungary          20     3      21     3     22     3        22     3
Ireland          10     2      2      1     8      1        7      1
Italy            19     3      13     2     18     2        17     2
Latvia           16     2      23     3     19     3        20     3
Lithuania        17     2      22     3     16     2        18     2
Luxembourg       7      1      1      1     6      1        6      1
Malta            24     3      17     2     25     3        23     3
Netherlands      5      1      3      1     5      1        5      1
Poland           21     3      24     3     24     3        24     3
Portugal         23     3      18     2     20     3        21     3
Romania          26     3      25     3     26     3        26     3
Slovakia         25     3      26     3     23     3        25     3
Slovenia         13     2      14     2     13     2        12     2
Spain            18     2      12     2     17     2        16     2
Sweden           1      1      6      1     2      1        1      1
United Kingdom   6      1      8      1     7      1        8      1

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Alvydas Balezentis (1), Tomas Balezentis (2), Romualdas Valkauskas (3)

(1) Mykolas Romeris University, Valakupiu g. 5, LT-10101 Vilnius, Lithuania

(2, 3) Vilnius University, Sauletekio al. 9, LT-10222 Vilnius, Lithuania

E-mails: (1) a.balezentis@gmail.com; (2) t.balezentis@gmail.com;

(3) romualdas.valkauskas@ef.vu.lt

Received 14 April 2010; accepted 20 October 2010

Alvydas BALEZENTIS. Dr (HP), Professor of the Department of Strategic Management at Mykolas Romeris University. While working at the Parliament of the Republic of Lithuania, Ministry of Agriculture, and Institute of Agrarian Economics he contributed to creation and fostering of the Lithuanian rural development policy at various levels. Major areas of interest: innovatics, state management, strategic management, rural development, regional development.

Tomas BALEZENTIS studies at the Vilnius University. His work experience includes traineeship in the European Parliament and work at Training Centre of the Ministry of Finance.

Romualdas VALKAUSKAS. Dr, Associated Professor of the Department of Quantitative Methods and Modelling, Faculty of Economics, Vilnius University. Major areas of interest: quantitative methods in social sciences, economic statistics, history of statistics theory and practice.

Table 1. Structural indicators used in evaluation
of Lithuania's position in the EU

                                                        Desirable
                       Structural indicator               value

I. General economic background
  1            GDP per capita in PPS (EU-27 = 100)         Max
  2          Labour productivity per person employed       Max
II. Employment
  3                      Employment rate                   Max
  4              Employment rate of older workers          Max
III. Innovation and research
  5              Youth education attainment level          Max
  6             Gross domestic expenditure on R&D          Max
IV. Economic reform
  7                    Business investment                 Max
  8                  Comparative price levels              Min
V. Social cohesion
  9                  At-risk-of-poverty rate               Min
  10               Long-term unemployment rate             Min
VI. Environment
  11                 Greenhouse gas emissions              Min
  12             Energy intensity of the economy           Min
  13         Index of inland freight transport volume      Min

Table 2. Ranks of European Union Member States according to the
reference point (RP) approach and ratio system (RS), 2008

                                              Rank
                  [MATHEMATICAL EXPRESSION
Member State     NOT REPRODUCIBLE IN ASCII]   RP    RS

Luxembourg                 0.232               7     1
Ireland                    0.253              10     2
Netherlands                0.254               5     3
Austria                    0.274               4     4
Denmark                    0.278               3     5
Sweden                     0.279               1     6
Finland                    0.284               2     7
United Kingdom             0.285               6     8
Germany                    0.286               8     9
Belgium                    0.287              12    10
France                     0.300               9    11
Spain                      0.310              18    12
Italy                      0.311              19    13
Slovenia                   0.330              13    14
Greece                     0.346              22    15
Czech Republic             0.349              14    16
Malta                      0.356              24    17
Portugal                   0.357              23    18
Cyprus                     0.358              15    19
Estonia                    0.372              11    20
Hungary                    0.378              20    21
Lithuania                  0.382              17    22
Latvia                     0.390              16    23
Poland                     0.392              21    24
Romania                    0.418              26    25
Slovakia                   0.449              25    26
Bulgaria                   0.485              27    27