MEASURING TERRITORIAL DISPARITIES IN ROMANIA - A CLUSTER ANALYSIS BASED ON THE ECONOMIC ACCOUNTS FOR AGRICULTURE

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1 MEASURING TERRITORIAL DISPARITIES IN ROMANIA - A CLUSTER ANALYSIS BASED ON THE ECONOMIC ACCOUNTS FOR AGRICULTURE BABUCEA ANA-GABRIELA, PROF. PHD., CONSTANTIN BRÂNCUŞI UNIVERSITY OF TÂRGU JIU, ROMANIA gabibabucea@gmail.com Abstract Romania's agriculture is an economic sector characterized by some specificities. Recent studies on the potential of the Romanian agricultural sector highlight its low economic performance. In this context, starting from the fact that the economic performance is a multivariate phenomenon, the main purpose of the article is to identify the differences and the similarities at the level of the NUTS2 administrative-territorial units, respectively the 8 th development regions in Romania, on the post-eu accession period. Were used specific multicriterial statistical analysis methods for territorial analysis based on the Eurostat data series available at the time of preparation of the study, for the entire set of variables, factors considered that are behind the gaps in economic performance of agriculture production sector at the Romanian territorial level. The variables considered are agricultural production value at basic prices for crop output, animal output, services output, secondary activities (inseparable), output of the agricultural industry, total intermediate consumption, and Gross value added at basic prices. The method used in this research for identifying disparities and analyze the changes in regional agricultural production value is hierarchical clustering analysis performed with SPSS We applied Ward method, based on the F value (ANOVA) to maximize the significance of differences between clusters, a method considered as a very efficient one. Classes of similar regions were delimited in order to delineate the profile of the Romanian agriculture from the perspective of the economic accounts, following the changes registered in the period after Romania's accession to the European Union, both for the year 2007 and 2015, the last year with available open data. The study confirms the existence of significant disparities in the performance of the agricultural sector at the level of Romania's development regions and can be used both in the analysis of territorial comparisons, but also in the adaptation of the agricultural policy measures. Keywords: cluster analysis, hierarchical clustering, agriculture sector, production, economic accounts, NUTS2 regions Classification JEL: C38, Q01, Q15, O13, O18 1. INTRODUCTION The successive reforms of the Common Agricultural Policy (CAP), the enlargements of the European Union (EU) and the impacts of climate change have amplified the diversity of European agriculture. These rapid changes have resulted in the intensification of agricultural activities in some regions, while they have led to the marginalization of agriculture and its eventual abandonment in others [1]. At the regional level, the Romania's agriculture has recovered numerous gaps in relation to the European Union. Those gaps were caused by inefficient allocation of production factors due to a rigid demand for agricultural products, or to the weather-dependent pronounced character of the Romanian agriculture, but also as a consequence of the political mistakes that got exacerbated in the first years after 1989 [2]. For several agricultural indicators Romania was placed on important position among the European countries, like the gross value added to the national GDP at the highest level in the EU in the year 2015, the available agricultural areas and the rate of its utilization, but especially the highest share of the population economically active in the agricultural sector. However, Romania recorded the lowest values for all indicators of labor productivity in agriculture and occupys the last places in the European Union hierarchy [3]. Many studies in the main literature remark that the economic performance of the agricultural sector in Romania remains low, as Romania has a significant unused potential [4]. In the same time, highlight many problems in Romanian agriculture [5, 6, 7]. In this context, the aim of our research is to highlight the evolution of the disparities in Romanian agricultural economic activity, on the 18

2 post-eu accession period, at the NUTS2 regions level, especially in terms of the economic accounts of this important national sector, agriculture. As the definition of the Romania's National Institute o Statistics, Economical Accounts in Agriculture (EAA) is a system of interconnected accounts that provides a systematic, comparable and as far as possible complete picture of economic activity in agriculture in order to analyze the production process and the primary income generated by it in the branch of agricultural activity [8]. Eurostat glossary remarks that The economic accounts for agriculture are a satellite account of the European system of national and regional accounts, adapted to the specific nature of the agricultural sector, providing complementary information and concepts. Although the structure of EAA matches very closely that of national accounts, their compilation requires the formulation of appropriate rules and methods [9]. This system includes the following accounts: content of production, the operating income account, income of the enterprise and the capital account. 2. EVOLUTION OF THE ROMANIAN AGRICULTURAL ECONOMIC ACTIVITY ON THE POST-EU ACCESSION PERIOD - A VIEW THROUGH ECONOMIC ACCOUNTS FOR AGRICULTURE Ten years after the accession into the EU, Romania continues to register largely lag behind in the process of catching up the developed states. To reduce gaps, Romania received significant financial assistance for agricultural activities from the European programs of Common Agriculture Policy. The evolution of the Romania's agricultural production during the post-accession period, presented in the Table no. 1 and graphically represented in Figure no. 1 and 2, regards three main indicators of economic accounts for agricultural, based on data from Eurostat: - Output of the agricultural 'industry', made up of the sum of the output of agricultural products, agricultural services and of the goods and services produced in inseparable nonagricultural secondary activities; - Gross value added at basic prices corresponds to the value of output (at basic prices) less the value of intermediate consumption; - Indicator A corresponds to the deflated (real) net value added at factor cost of agriculture, per total annual work unit. The implicit price index of GDP is used as the deflator. Output is valued at basic prices. The basic price is defined as the price received by the producer, after deduction of all taxes on products but including all subsidies on products. The definition of the agricultural industry is based on Division 01 of NACE Rev. 1. Table no. 1 Evolution of the main indicators of the Romania's Agricultural Production value at basic prices after accession to EU Indicators\time /2007 Output of the agricultural industry 14301, , , , , , , , , ,46 e 1163,79 (Million EUR) - Crop output 8611, , , , , , , , , ,77 e 1190,11 - Animal output 4374,6 4261, , , , , , , , ,39 e -354,37 Gross value added of the agricultural industry (Million EUR) Indicator A of the income from agricultural activity (2010 = 100) 6244, ,8 6391, , , , , , , ,55 e 244,18 63,2 93,8 79, ,9 94,4 111,5 121,8 119,8 154,7 e 56,6 r=revised p=provisional e=estimated Source: Eurostat: It should be noted the agricultural industry and gross value added of the agricultural industry had almost stationary outputs, but with some annual large fluctuation in the first years, in 19

3 time of one constant ascendent tendency of the income from agricultural activity by Indicator A. (See Figure 1, a, b) a) b) Figure no.1 Evolution of the Evolution of the Romania's Agricultural Production value at basic prices, a) output and Gross value added of the agricultural industry (Million EUR), and b) Indicator A of the income (2010=100%) Given that the crop production has the largest share in the output of agricultural industry, this fluctuations results to the influence of the climatic factor of this sector. It can be remark a very slow ascendent tendency of it. The other important agricultural activity, animal production, after an accelerated descending during the period , has in the last six years an annual value quasiconstant. (See Figure no. 2 a, b) a) b) Figure no. 2 Evolution of the Evolution of the Romania's Agricultural Production value at basic prices, after accession to EU a) Crop output and b) Animal output (Million EUR) The evolution of the results of Romanian agriculture is more relevant in relation to the other Member States. To comparing its evolution, was classified European counties based on the main indicators of the economic accounts of agriculture for the year of accession and the last year with registered data, using the Eurostat Map tool. In the year 2016, with a better performance as in 2007, Romania remains in the first countries group if we consider Crop output, even that this group reduced its content from eight to seven countries Source: Eurostat 20

4 Figure no. 3 - Production value at basic prices Crop output (Million EUR) Referring to the Animal output, both for the year 2007 and 2016 Romania remains in the second group of European countries by importance, even that this group improved its minimum and maximum results Source: Eurostat Figure no. 4 - Production value at basic prices Animal output (Million EUR) Seen individually, these indicators seem to indicate some performance of agricultural output in Romania if we consider the continuous development of this sector at the level of all European countries. However, things are not at all like this if we are considering the indicators output of the agricultural industry and gross value added of the agricultural industry. In relation with the other countries, for both indicators, Romania left the position of the top countries in which was classified in 2007 to be placed in the second performance group in 2016, in the condition of its agricultural industry stationary evolution. (See Figure no. 5-6) Source: Eurostat Figure no. 5 - Production value at basic prices - Output of the agricultural industry (Million EUR) Besides of that, indicator A corresponds to the deflated (real) net value added at factor cost of agriculture, per total annual work unit sent Romania, in the year 2016, in the class with the higher increasing in the report with the year (Figure no. 7) 21

5 Source: Eurostat Figure no. 6 - Production value at basic prices - Gross value added of the agricultural industry (Million EUR) It is true that a number of uncontrollable factors such as the fluctuations in exchange rates or the commodity prices affect the value of annual agricultural industry output. However, given that Romania is on the sixth position in the EU in terms of available agricultural area and the land use rate is lower than the EU average, a better efficiency of agricultural activities can transforme Romania's agricultures unused potential Source: Eurostat Figure no. 7 - Production value at basic prices - Indicator A of the income from agricultural activity (2010 = 100) In addition, remark that Romania, considering the size, accounts for 6% of the total EU area and 4% of its population. Romania is one of the European states that enjoys a good endowment in terms of land, water, and human resources in agricultural activities. However, to date, these advantages have only limited influence in generating significant development and restructuring in agriculture and rural areas. 3. ANALYSIS TERRITORIAL DISPARITIES IN ROMANIAN PRODUCTION PROCESS OF THE AGRICULTURAL 'INDUSTRY' To analyze the change in Romania's territorial disparities in production process of the agricultural 'industry', but also the primary income generated by this production, we consider data sets for main indicators of Economic Accounts for Agriculture (EAA) registered at the level of NUTS 2 regions, for the year 2007 and 2014, the latest Eurostat available datasets at the study moment Datasets overview and methodology The main indicators considered to describe the production account, registered at the level of eight NUTS 2 Romania's regions, are presented in the table below and graphically represented in 22

6 Figures no Table no. 2 - Economic accounts for agriculture by NUTS 2 regions, (Million EUR) Nord- Sud - Bucuresti - Sud-Vest NUTS 2 Regions Centru Nord-Est Sud-Est Vest Romania Vest Muntenia Ilfov Oltenia , , , , ,94 88,12 776, , , , , , , ,86 110, , , ,97 - Crop output Changes absolute -151,48-87,59 376,09 791,27 859,92 22,73 504,12 112, , /2007 relative -10,34-6,76 26,37 68,03 65,25 25,79 64,93 10,47 28, ,75 604,69 781,07 566,81 776,12 63,24 472,66 435, , ,9 590,87 712,78 561,57 681,04 28,44 367,06 473, ,25 - Animal output Changes absolute -122,85-13,82-68,29-5,24-95,08-34,8-105,6 38,33-407, /2007 relative -18,21-2,29-8,74-0,92-12,25-55,03-22,34 8,81-9, ,65 8,03 15,77 80,93 30,02 15,97 26,06 16,89 205, ,73 4,62 7,96 54,82 31,9 89,39 14,57 13,57 221,55 - Services output Changes absolute -6,92-3,41-7,81-26,11 1,88 73,42-11,49-3,32 16, /2007 relative -59,40-42,47-49,52-32,26 6,26 459,74-44,09-19,66 7, , , , , ,07 167, , , , , , , , ,56 234, , , ,81 Agricultural output Changes absolute -52,22 103,40 616, ,78 961,49 67,34 583,30 269, , /2007 relative -2,43 5,42 27,75 56,87 45,27 40,25 45,74 17,57 27, ,78 128,66 249,89 145,51 183,85 13,24 133,4 117, , ,04 208,23 316,83 269,88 194,76 5,98 196,26 121, ,04 Secondary activities Changes absolute 91,26 79,57 66,94 124,37 10,91-7,26 62,86 3,73 432,39 (inseparable) 2014/2007 relative 66,24 61,85 26,79 85,47 5,93-54,83 47,12 3,18 38, , , , , ,92 180, , , ,54 = , , , , ,32 240, , , ,85 Output of the Changes absolute 39,04 182,97 683, ,15 972,40 60,08 646,16 272, ,31 agricultural industry 2014/2007 relative 1,71 8,98 27,65 59,00 42,13 33,27 45,87 16,54 28, , , , , ,72 111,38 795,94 907, , , , , , ,71 92, , , ,73 Total intermediate Changes absolute -5,05 71,87 231,72 481,42 456,99-18,68 230,75 154, ,20 consumption 2014/2007 relative -0,42 6,59 16,71 40,21 33,44-16,77 28,99 16,98 19, ,50 946, ,03 758,95 941,2 69,18 612,63 740, ,01 = ,05 860, , , ,25 65,37 841,35 746, ,08 Gross value added at Changes absolute -173,45-85,66 146,71 414,34 334,05-3,81 228,72 5,16 866,07 basic prices 2014/2007 relative -15,93-9,05 13,51 54,59 35,49-5,51 37,33 0,70 13,87 Source: Based on Eurostat database - Economic accounts for agriculture by NUTS 2 regions [agr_r_accts], Last update: There are some disparities in territorial agricultural production outputs in the year 2007 in the context of the different distribution of the rural or agricultural areas, and geographical resources at the level of NUTS 2 regions. In addition, if we compare the levels of the considered indicators for the years, 2014 and 2007, can be remarked greater regional gaps in their relative evolution. Different increasing of all indicators at the level of NUTS2 regions, disparities that seem to amplify the gaps denote structure change in regional agricultural aport in production value. The method used in this research for identifying disparities and analyze the changes in regional agricultural production value is cluster analysis. 23

7 Figure no. 8 - Production value at basic prices Output of the agricultural industry and components (Million EUR) Figure no. 9 - Production value at basic prices, - Gross value added and components (Million EUR) Cluster analysis is, therefore, a multivariate analysis technique that includes a number of algorithms for classifying objects (elements or individuals) into homogeneous groups. The cases are classified into clusters so that there are similarities, similarities between the members of the same cluster, in time that between the members of different clusters there are weaker similarities. Since these techniques, based on the use of the concept of distance, are useful and effective in preliminary data analysis, they allow more efficient organization of heterogeneous data, as well as easier and more consistent retrieval and interpretation of information in such data structured [10]. In the study of the economic performance of agriculture at the level of the Romania's administrative units NUTS2 regions, firstly performed a hierarchical method to define the number of clusters. The variables considered are agricultural production value at basic prices for Crop output, Animal output, Services output, Secondary activities (inseparable), Output of the agricultural industry, Total intermediate consumption and Gross value added at basic prices, all measured in Million Eur. Datasets considered are presented in Table no. 2. In this regard, we use Ward method. Ward's method uses the F value (ANOVA) to maximize the significance of differences between clusters. This method is considered as a very efficient one even if, it creates small size clusters [11]. Distances between those objects were defined by the chosen distance measure, Squared Euclidian distance. After that, the k-means procedure is used to actually form the clusters Results and discussions Cluster procedure was used performed in SPSS v20.0. The number of clusters got by using the Ward method and T-statistics for natural breaks in the groupings to determine the appropriate number of clusters. The Ward Linkage Outputs, respectively dendrograms, the agglomeration schedule coefficients, and Line graph indicate for the data considered in the analysis, related to the year 2007, four different clusters, and related to 2014, only three different clusters. In both situations, region Bucuresti-Ilfov is single in a cluster, because of included Bucuresti, Romania's capital, in the region, a predominantly urban one, with low agricultural activity. (See Figures no ) Changes in clusters membership are presented in the table below. 24

8 Table no. 3 - Changes in Clusters Membership Year 2007 Year 2014 Case Number NUTS2 regions Cluster Distance NUTS2 regions Cluster Distance 1 Nord-Vest 1 148,314 Nord-Vest 2 215,489 2 Centru 1 302,108 Centru 2 137,089 3 Nord-Est 1 220,833 Nord-Est 1 230,079 4 Sud-Est 2 218,837 Sud-Est 1 140,229 5 Sud - Muntenia 1 159,070 Sud - Muntenia 1 296,140 6 Bucuresti - Ilfov 4,000 Bucuresti - Ilfov 3,000 7 Sud-Vest Oltenia 3,000 Sud-Vest Oltenia 2 178,837 8 Vest 2 218,837 Vest 2 199,864 Source: SPSS Outputs of K-means clusters procedure In the year 2014, face to the year 2007, Nord-Vest, Centru regions leave the Cluster no. 1 for cluster no.2 near Sud Vest Oltenia and Vest regions, in time that Sud-Est leaves it and comes near Nord-Est and Sud Muntenia Regions forming cluster no.1 with the best performance. For the year 2014, note a higher similarity between the clusters members, but larger distances between group s centers compared with a) Dendrogram b) Agglomeration Schedule Coefficients Figure no. 10 Ward Linkage Output for the year 2007 The dendrograms display the groups formed by clustering of the regions at each step based on the distances between the clusters formed. a) Dendrogram b) Agglomeration Schedule Coefficients Figure no. 11 Ward Linkage Output for the year 2014 In brief, the final analysis by comparing formatted clusters using K-means Clustering has shown that in 2014 there is a significant change in the structure of clusters, regarding al considered indicators. (See Table no. 4) 25

9 Note that Bucuresti-Ilfov region, which is a predominantly urban, remained single in cluster both in 2007 and 2014, increase its final cluster center significantly for Output of the agricultural industry based on Agricultural services output. Table no. 4 Final Cluster Centers Source: SPSS Outputs of K-means clusters procedure We can note the longer distances between clusters in 2014 compared within 2007, and changes in clusters contents, too. Bucuresti-Ilfov region, which is a predominantly urban, remained single in cluster. (See Table no. 3,5) Table no. 5 - Distances between Final Cluster Centers Source: SPSS Outputs of K-means clusters procedure Analysis of variance, using ANOVA tests, indicate large observed significance level for one of the considered variables, respectively Agricultural services output recorded for It do not contribute much to the separation of the clusters. (See Table no. 6) Table no. 6 - Distances between Final Cluster Centers Source: SPSS Outputs of K-means clusters procedure In this regards, note agricultural services output for both years 2007 and 2014, secondary activities inseparable for 2007, animal output, and output of the agricultural industry for The other indicators have a significant contribution to the separation of the clusters. CONCLUSIONS Regional development is one of the most important objectives of the development policies and strategies of all countries, and for Romania, the development of agriculture is at least as important as other economic sectors. The study confirmes the evolution in the production process 26

10 Annals of the Constantin Brâncuşi University of Târgu Jiu, Economy Series, Special Issue, volume II/2017 of the agricultural 'industry' on the post-eu accession period, on the national level, but even on the NUTS2 levels, too. Even if the cluster method is a subjective one, depending on the variables considered, the results of the multicriterial research on changes in the economic accounts of agriculture, after the year 2007, deliver relevant messages to decision-makers, defining profiles of NUTS2 regions and highlighting factors that can influence the regional development from this point of view. [12]. The study also highlights the fact that the persistence of significant spatial inequalities in Romania's agriculture requires the design of territorial policies capable of supporting the faster development of the lagging counties, in particular by making better use of local resources. BIBLIOGRAPHY [1]. Giannakis, E., & Bruggeman, A. (2015). The highly variable economic performance of European agriculture. Land Use Policy, 45, [2]. Bălăceanu, C., Predonu, M. A. (2011). Efficiency methods to absorb structural funds for the Romanian agriculture. Annals-Economy Series, 4, [3]. Constantin, F. (2017). Study on the evolution of labor productivity in Romanian agriculture compare to some EU countries. Quality-Access to Success, Supplement 2, 18, [4]. Rabontu, C. I., & Babucea, A. G. (2013). Economic aspects of the Romanian agriculture evolution, Annals of'constantin Brancusi'University of Targu-Jiu. Economy Series, 6, [5]. Bruja, C., (2012). Determinants of the agricultural productivity growth among Romanian regions, Annales Universitatis Apulensis Series Oeconomica, 14(1), [6]. Aceleanu, M. I., Molănescu, A. G., Crăciun, L., Voicu, C. (2015). The status of Romanian agriculture and some measures to take. Theoretical & Applied Economics, 2, [7]. Barbu, C. M. (2015). The Romanian" agricultural power" in the European context. Academic Journal of Economic Studies, 1(3), [8]. Romania's National Institute of Statistics web site [9]. Eurostat web site [10]. Ionescu, Ș. A. (2015). Clusterizarea ierarhică cu aplicaţii în analiza financiară. Revista Română de Statistică-Supliment, 3, [11]. Tan, P.N., Steinbach, M., & Kumar, V. (2006). Introduction to Data Mining. Michigan State University and University of Minnesota, [12]. Babucea, A.G., (2017). Recent aspects regarding the financial behaviour of the Romanian households for housing loans - Study case at the level of nuts 3 regions, International Scientific Conference ECOTREND 2017, Economy and social development in the open society, XIVth Edition, October 20-21, 2017, Târgu Jiu, Gorj County, Romania, Proceedings,