The main achievements of the EU structural funds 2007-2013 in the EU member states: efficiency analysis of transport sector

E QUILIBRIUM Quarterly Journal of Economics and Economic Policy Volume 13 Issue 2 June 2018 p-ISSN 1689-765X, e-ISSN 2353-3293 www.economic-policy.pl ORIGINAL PAPER Citation: Melecký, L. (2018). The main achievements of the EU structural funds 2007– 2013 in the EU member states: efficiency analysis of transport sector. Equilibrium. Quarterly Journal of Economics and Economic Policy, 13(2), 285–306. doi: 10.24136/eq.2018.015 Contact: lukas.me[email protected], Department of European Integration, Faculty of Economics, VŠB-Technical University of Ostrava, Sokolská třída 33, 702 00 Ostrava 1, Czech Republic Received: 24 April 2017; Revised: 5 December 2017; Accepted: 8 January 2018 Lukáš Melecký VŠB-Technical University of Ostrava, Czech Republic The main achievements of the EU structural funds 2007–2013 in the EU member states: efficiency analysis of transport sector JEL Classification: C67; O11; O52; R11; R12 Keywords: DEA; efficiency; European Regional Development Fund, ex-post evaluation; Cohesion Fund Abstract Research background: The European Union currently provides financial support to the Member States through various financial tools from European Structural and Investment Funds 2014–2020, and previously from the EU Structural Funds. In both terminologies, the funds represent the main instrument of EU Cohesion Policy to sustain territorial development, to increase competitiveness and to eliminate regional disparities. The overall impact of EU Funds depends on the structure of funding and absorption capacity of the country. Purpose of the article: The efficiency of funding across the EU Member States is a fundamental issue for EU development as a whole. The Author considers deter-mining the efficiency of EU Funds as an issue of high importance, and therefore this paper provides a contribution to the debate on the role of EU Cohesion Policy in the Member States. The paper focuses on territorial effects of relevant EU Funds in programming period 2007–2013 in infrastructure through efficiency analysis. Methods: Efficiency analysis is based on data at the country level, originating from ex-post evaluation of Cohesion Policy programmes 2007–2013 and representing the input and output variables to analyse whether the goal of fostering growth in the target countries have been achieved with the funds provided, and whether or not more resources generated stronger growth effects in transport accessibility. The paper deals with comparative cross-country analysis, descriptive analysis of dataset and multiple-criteria approach of Data Envelopment Equilibrium. Quarterly Journal of Economics and Economic Policy, 13(2), 285–306 286 Analysis (DEA) in the form of output-oriented BCC VRS model of efficiency and outputoriented APM VRS subsequently model of super-efficiency. Findings & Value added: The paper aims to test the factors of two inputs and five outputs, trying to elucidate the differences obtained by the Member States in effective use of the European Regional Development Fund and the Cohesion Fund in the transport sector. The paper determines if the countries have been more efficient in increasing their levels of competitive advantages linked with transport. Preliminary results reveal that most countries with a lower amount of funding achieve higher efficiency, especially countries in a group of socalled “old EU Member States”, i.e. group EU15. Introduction The establishment of the EU market at the beginning of new area; the European Union (EU) Member States currently enjoy many benefits in this respect: a free market, effective trading, enhanced security, economic cohesion, sustainable development, protection of human rights, creation of jobs etc. The main goals of the EU are to boost economic and social progress and eliminate the existing differences between the standards of living of Member States and in their regions. The European Structural and Investment Funds (ESIF) are basic instruments of the EU Cohesion Policy to promote the overall harmonious development of the EU, to reduce disparities between the levels of development of the various regions, and to strengthen its economic, social and territorial cohesion. ESIF consist of the following five funds, i.e. European regional development fund (ERDF), European social fund (ESF), and Cohesion Fund (CF), European agricultural fund for rural development (EAFRD) and European maritime and fisheries fund (EMFF). The EU devotes an important part of its resources to financing regional development projects through ESIF, which provide subsidy aid to the Member States and their regions based on their economic situation, mainly based on the particular region's GDP. The EU defines subsidies as "any aid granted by the State or through State resources in any form whatsoever" based on Rubini's analysis (Rubini, 2010). How efficiently the Member States apply the European funds is a basic and pivotal topic for success and continuity of implementation of the EU Cohesion Policy, and especially so in the context of the economic crisis and the growing number of regions with low levels of development that the incorporation of so-called new countries into the EU has assumed. Such circumstances have forced the EU to make huge economic efforts to maintain and increase the resources for the funds, and so it is vital for the European authorities to know how effective the funds are being applied (Enguix et al., 2012). The efficiency of the EU funds is an issue of high importance, and Equilibrium. Quarterly Journal of Economics and Economic Policy, 13(2), 285–306 287 this paper provides a contribution to debate on the role of the EU Cohesion Policy in the EU Member States. The paper focuses on the territorial effects of the EU Funds in programming period 2007–2013 in infrastructure through transport efficiency analysis. Efficiency analysis is based on national data originating from the expost evaluation of Cohesion Policy programmes 2007–2013 representing the input and output variables to analyse whether the goal of fostering growth in the evaluated countries have been achieved with the European funds provided and whether or not more sources created stronger growth effects and impacts in transport accessibility. By analysing the amounts granted to each Member State, the efficiency level of using funds is observed. The paper deals with comparative cross-country analysis, descriptive data analysis and multiple-criteria approach of Data Envelopment Analysis (DEA) in the form of output-oriented BCC VRS model. The paper aims to test several factors in the form of two inputs and five outputs, trying to elucidate the differences obtained by the EU Member States in effective use of ERDF and CF in the transport sector. The paper determines if the countries have been more efficient in increasing their levels of competitive advantages linked with transport. Additionally, development challenges are discussed for improvement of the efficiency effect of the EU Structural Funds on the national performance in the transport sector. The paper is structured as follows. Section 2 briefly introduces the EU Cohesion Policy, reviews the methods for evaluation of the EU funds and highlights the importance of transport for development. Section 3 briefly introduces history and background of DEA methodology and proposes the efficiency and super-efficiency approaches. Section 3 describes the empirical background, i.e. inputs, output, evaluated units (DMUs), and data source. Section 4 presents and discusses the main and important empirical results. Section 5 compares the findings in the paper with the findings of other authors. Conclusions are summarised in the last section. Theoretical background The goals of the EU Cohesion Policy lied in the perception of fact that a common market or internal market requires a certain degree of homogeneity in economic development of countries, which is not necessarily an automatic outcome of the European integration process but, eventually, has to be assisted by active policy interventions (both European or/and national interventions). Therefore, the EU Cohesion Policy aims at increasing competitiveness and the level of development and reducing economic and so- Equilibrium. Quarterly Journal of Economics and Economic Policy, 13(2), 285–306 288 cial disparities between the different regions involved. The means by which this goal can be supposedly reached are the EU Funds (formerly Structural Funds, and now ESI Funds or ESIF). Given their impressive amount, their impact has been analysed from several different perspectives. Theoretical approaches analysing the impact of economic integration and the EU Cohesion Policy can be classified as growth theories and trade theories, distinguishing between classical and new approaches. In part, these approaches have diametric political implications (Mohl & Hagen, 2010). It is not possible to identify the correct theory for the evaluation of the EU Cohesion Policy. It is striking that almost all empirical studies investigating the impact of the EU funds on regional economic growth are based on a neoclassical growth model, where funds mainly correspond to investments, which are endogenous in the neoclassical growth framework. As the EU budget and financial perspective of each programming period become tighter and major recipients of European regional transfers struggle with financial and economic crises, questions and discussions about the proper utilization and effectiveness of financial transfers from the central EU budget to EU’s poorest countries and subsequently regions are hot topics of policy debated. Spending the money allocated through funds was placed at the top of the list of most national governments. In order to boost the absorption rate, European institutions implemented a set of measures (Healy & Bristow, 2013; Katsarova, 2013). Investigating the impact of the EU funds on the economic growth and convergence process is thus a broad research topic (for a summary of the newest research see Table 1). Although studies on the efficiency of the EU Cohesion Policy through funds have not provided conclusive findings (see overview in Mohl & Hagen, 2010; Rogalska et al., 2017), it is useful to determine whether the huge amounts of resources employed could have given better results. Transport, resp. infrastructure as a whole can be seen as a baseline of European integration process from its beginning. This problem is one of the first and key-topicality issues to be listed to the EU common policy activities. The Treaty of Rome from 1957 (EUR-Lex, 1957) included the statement that transport or widely perceived infrastructure will have a big impact on securing three (free movement of goods, free movement of services and free movement of people) of four freedoms which internal market constitutes and ensures. Implementation of freedom could result in an effectively functioning transport network, i.e. one baseline of the whole infrastructure system. The key objectives: deeper common or internal market integration between the EU countries, the construction of efficient and big transport infrastructures, were seen as a needed assumption towards fulfilment of this goal. At the end of the 1990s, the EU has chosen a priority list Equilibrium. Quarterly Journal of Economics and Economic Policy, 13(2), 285–306 289 of key investments in transport, so-called the Trans European Network (TEN) investments. The EU transport policy ensures two key objectives, i.e. to decrease trade costs (in line with the aim to build the EU internal market; and to promote the socio-economic development and structural adjustment of lagging regions and less-developed regions. Investments in infrastructure cause economic growth, supports both internal and external trade, higher employment rate and globally in the European context — increase the quality of life of European inhabitants and other favourable aspects. Therefore, the attractiveness of territory can be boosted by updating and upgrading the equipment in technical and social infrastructure, especially transport infrastructure — therefore it can get lower when distance, time and cost are taken into consideration. With respect to those facts, territories which can be characterized as those with highly developed transport infrastructure, are more attractive for investors, and in this can be seen as their competitive advantage too (see Górniak, 2016; Sucháček, 2013). The appropriate level in terms of the quality and the scope of transport infrastructure in the formed road, railway, air and water connections result in constant and better-increasing movement of people and goods and factually tend to improve the quality of life through the availability of transport services. Research methodology The efficiency of the EU Cohesion Policy elements is an issue of high relevance, and it is the main aim of this paper which provides a contribution to the debate on the role of the EU Cohesion Policy in the EU Member States. The EU Cohesion Policy should be effective, as is the case for transport policy. The economic performance of transport infrastructure can be improved by investing in transport infrastructure, by selecting investments more carefully, and by using the existing infrastructure better. Whether interregional transport infrastructure is beneficial in terms of welfare and whether it generates economic growth at the macroeconomic level are two different issues. Assessing the benefits of transport investments ex-ante, but also ex-post is difficult. There is a number of potential problems with evaluations which mostly relate to the limited availability of sufficient data in the cross-sectional as well as the time dimensions, and to the methods applied. Currently, the trend in efficiency studies revolves around the application of non-parametric models, since they allow to consider a multiplicity of outputs and inputs in the analysis, and thus make less severe demands on the whole and the frontier of production. Efficiency measurement has been Equilibrium. Quarterly Journal of Economics and Economic Policy, 13(2), 285–306 290 the challenge of many entities interested in improving their productivity. In 1957, Farrell in his study investigated the issue of measuring efficiency and supported the relevance of efficiency evaluation for economic policymakers (Farrell, 1957). One of the reasons that all attempts to solve the problem have failed is the failure to combine the measurement of multiple inputs into any desirable outputs (Cook & Seiford, 2009; Caves et al., 1982). During subsequent years, methods, tools and techniques for efficiency measuring have become more frequent, popular and improved, as mentioned Melecký (2017). Between the non-parametric techniques, Data Envelopment Analysis (DEA) is the most accepted method. DEA is the data-oriented approach for providing a relative efficiency assessment and evaluating the performance of a set of peer entities called Decision Making Units (DMUs). DEA provides a single measure and in a simple way deals with multiple inputs and multiple outputs, and its goal is to divide DMU into an efficient group or inefficient group, based on size and quantity of consumed inputs and produced outputs. In recent years, we have seen a great variety of applications of DEA for evaluating the performances of many different kinds of entities engaged in many different activities (such as banks, hospitals, universities, cities, courts, business firms, and others, including the performance of countries, regions, etc.), for more information see the latest examples of sectoral applications DEA methodology; e.g. Grmanová and Pukala (2018) or Balcerzak et al. (2017). Evaluation of territorial units is a topic of interest in this paper, for more DEA works about national or regional efficiency (see Staníčková, 2017, 2014); or previous works of the author, e.g. Meleck (2013); Melecký and Staníčková (2014). Further, DEA has proved especially valuable in cases where we have non-marketed inputs or outputs and/or cannot be derived or agreed upon between different DMUs. Various types of DEA models can be used, depending upon the problem at hand. Used DEA model can be distinguished by the scale and orientation of the model. With respect to the orientation of economic policy and decisions of policymakers to achieve better efficiency of activities, governments' priorities are to adjust their outputs rather than inputs, therefore an outputoriented (OO) DEA model, rather than an input-oriented (IO) one, is convenient and also used in the paper. From this point of view, here it is necessary to note that most of the studies mentioned above used only one common type of DEA model — an output-oriented model, which is considered suitable for measuring the efficiency of territories in the case of links between competitiveness and efficiency. The next step is Returns to Scale (RTS) estimation and based on RTS estimation and classifications of countries into RTS, then DEA model choice will is characterized, i.e. in most of Equilibrium. Quarterly Journal of Economics and Economic Policy, 13(2), 285–306 291 the countries variable returns to scale (VRS) were estimated and thus used in this paper. For calculations of efficiency, it is used for output-oriented BCC Banker-Charnes-Cooper) model with VRS. The principle for calculating the efficiency scores can be explained briefly using the mathematical formula of the model (1) (Cook & Seiford, 2009): max g ε( ), q φ = + T + T - e s + e s subject to , q = - X λ+ s x , q q φ = + Y λs y 1, = T eλ , ≥ + - λ,s ,s 0 where g is the coefficient of efficiency of unit U q ; ϕ q is radial variable indicates required rate of increase of output; ε is infinitesimal constant; e T λ is convexity condition; s + , and s − are vectors of slack variables for inputs and outputs; λ represent vector of weights assigned to individual units; x q means vector of input of unit U q ; y q means vector of output of unit U q ; X is input matrix; Y is output matrix. In OO BCC model, the coefficient of efficient DMU equals 1, but the coefficient of inefficient DMU is greater than 1. In BCC model, the coefficients of efficient units are equal to 1. With respect to the selected DEA model and the relationship between the number of units and the number of inputs and outputs in results — a number of efficient units can be relatively large. Due to the possibility of evaluated DMUs' classification, it is used in Andersen-Petersen's model (APM) of super-efficiency. Following OO VRS model is a dual version of APM (2) (Andersen & Petersen, 1993): max q i i g ε( ), φ + − = + T T e s + e s subject to 1 , nij j i iq j x λs x = + =  1 , n+ kj j i q kq j y λs y φ = − =  1, = T eλ (1) (2) Equilibrium. Quarterly Journal of Economics and Economic Policy, 13(2), 285–306 292 0, q λ = , , 0, + - j k i s s λ ≥ 1,2,..., , ; 1,2,..., ; 1,2,..., . j n j q k r i m = ≠ = = where x ij and y rj are i-th inputs and r-th outputs of DMUj; ϕ k is efficiency coefficient of observed DMU k ; λ j is the dual weight which shows DMU j significance in the definition of an input-output mix of the hypothetical composite unit, DMU k directly comparing with. The rate of the efficiency of inefficient units (ϕ k >1) is identical to model (1); for units identified as efficient in the model (1), provides OO APM (2) rate of super-efficiency lower than 1, i.e. ϕ k ≤1. Based on the mentioned information, it is considered as appropriate to apply DEA mathematical technique, which allows calculating the technical efficiency and inefficiency of using the funds for enhancing transport accessibility and transport sector as a whole in the EU Member States. This paper covered 27 Member States of the EU drawing money from the EU during the programming period 2007–2013. With the aim to analyse and evaluate differences in terms of transport infrastructure, the paper utilized data from reports Evaluations of the 2007–2013 programming period: Ex Post Evaluation of the ERDF and CF: Key outcomes of Cohesion Policy in 2007–2013 (European Commission, 2016). The efficiency analysis is based on data at the country level originating from ex-post evaluation of the EU Cohesion Policy programmes 2007–2013, representing the input and output variables to analyse whether the goal of fostering growth in the target countries have been achieved with the funds provided, and whether or not more resources generated stronger growth effects in transport accessibility. Inputs represent two variables Road (in billion EUR, I1) and Rail (in billion EUR, I2); outputs represent five variables km of new roads (O1), km of new TEN roads (O2), km of reconstructed roads (O3), km of TEN railroads (O4) and km of reconstructed railroads (O5). In Table 2, data for 27 Member States (DMUs) with two inputs and five outputs are demonstrated in the numerical example. With respect to data availability and the need for relevancy of gained results, data for 23 Member States come into efficiency analysis through DEA method, i.e. without AT, DK and LU with zero values of indicators, and also without BE only with the one-known value of indicators. For other countries, the values are available for all of the indicators, or some indicators show missing data and therefore report zero values. In the case when the number of inputs and outputs together is relatively high with respect to a number of evaluated units, as a result, most of the evaluated units will result efficiently, i.e. units behave efficiently. Thus, the Equilibrium. Quarterly Journal of Economics and Economic Policy, 13(2), 285–306 293 getting results are not expressed as relevant. There is a rule of thumb expressing the relationship between a number of DMUs and number of efficiency measures, found by Toloo et al. (2015), that in nearly all of cases the number of inputs and outputs together does not exceed 6. Suppose there are n DMUs consuming m inputs to produce s outputs. A simple calculation expresses that when m ≤ 6 and s ≤ 6, then 3 (m + s) ≥ m × s. As a result, in this paper formula (3) is applied: 3( ). n m s ≥ + (3) In the paper, DMUs number is three times higher than the sum of input and outputs together, i.e. 23 ≥ 3 (2 + 5), 23 ≥ 3 (7), 23 ≥ 21, thus the rule has been proved for the DEA application in the paper. DEA Frontier software tool for calculating issues of linear programming is applied in the paper. Results In the first step, OO BCC VRS model of efficiency should be solved for the EU23 Member States. In this way, efficient and inefficient countries can be determined. In the second step, OO APM model of super-efficiency should be solved for all the EU23 Member States. Based on the results of Andersen-Petersen's model, efficient and inefficient countries can be determined and ranked. Complete results of efficiency analysis of the EU23 Member States are represented in Table 3. The paper reveals that only 12 countries are efficient in OO BCC VRS efficiency model, and all other 11 countries are inefficient. Output oriented BCC VRS model of efficiency and OO Andersen-Petersen's model of super-efficiency singled out productive units which are efficient; among the countries of this group there are: Bulgaria (BG), Spain (ES), France (FR), Italy (IT), Cyprus (CY), Malta (MT), Netherlands (NL), Poland (PL), Portugal (PT), Romani (RO), Finland (FI) and Sweden (SE). Efficient countries are highlighted in bold in Table 3. In this case, the efficiency boundary is a straight line cutting through these DMUs. All other units are inefficient, i.e. they fall short of the efficiency curve. Inefficient countries are the Czech Republic (CZ), Germany (DE), Estonia (EE), Ireland (IE), Greece (EL), Latvia (LV), Lithuania (LT), Hungary (HU), Slovenia (SI), Slovakia (SK) and United Kingdom (UK). Inefficient countries are highlighted in italics in Table 3. In Table 3, the final results of OO APM model of super-efficiency are highlighted by the visual approach in the form of a colour method called traffic light. The range of colours can Equilibrium. Quarterly Journal of Economics and Economic Policy, 13(2), 285–306 300 the production possibility frontier, i.e. technical efficiency or superefficiency. This cannot be a positive fact, as it shows countries getting efficiency based on shifts in sources of competitiveness, i.e. quantitative type of competitive advantages. The character of technical efficiency thus contributes to the quantitative type of economic growth having limits in sources and their utilization. In the framework of the evaluation, it is necessary to move from efficiency to effectiveness, i.e. instead of conducting economic policy activities to their own setting and objectives, but this cannot be done by the DEA method. From the point of view of future research, it is necessary to rely on the evaluation of the relationship between outputoutcome (effectiveness) and not input-output (efficiency), which the DEA method evaluates. Not only the reconstructed or newly built technical and transport infrastructure, i.e. its factual or physical existence but, above all, the possibilities of its proper use in activities generating added value for the economy, i.e. qualitative competitive advantage, is the key for the knowledge economy. This should be the topic of future research, i.e. how the factor endowment of the given economy contributes to its growth and how the economy can use not only its quantitative but especially qualitative competitive advantages. 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European Parliament, Working Study IP/B/REGI/FWC/2010-002/LOT1/C1/SC1. Toloo, M., Barat, M., & Masoumzadeh, A. (2015). Selective measures in data envelopment analysis. Annals of Operations Research, 226(1). doi: 10.1007/s10479-014-1714-3. Acknowledgements The paper is supported by the Faculty of Economics, VŠB-TUO (project no. IP1003311) and the SGS project (SP2017/111), and the Operational Programme Education for Competitiveness – Project No. CZ.1.07/2.3.00/20.0296. Annex Table 1. Literature review of the EU funds evaluation Title of the paper Author(s) Year Comparison of investment costs for companies using EU Structural Funds and investment incentives Brzakova, K. & Pridalova, K. 2016 Review of financial support from EU Structural Funds to sustainable energy in the Baltic States Štreimikienė, D. 2016 Structural Funds And Economic Crises: Romania’s Absorption Paradox Tatulescu, A. & Patruti, A. 2014 A simulation of the impact of withdrawal European funds on Andalusian economy using a dynamic CGE model: 2014–20 Cardenete, M.A. & Delgado, M.C. 2014 Absorption of European Funds by Romania Lucian, P. 2014 An Examination of the Romanian State Budget Regarding the European Funds: Co-Financing Provisions Gherman, M.G. 2013 Absorption of Structural Funds International Comparisons and Correlations Hapenciuc, C.V., Morosan, A.A. & Arionesei, G. 2013 An Impact Analysis of the European Structural Funds on the Variation of the Rate of Employment and Productivity in Objective 1 Regions Enguix, M.R.M., García, J.G. & Gallego, J.C.G. 2012 EU Structural Instruments key component in improving the Romanian macroeconomic stability? Dragan, G. 2012 Too much of a good thing? On the growth effects of the EU’s regional policy Becker, S.O., Egger, P.H. & von Ehrlich, M. 2012 Structural funds and the economic divide in Italy Aiello, F. & Pupo, V. 2011 Do EU structural funds promote regional growth? New evidence from various panel data approaches Mohl, P., & Hagen, T. 2010 Going NUTS: The effect of EU Structural Funds on regional performance Becker, S.O., Egger, P.H. & von Ehrlich, M. 2010 Table 2. Numerical values of input (I) and output (O) indicators for DEA analysis: initial values Country I1 I2 O1 O2 O3 O4 O5 BE 14.210 0.000 0.000 0.000 0.000 0.000 0.000 BG 1078.845 341.391 175.000 173.000 1040.480 234.000 234.000 CZ 3796.887 2900.935 311.770 110.750 2017.880 294.000 369.060 DK 0.000 0.000 0.000 0.000 0.000 0.000 0.000 DE 2082.771 766.349 293.520 100.700 769.900 158.800 248.600 EE 290.406 185.308 69.740 0.000 205.000 0.000 0.000 IE 63.500 16.750 0.000 0.000 33.000 0.000 0.000 EL 4602.952 530.576 144.400 144.400 2645.900 11.400 60.300 ES 2296.862 4139.081 509.750 124.720 2458.100 0.000 1.210 FR 171.837 202.326 28.000 0.000 0.000 57.000 549.870 IT 835.378 2185.181 94.270 0.000 188.070 733.190 1034.960 CY 33.209 0.000 2.900 3.000 3.420 0.000 0.000 LV 483.041 256.300 0.000 0.000 636.570 0.000 0.000 LT 681.253 580.370 0.000 0.000 1473.440 0.000 0.000 LU 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Table 2. Continued Country I1 I2 O1 O2 O3 O4 O5 HU 3276.672 1720.107 501.980 135.200 2521.170 20.000 216.000 MT 103.432 0.000 0.000 0.000 13.290 0.000 0.000 NL 8.450 0.424 0.000 0.000 0.000 0.000 0.000 AT 0.000 0.000 0.000 0.000 0.000 0.000 0.000 PL 15910.620 5479.094 1886.270 1056.010 7216.230 123.650 482.060 PT 813.206 375.641 300.410 138.220 2996.660 47.550 385.500 RO 3377.417 1692.047 367.900 313.600 1892.820 21.800 122.260 SI 404.809 434.568 59.980 52.420 10.650 89.460 89.460 SK 1888.527 1028.793 79.500 40.570 1625.690 64.310 64.310 FI 14.776 10.198 0.000 0.000 0.000 0.000 0.000 SE 9.272 11.605 36.000 0.000 14.000 0.000 81.000 UK 253.055 65.432 13.000 7.000 11.000 2.000 2.000 Note: I-1: Road (mld. EUR), I-2: Rail (mld. EUR), O-1: km of new roads, O-2: km of new TEN roads, O-3: km of reconstructed roads, O-4: km of TEN railroads, O-5: km of reconstructed railroads Source: own elaboration based on European Commission (2016). Table 3. Relative the EU countries' DEA efficiency DMU Efficiency model Super-efficiency model Final order based on results of super-efficiency model EU OO BCC VRS OO APM VRS Rank of countries No. EU OO APM VRS BG 1.000 0.347 1 FI 0.007 CZ 1.267 1.267 2 SE 0.008 DE 1.143 1.143 3 NL 0.015 EE 1.842 1.842 4 CY 0.214 IE 4.112 4.112 5 PL 0.223 EL 1.159 1.159 6 IT 0.254 ES 1.000 0.895 7 MT 0.257 FR 1.000 0.335 8 FR 0.335 IT 1.000 0.254 9 PT 0.345 CY 1.000 0.214 10 BG 0.347 LV 2.783 2.783 11 ES 0.895 LT 1.702 1.702 12 RO 0.988 HU 1.131 1.131 13 HU 1.131 MT 1.000 0.257 14 DE 1.143 NL 1.000 0.015 15 SI 1.148 PL 1.000 0.223 16 EL 1.159 PT 1.000 0.345 17 CZ 1.267 RO 1.000 0.988 18 LT 1.702 SI 1.148 1.148 19 EE 1.842 SK 1.860 1.860 20 SK 1.860 FI 1.000 0.007 21 LV 2.783 SE 1.000 0.008 22 IE 4.112 UK 4.214 4.214 23 UK 4.214 Table 4. Numerical values of input (I) and output (O) indicators for DEA analysis: efficient targets Country I1 I2 O1 O2 O3 O4 O5 BG 1078.845 341.391 175.000 173.000 1040.480 234.000 234.000 CZ 3796.887 2199.226 519.539 256.480 2557.546 372.628 698.245 DE 2082.771 766.349 335.564 223.901 2049.805 181.547 313.335 EE 290.406 138.908 128.464 48.335 1057.030 16.628 187.483 IE 63.500 16.750 15.600 8.477 135.700 2.111 17.113 EL 1282.721 530.576 345.060 167.330 3066.051 55.554 383.837 ES 2296.862 4139.081 509.750 124.720 2458.100 0.000 1.210 FR 171.837 202.326 28.000 0.000 0.000 57.000 549.870 IT 835.378 2185.181 94.270 0.000 188.070 733.190 1034.960 CY 33.209 0.000 2.900 3.000 3.420 0.000 0.000 LV 483.041 226.137 191.820 81.455 1771.721 28.022 260.446 LT 681.253 315.890 257.011 115.533 2507.102 39.745 335.521 HU 3276.672 1720.107 567.569 271.710 3535.631 51.334 339.480 MT 103.432 0.000 0.000 0.000 13.290 0.000 0.000 NL 8.450 0.424 0.000 0.000 0.000 0.000 0.000 PL 15910.622 5479.094 1886.270 1056.010 7216.230 123.650 482.060 PT 813.206 375.641 300.410 138.220 2996.660 47.550 385.500 RO 3377.417 1692.047 367.900 313.600 1892.820 21.800 122.260 SI 404.809 184.079 68.835 60.159 369.320 102.668 123.229 SK 1888.527 914.309 393.099 190.023 3023.583 119.609 455.496 FI 9.169 10.198 31.469 0.000 12.238 0.000 70.806 SE 9.272 11.605 36.000 0.000 14.000 0.000 81.000 UK 192.377 65.432 54.778 29.496 185.078 39.334 95.493 Note: I-1: Road (mld. EUR), I-2: Rail (mld. EUR), O-1: km of new roads, O-2: km of new TEN roads, O-3: km of reconstructed roads, O-4: km of TEN railroads, O-5: km of reconstructed railroads Source: own elaboration based on European Commission (2016).