Comparative Assessment of Efficiency in Attracting European Funds by Regions of Eastern European Countries

Lupu, Dan; Asandului, Mircea Article Comparative Assessment of Efficiency in Attracting European Funds by Regions of Eastern European Countries CES Working Papers Provided in Cooperation with: Centre for European Studies, Alexandru Ioan Cuza University Suggested Citation: Lupu, Dan; Asandului, Mircea (2015) : Comparative Assessment of Efficiency in Attracting European Funds by Regions of Eastern European Countries, CES Working Papers, ISSN 2067-7693, Alexandru Ioan Cuza University of Iasi, Centre for European Studies, Iasi, Vol. 7, Iss. 2a, pp. 531-544 This Version is available at: https://hdl.handle.net/10419/198404 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ CES Working Papers – Volume VII, Issue 2A 531 COMPARATIVE ASSESSMENT OF EFFICIENCY IN ATTRACTING EUROPEAN FUNDS BY REGIONS OF EASTERN EUROPEAN COUNTRIES Dan LUPU* Mircea ASANDULUI** Abstract: In the current decentralization reforms and severe budget constraints faced by Eastern European countries, we consider as imperative to analyze the effectiveness of structural funds management at regional level. Therefore, the purpose of this study was to estimate the technical efficiency of attracting structural funds by the regions in Poland, Bulgaria and Romania, determining the factors that influence efficiency and its implications for local development. The calculations were based on the mathematical model Data Envelopment Analysis, the main source of data being EUROSTAT. The estimates confirm the strong need for systemic reforms in the organization and operation of the development regions: modification of current transfer system, strengthen financial autonomy and solve the problem of excessive fragmentation of administrative-territorial structure, all having a negative impact on the efficiency of absorption of European funds developing regions of analyzed states. Keywords: absorption capacity; European funds; DEA method; CEE countries JEL Classification: A1; A2 Introduction Benchmarking the EU accession process is one of the major areas of structural funds management. This assessment provides decision makers the necessary information on the consequences of projects, plans, policies and regulations regarding the designated objectives to be primarily achieved. Therefore, it may be noted that benchmarking is more likely a strategic tool in the EU integration process. The European Union proposes a single funding system for managing economic integration and the introduction of specially designed funding schemes for almost all policy areas to promote economic and social cohesion among countries. Current European financing operations are based on rigorous management, monitoring, control and evaluation. European Commission states that most effects of cohesion policy cannot be expressed in quantitative terms. There are several studies in the literature that are concentrated on evaluating the efficiency of allocated funds and their impact on economic growth in the context of comparative performance evaluation. Some econometric analysis say that European funds have a negligible or even negative impact on convergence, while others imply a significant positive impact. Those studies suggest a number of different models and approaches to calculate the efficiency and impact of European funds: increasing levels of Europeanization, the capacity to absorb EU funds or to calculate additional added * Assistant Professor, Alexandru Ioan Cuza University of Iasi, Romania, e-mail: dan.lup[email protected] ** Assistant Professor, Alexandru Ioan Cuza University of Iasi, Romania, e-mail: mircea.as[email protected]o Dan LUPU and Mircea ASANDULUI 532 value resulting from Community assistance; finally, all tending to explore the relationship between European funds and their impact on the region which attract them. OECD defines absorption capacity as accumulating and disseminating adequate information, capacity building in local government and civil society to formulate and implement development projects (OECD, 2006). Absorption capacity leads to a strong performance of EU funds only if the economy is fully taken into account (Sumpikova et al., 2004). Sumpikova et al. (2004) define absorption capacity as far as a state is able to fully acquire the financial resources allocated from the EU. The literature on absorption capacity of EU funds in candidate states offers three main definitions (Zerbirati, 2004; Oprescu et al., 2005; Georgescu, 2008; Lupu et al., 2014): • Macroeconomic absorption capacity, which can be defined and measured in terms of relation between the GDP and the structural funds allocated (upper limit for the structural and cohesion funds was generally set at 4% of GDP respective states) • Administrative absorption capacity, which can be defined as the ability and skills of central, regional and local authorities to prepare acceptable plans, programs and projects, to decide on them, to organize coordination, main partners, to deal expeditiously and administrative bottlenecks, work reports requested by the Commission and to finance and supervise applying their implementation properly, avoiding fraud as much as possible. • Financial absorption capacity, which means the ability to co-finance programs and projects supported by the EU, to plan and guarantee these national contributions in multi-annual budgets, and to allocate these contributions from as many partners (public and private) interested in a program or project. 1. Interregional disparities in economic development from Romania, Bulgaria and Poland Eastern European States entered the transition process with a relatively low level of regional disparities as compared to states having a market economy tradition. However, these disparities have rapidly grown and, in particular, between the regions that include the capital and other regions. Interregional disparities are absolutely small as compared with the European Union, but in relative terms, they have reached levels comparable to those in the Czech Republic, Hungary and Germany. Regional development policy is a relatively new concept in Romania. Since 1998, the country was divided into 8 regions (NUTS II level), grouping the 41 existing counties and the capital Bucharest, as displayed in table no. 1. Set out on a voluntary basis, these regions have the status of administrative units, but represent territorial units large enough to constitute a good basis for COMPARATIVE ASSESSMENT OF EFFICIENCY IN ATTRACTING EUROPEAN FUNDS 533 developing and implementing regional development strategies, enabling efficient use of financial and human resources. Table 1 - Statistical information on the development regions in Romania, Bulgaria and Poland Surface Population GDP/ capita Surface Population GDP/ capita [km²] [euro] [km²] [euro] ROMANIA 5800 POLAND 9200 Nord–Est 36850 3674367 5200 Łódzkie 18219 2571534 8500 Sud-Est 35762 2848219 5600 Mazowieckie 35579 5164612 15000 Sud Muntenia 34489 3379406 3600 Małopolskie 15183 3298270 7800 Sud Vest Oltenia 29212 2330792 4800 Śląskie 1233309 4620624 9800 Vest 32028 1958828 4800 Lubelskie 25155 2175251 6200 Nord-Vest 34159 2746064 13800 Podkarpackie 17844 2101732 6200 Centru 34100 2533021 4500 Świętokrzyskie 11672 1281796 7000 Bucuresti-Ilfov 1811 2242377 6600 Podlaskie 20180 1197610 6700 Wielkopolskie 29826 3374653 9600 BULGARIA 4800 Zachodniopomorskie 22896 1693533 8000 Severozapaden 190703 923000 2900 Lubuskie 13985 1008424 7800 Severen tsentralen 149740 928000 3100 Dolnośląskie 1994674 2914362 10400 Severoiztochen 144874 992000 3900 Opolskie 94125 1044346 7300 Yugoiztochen 197987 1124000 3900 Kujawsko-Pomorskie 17 97134 2 098 370 7700 Yugozapaden 203064 2132848 8200 Warmińsko-Mazurskie 24 17317 1 451 950 6800 Yuzhen tsentralen 223651 154000 3300 Pomorskie 18293 2201069 8800 European Union (27 countries) 24500 Source: Eurostat In Romania, except for Bucharest, whose situation in the economic landscape of the country is completely special, growth followed a west-east direction, proximity to western markets acting as a growth factor delivery. Although statistics show some oscillations in time, due to local factors, economic growth had a significant geographic component; underdeveloped areas are concentrated in the Northeast, on the border with Moldova and South along the Danube. Underdevelopment appears to be largely correlated with unemployment and with the predominance of rural activities, and the inability to attract foreign direct investment. The table below summarizes key information on developing regions. North East Region is characterized both by its dependence on agriculture and the proximity to the border with Moldova and Ukraine. The same is true, to some extent, in South Muntenia which is also dependent on agriculture and the Danube has acted as a barrier to cross-border trade. Western and central parts of the country were benefiting from their position closer to Western markets and lowered their dependence on primary sector, benefiting even more from FDI. Poland has 16 regions corresponding to NUTS II level. In terms of territory, the biggest are Mazowieckie, Wielkopolskie and Zachodniopomorskie while in terms of population, are Mazowieckie (capital region, 5.1 million inhabitants) and Slaskie (Poland's largest concentration of old industries, 4.9 million inhabitants); the smallest in terms of population is the western region, Dan LUPU and Mircea ASANDULUI 534 Lubuskie (1 mln. inhabitants). The decentralization reform in 1999 gave complete autonomy to regions and responsibility for returning their economic development. Along with the transformation and growth of the 90s, regional and social disparities in Poland have become increasingly apparent (Table 1). As it can be seen from table 1, in economic terms, the differentiation between Polish regions is relatively low: the relationship between GDP per capita of the poorest (Lubielskie) and the richest (Mazowieckie) region is about 1: 2.2 which is much less than in countries like Italy or Spain. The poorest regions of Poland are located in the eastern part of the country: Lubielskie, Podkarpackie, Podlaskie, Warmino-Mazurskie and Świętokrzyskie. In Poland, like in other East European countries, the capital region (Mazowieckie) is the most developed in the country because of a significant concentration of economic activity in the country's political center. Slaskie and Wielkopolskie are the only regions, apart from Mazowieckie, which are above national average. Most economically disadvantaged regions in Poland are the country's eastern periphery (Podlaskie, Lubelskie, and Podkarpackie). Location of these regions (in the vicinity of less developed countries such as Belarus, Ukraine and Russia) offers limited possibilities for fruitful cross-border cooperation and joint economic initiatives. The second factor affecting the economic situation of peripheral areas is the predominance of agriculture in the regional economy, which still needs urgent structural reforms to increase competitiveness in the future. Looking at Bulgaria we can say that it is divided into six development regions characterized by the same disparities as the other considered countries. We can add that five out of six regions are the poorer regions from the whole European Union; only the region that comprises the capital Sofia (Yugozapadan) is relatively more developed. The economy of regions is mostly based on agriculture and tourism, industry and services being more developed especially in the capital. Also, we must mention that Bulgaria’s regions have reduced autonomy. 2. Data and methodology In our efficiency analysis, we will use the Data Envelopment Analysis (DEA) methodology. DEA is a non-parametric analysis of deterministic performance, developed by Charnes et al. (1978). DEA is an "oriented data" approach to assess the performance of an equal set of units called decisionmaking units (DMUs) which convert multiple inputs to multiple outputs (Cooper et al., 2000). DEA is among the preferred methods for analyzing the performance or efficiency over a number of advantages over parametric methods. Unlike other methods, such as regression analysis that require a priori assumptions, DEA requires very few assumptions, never attempting to explain the nature of the relationship between inputs and multiple outputs belonging analysis units in deterministic manner. COMPARATIVE ASSESSMENT OF EFFICIENCY IN ATTRACTING EUROPEAN FUNDS 535 In DEA, the relative efficiency of any DMU is calculated as the weighted sum of outflows from the weighted sum of inputs, being a scalar value ranging between zero and one, which is evaluated by a linear programming model. The calculation of the efficiency of each DMU, DEA forms a border ,,possibility of output'' to the most efficient DMU based on available data, if and only if the performance of other control units show that some of the inputs and outputs can be improved without worsening overall efficiency. The objective function for DMU, that is being evaluated, includes maximizing the value of output relative to the inputs. There are two types of borders in DEA: one that refers to constant returns to scale (CRS) and one to variable returns to scale (VRS), respectively. As the name indicates, an implicit assumption on the yields of scale associated with each area and thus, the opportunity of a particular envelope surfaces is frequently determined (dictated) the economic assumptions or otherwise made on the set of data to be analyzed. Assuming constant returns to scale are only possible when agents are operating at an optimal scale. Imperfect competition, financial constraints, etc. can cause an agent not to operate at optimal scale. Banker et al. (1984) suggested an extension of the DEA model with constant returns to scale (CRS DEA) to explain situations with variable returns to scale. The DEA models evaluate the effectiveness of the units surveyed (in our case, the development regions in the 3 considered countries) with any number of inputs and outputs. The coefficient of efficiency (CE) represents the ratio between the weighted sum of outputs and the weighted sum of inputs. In our analysis, for each region we select the input and output weights that maximize the efficiency scores. The coefficient of efficiency (CE) ranges from 0 to 1. At the DEA model relative to inputs, CE for the most efficient regions (located on the border line efficiency) is always equal to 1, while CE for ineffective regions are smaller than 1. DEA model relative to outputs, CE for the most efficient regions (situated on the efficient frontier) are always equal to 1, while CE for ineffective regions are greater than 1. Also, DEA allows us to calculate the improvement needed to turn the ins and outs ineffective in the most efficient values. Assuming we have 3 countries (Romania, Poland and Bulgaria) and 30 NUTS 2 regions, each with m inputs and r outputs, score relative effectiveness of a test region (q) is obtained by solving the equations (1) - (5) (Zhu, 2012). For the DEA models relative to inputs (with multiple inputs and outputs), assuming variable returns to scale VRS, the used formulas are: max z =   iq r i iyu Dan LUPU and Mircea ASANDULUI 536   m j jkjiq r i ixvyu  , k=1,2….n  m j jqjxv 1   i u   j v where: z is the coefficient of efficiency for the unit Uq, ui are the weights assigned to output i, vj are the weights assigned to the input j, ε is an infinitesimal constant, xjk is the value input unit j for unit Uk, xjq is the value input unit j for the unit UQ, Yik is the unit output value and drive Uk, YIQ unit output value for Uq, m represents inputs and r outputs. Applying the DEA model requires the definition of input variables related to output. Literature and data availability are determining factors for choosing the model variables. Detailed and standardized data availability was a major problem because absorption of European funds analysis by development regions are among the first studies in the literature. Three European countries were analyzed in this study: Romania and Bulgaria (countries that absorbed the fewest structural funds) and Poland (champion of European funds absorption), taking into consideration 30 development regions. Specific quantities of data input and output for these three countries have been collected and processed through Eurostat, national governments and institutions managing European funds websites. The number of inputs and outputs was determined according to the need to maximize discrimination observed in the existing units. Thus, we used three variables output and two input. To decide which variables are most suitable to use, we considered set of internationally recognized indicators to analyze the efficiency of absorption of structural funds. There are two input variables: the amount of absorbed EU funds by region and the number of projects implemented by each region. The value of Structural Funds absorption indicator was analyzed to demonstrate the increasing influence on regional development, at different stages of absorption (lowest vs. highest). Although paradoxically, one of the main issues mentioned in recent analyzes of the European Commission is the ability or inability of European states to absorb EU funds, a phenomenon that is assigned to financial fragility of these countries. Also, very often mentioned cause is the reduced capacity of countries to prepare projects eligible for EU funds absorption. It is assumed that there is a direct relationship between the absorption of structural funds and development of a region in the respective countries. COMPARATIVE ASSESSMENT OF EFFICIENCY IN ATTRACTING EUROPEAN FUNDS 537 The second considered indicator is the number of incoming projects undertaken by the respective regions. Analyzing the variable referring to the number of finished projects seems significant due to the major differences between regions: while in Romania and Bulgaria the number does not exceed several hundred projects, in Poland it reaches thousands. The indicator reflects the strength of the proposed projects in order to create an impact on relevant factors such as: the number of potential beneficiaries, impact duration and geographical coverage. Three output variables were considered, namely the GDP (based on purchasing parity EU average), the unemployment rate and the risk of poverty of each region. Analysis of these three indicators was achieved from ERDF objectives and requirements: encourage cohesion and reducing regional disparities. Gross domestic product per capita in purchasing power standards as compared to the EU average (PPS-EU) is the ratio of the gross domestic product (GDP) expressed in purchasing power standards and the total population. GDP in PPS-EU is obtained by converting a fictional currency conversion using purchasing power parity index (express connections between same good prices in different countries establishing a common currency). The variable can be justified by the fact that attracting European funds leads to infrastructure improvement, new business opportunities, cost savings and revenue growth, and cumulatively leading to a real GDP growth in the region. Unemployment is a negative state of the economy materialized in a significant imbalance in the labor market where labor supply is greater than demand; i.e. lack of a job for a while. The indicator is considered as one of the objectives of the ERDF in order to create sustainable jobs. It is assumed that regions which absorb more EU funds will develop a greater number of new businesses and therefore employment will increase, eventually leading to a decline in unemployment. Risk of poverty rate is the share of persons with an equivalent disposable income (after social transfers) below the risk of poverty, which is set at 60% of median national disposable income after social transfers’ equivalent. This indicator does not measure wealth or poverty, but only those on low incomes compared to other residents of the same country. Part of social strategy at EU level, social inclusion has been recognized as a common objective of the member states and became part of the national anti-poverty plans. The indicator was analyzed because it is assumed that the attraction of bigger European funds would lead to poverty reduction. 3. Results of analysis The analysis was based on the statistical regional data published annually by Eurostat. As mentioned before, the variables considered in the model are the inputs (amount absorbed EU funds Dan LUPU and Mircea ASANDULUI 538 and the number of European projects contracts) and the outputs (GDP based on purchasing power parity EU, unemployment and the risk of poverty). In Table 1 of the annexes input-output data for the 30 regions analyzed are separately displayed for each country. Therefore, for the indicator referring to executed contracts we see that Bulgaria is the country with the fewest (of the order of 100-200), followed by Romania (300-500) and Poland (800-4500). When looking to the EU funds amounts absorbed we also observe significant differences: values from 60.8 million (Ilfov region) to 221.77 million Euro (Southeast) in the case of Romania; from 110 million (Northern Central) to 185 million (Central Region) in Bulgaria and from 1162 million Euro (Lubuskie) to 3529 million (Wielkopolskie) Poland. For this indicator it can be easily seen that the total outstanding balance of the regions of Romania and Bulgaria are taken together mean within a region of Poland. GDP purchasing power parity relative to the EU shows that the poorest EU regions are in Romania (North-East and South-West) and Bulgaria (Northwestern, Northern, Central and Southern Central Region) with values around 30% of European average value of the indicator. The richest regions, with rates of over 70% are in Bucharest-Ilfov (Romania), Southwestern (Bulgaria), Mazowieckie and Dolnośląskie (Poland). In table no.2 we present the descriptive statistics for the 30 analyzed regions. For the number of ongoing contracts, the minimum (95) is set by region North Central - Bulgaria, the maximum (4501) for Śląskie (Poland) and the average is 1144 contracts. For amounts absorbed EU indicator, the minimum (euro 60.8 million) is the Bucharest-Ilfov region (Romania), maximum (3529 mil. Euros) Wielkopolskie (Poland), averaging 1301 millions Euros. It may be noted here that all regions of Romania and Bulgaria is below the average outstanding balance, while in the case of Poland all regions are above average. Table 2 - Descriptive statistics for the 30 regions analyzed Variable Minimum Maximum Mean Std. Deviation Variance Skewness Kurtosis Contracts 95 4501 1144.37 1024.34 1049285.27 1.29 2.32 Euro amounts absorbed 60.80 3529 1301.41 1217.50 1482309.93 .47 -1.24 PIB PPC UE 26 111 51.03 19.58 383.48 1.54 2.87 Unemployment 1.90 18.2 9.95 3.27 10.69 -.27 1.35 Risk of poverty 43.30 76.9 64.22 11.14 124.27 -.67 -1.11 Source: Authors’ calculations The correlations coefficients of the considered variables that are taken into account when estimating the model are presented in Table 3. The number of executed contracts is strongly correlated with the amounts absorbed and the risk of poverty; the amounts of EU funds attracted is very strong and directly correlated with the number of executed contracts and the risk of poverty; GDP (PPC-