Natalija Bogdanov LFA Serbia

8/8/2019 Natalija Bogdanov LFA Serbia

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* Natalija Bogdanov, Proessor o the Faculty o Agriculture o the University o Belgrade, Ser-bia, David Meredith, Scientic Ocer o the EAGASC, Ireland, Sophia Estratoglou, Pro-essor o the Agricultural University o Athens, Greece.

ABSTRACT: Te goal o this paper is topresent a method to establish the typologyo rural areas in Serbia . Initially the OECDrurality criterion was applied to dene the

rural areas in Serbia. Subsequently, relevantindicators were selected (demographic,

geographic, economic, employment-related,human capital, agricultural, tourism andinrastructure) and used to dene anddistinguish relatively homogeneous ruralregions, based on correlation analysis,

actor analysis (VARIMAX method)and cluster analysis. Cluster analysisrevealed six regions o dierent sizes andcharacteristics. Practical considerations

reduced this to our types, resulting in arobust scheme which accurately refects theheterogeneous nature o rural Serbia.

KEY WORDS: rurality, typology, ruralregions, Serbia

Natalija Bogdanov, David Meredith, DOI:10.2298/EKA08177007B

Sophia Estratoglou*

A TYPOLOGY OF RURAL AREAS IN SERBIA

SCIENTIFIC PAPERS

JEL CLASSIFICATION: O18, Q18, R12, R58


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Natalija Bogdanov, David Meredith, Sophia Estratoglou

1. Introduction

With time the concept o ruralityhas been made more extensive and proound.Originally, a rural region was viewed as a residual area o an urban centre (veryoen rural areas are simply dened as those areas that are not urban non-urban areas). Te term rurality relates to a specic set o characteristics, whichcould not be used as criteria in dening rural areas. Although rurality is relatedto specic characteristics such as lower population density, small population sizeo settlements (villages and small towns), countryside lie, presence o agricultureand orestry, mono-residences, smaller size o enterprises and lower scale oeconomic activities, these characteristics are not used as criteria in dening rural

areas. Te most oen used criteria are population density and population size osettlements. However, it is also possible to use other criteria to dene rural areas,such as those relating to the territorial and/or sectoral characteristics. Nowadays,predominant opinion is that a rural region represents a territorial unit with one ormore small/middle-sized towns surrounded by a large area o open space, with arelatively low population density and regional economic structure, which reectsthe situation o a certain labour market (Bogdanov and Stojanovic, 2006).

Te widespread adoption o area or regional based approaches to rural

development has resulted in a greater demand rom policy ormulators andadministrative bodies or increasingly rened denitions o rurality (Comminsand Keane, 1994, Green, 2005). Tis demand stems rom widespread recognitiono the limitations o sectoral approaches to rural development (CEC, 1988,Fluharty, 2008). Te area based approach to rural development depends on theheterogeneous characteristics o rural areas and their potential, the complexconnections between town and countryside, the need or a more ecient policyor these areas through decentralised decision-making, and management o thedevelopment process and other actors. Tis approach has been incorporatedin the European Rural Development Policy since the 1980s, ollowing the shirom exogenous to endogenous development concepts. Bryden(2002)reportedthat such policies were directed towards creating economic and social cohesion,achieved by assigning responsibilities to regional and local communities, throughpartnerships and an integrated approach to territorial development.

Public institutions responsible or rural development, particularly thosein developed economies, have come to recognise the signicance o ruralheterogeneity in determining the outcome to policy interventions. Diversity inrural areas arises rom diferences in the geographic distribution o natural and

human capital and the relative location o rural enterprises vis--vis local, regional,


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national and international markets (Wiggins and Proctor, 2001). Increasingly,policy ormulators are interested in understanding the spatial distribution o

such diferences within and between rural areas (CEC, 2005). Recent examples othe creation and implementation o rural development policy ollow our logicalphases (Bogdanov and Stojanovic, 2006):

Dening rural areas, i.e. denition o rurality, Dening types o rural areas with relatively homogeneous characteristics, Creating specic development policies or each area, Establishing indicators or the evaluation o the efects o rural policies.

Te EU has recently developed two sets o baseline indicators: the ContextRelated Baseline Indicators and the Objective Related Baseline Indicators(DG-Agri, 2006a). Tese are ounded on a comprehensive understanding othe international research in this area and seek to capture the structure andunctioning o rural areas through the monitoring and evaluation o key social,economic and demographic data. Te baselines incorporate data pertaining tonine broad dimensions including demography, geographical characteristics,economic structures, employment patterns, human capital, agriculture, tourismand inrastructure. Tese data do not in themselves constitute a typology, but stem

rom the need to develop more efective and ecient programmes and measuresthrough evidence based planning. Development o the baseline indicators assistsin this as it acilitates comparative assessment o the challenges conrontingsustainable rural development at sub-national levels.

2. The Methodological Scheme to Define Rural Typology

In response to growing interest in the issue o regional diferences, geo-statistical

techniques o identiying, classiying and grouping diferent types o rural areasare increasingly incorporated into rural development policy design processes(Coombes, 1996, DoELG, 2002). Frequently these analyses ocus, or the purposeo acilitating policy development, on generating a classication or typology orural areas based on assessment o demographic, economic and other actors.A review o relevant spatial analysis literature places questions o regionaldiferences and the development o rural typologies in the realm olocal analysis(Fotheringham, et al., 2000). Local analysis studies can be divided into univariate(the evaluation o a single variable describing a particular location or area) andmultivariate (the analysis o several measurements describing the distribution o

a particular eature). Which approach is selected depends on the availability o


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spatial data and the question being asked. In the development o spatial typologiesto characterise rural areas, multivariate analysis is most commonly used, given

the need to consider several social, economic and demographic dimensionssimultaneously. Other approaches involving univariate analysis were also appliedin the past due mostly to the general absence o large scale spatial datasets (CEC,1988). Regardless o which approach is adopted, their undamental utility lies inproviding both a descriptive assessment o rural areas and a baseline rom whichthe impact o policy impacts can be measured (McHugh, 2001).

Whilst the introduction o baseline indicators, such as those published by the EU,is valuable in undertaking comparisons o small sets o variables, they are limited

in terms o developing a uller understanding o the characteristics o diferenttypes o rural area. Tis constraint is amplied when seeking to compare alarge number o variables and/or regions. For this reason a combination o datatechniques, primarily data reduction through Principle Component Analysis(PCA) and data grouping through Cluster Analysis (CA), has been appliedto identiy groups o rural areas that share similar core characteristics. Tesetechniques have been applied in a number o diferent elds, ranging rom waterquality monitoring on large rivers (Singh, et al., 2004) to studies o paleoclimates(Phillip, 2008). Most pertinent to this study is research undertaken by Malinen

(1995), McHugh (2001) and Gulumser et al., (2007), where PCA and CA werecombined to characterise and classiy groups o rural areas. A recent study orurality in urkey provides a detailed structure and methodological rameworkidentiying and classiying rural areas (Gulumser, et al., 2007). A threeoldmethodology, based on identiying rural areas using internationally recognisedcriteria, statistical analysis o social, economic and demographic indicators,and nally principle component analysis to identiy key characteristics o ruralmunicipalities, was used, based on techniques applied by Malinen to rural areasin Finland (Malinen, 1995) and McHugh to rural areas in the Republic o Ireland

(McHugh, 2001). Tese publications were crucial in the choice o the methodologyand the techniques o data collation and analysis used in this research. Tis ourstage process comprised the identication o rural areas in Serbia, the collationand preliminary statistical analysis o indicators or rural characteristics, PCA,and nally CA and spatial analysis:

Identiying rural areas in Serbia - A review o indicators used in Serbia and inthe European Union or establishing rurality. Selection the most meaninguland appropriate criteria or the Serbian countryside.

Serbias Rural Mosaic - A review indicators/variables used or classiying

rural areas at European level and selection the most appropriate or the


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typology o rural areas in Serbia, taking into consideration data availabilityand administrative structures.

Classiying Rural Municipalities in Serbia - Statistical analysis (PCA and CA)to identiy homogeneous clusters o rural areas.

Grouping Rural Municipalities in Serbia Adaptation o the initial results,in accordance with international practice and experience, taking intoconsideration the inuence o immeasurable actors on results obtained bycluster analysis.

3. Rural Areas in Serbia and their Typology Research Results

3.1. Identifying Rural Areas in Serbia

InSerbia there is no ocial statistical denition o rural regions. Te classicationo settlements as urban, rural or mixed was used in the censuses conducted in1953, 1961 and 1971, when the size o the settlement and the ratio o agriculturalcompared to the total population were used as criteria. Unortunately, thisapproach was abandoned. In the 1981, 1991 and 2002 censuses, settlementswere just classied as urban or other. In these years settlements were dened as

urban according to the decision o the relevant local authorities. Settlements notdeclared urban were considered rural (Map 1).


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Map 1. erritory o Serbia according to the denition o rurality by StatisticalOce o the Republic o Serbia


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Statistical criteria were obviously not taken into account and this is a majormethodological aw. From the methodological standpoint, this is an important

issue because any research ocused on rural regions is at great risk with respectto data interpretation. Hence, the selection o relevant eatures and derivedindicators to dene rural areas in Serbia was determined by the ollowing:

Consideration o limitations resulting rom the heterogeneous social,economic and natural geographic (spatial) characteristics o the region

Consideration o limitations in availability o the data and inormation orsome units (at national and lower levels). Te existing subdivision into regionaladministrative units and the availability o statistics efectively constrain any

approach to this problem1 (Bogdanov and Stojanovic, 2006). Te need to use internationally recognised criteria and standards to ensure

the validity o comparisons with studies o other areas.

Other European countries are also aced with similar problems, especially the newmember states (NMS) striving to reorganise their national statistics according tothe generally accepted standards (IAMO 2004).

Te OECD denition o rurality has been accepted in majority o countries.

Te OECD denes rural areas as those communities (NUS V level) with apopulation density o less than 150 inhabitants per square kilometre. Based onthis, the OECD has urther developed a typology o rural areas (at NUS IIIor NUS II level), which characterises rural regions as Predominantly Rural(PR), Intermediate (IR) and Predominantly Urban (PU). In the recent EUreport on Rural Development in the European Union: Statistical and EconomicInormation (DG Agriculture and Rural Development Report, August 2006),the OECD denition is used or the comparative analysis o the socio-economicsituation o rural areas in member states2.

For the purposes o the Rural Development Plan and or allowing comparisonswith EU statistics, rural areas in Serbia have been dened according to the OECDcriteria3. According to this denition 129 municipalities were characterised as

1 A large number o indicators (even in the rich national statistics) is monitored only at thenational and the regional levels. Projection o these indicators to the lower territorial units isoen impossible or insuciently reliable.

2 In the past or dening rural areas, the European Commission has also used the populationdensity criterion, but at lower level


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rural, comprising 3,904 settlements (Map 2). Choosing the OECD denitionor the rural areas will provide Serbia with the additional advantages o being

able to compare the socio-economic make-up o its rural areas with those o theEU member states, and to benet rom best practice with respect to strategies,policies and interventions being implemented in similar areas o Europe.

Map 2. Rural areas o Serbia according to OECD criteria o rurality


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3.2 Serbias Rural Mosaic

Recognising the diversity o rural areas is an important element o the ruraldevelopment policy. Tis diversity is requently reerred to as the rural mosaic.For efective rural strategies and policies to be developed and implemented inrural areas, it is necessary to recognise these diferences, identiy their strengthsand weaknesses and develop strategies which incorporate them

Various indicators have been used to develop typologies o rural areas. Drawingon McHugh (2001) and the Context Related Baseline Indicators and the ObjectiveRelated Baseline Indicators developed by the European Commission or the 2007-

2013 programming period, eight thematic areas were identied. Tese compriseddemographics, economic structures including employment, agricultural andtourism aspects, human capital, transport and telecommunications inrastructureand spatial characteristics. Tese indicators provide a good representation o thestatus and developmental potential o rural areas. Te data collected thereorecan be used to build highly complex models o rural areas and their structure,evolution and unctioning or policy planning purposes.

Having dened rural areas, it was necessary to identiy various types presentin Serbia, given the diversity o such areas. Distinguishing actors includegeographical characteristics (mountains, plain areas, valleys), accessibility(areas adjacent to cities, remote areas), population uctuations and migration,inrastructure, diferences in environmental conditions (e.g. protected areas),variations in agricultural use and productivity, degree o diversication o localeconomies (activities such as tourism, processing, manuacturing), etc.

Based on the above, a typology o the rural areas in Serbia was constructedusing those variables which accounted or the greatest diferences between areas.Te ollowing thematic or sectoral actors were considered most important:

demographic structures, geographical characteristics, structure o the economy,structure o employment, human capital, agricultural structures, tourism andinrastructure. Te 2002 Census provided most o the social, cultural anddemographic variables and much o the economic data. However, there are anumber o critical gaps in the data. Tese primarily relate to the structure oagricultural activities at the municipality level and the geographic and locationalcharacteristics o each municipality. In the case o the geographic data, it waspossible to develop a classication or municipalities based on their predominanttopography (able 1).


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Table 1. List o indicators / variables used or the typology orural areas in Serbia

Demographic structures indicators Infrastructure

1 Population density (inhabitants per km, 2002) 1 Number o telephones/1000persons

2 Population change (in % 1991-2002) 2 Number o persons per doctor

3 Importance o young people (65) 4 High roads/km2

5 In or out migration rate Tourism capacities

6 Demographic Viability (20-39/60+) 1 Number o hotel beds/1000persons

7 EDR (employees/total) Agriculture Gender 1 % o agricultural land

1 Gender ratio (25-44) F/M 2 Labour productivity (Serbia 100%)

Geographical characteristics 3 Land productivity (Serbia 100%)

1 % o area under orestry 4 Average size o arm holdings

2 opography 5 owned land

Structure of the economy indicators 6 used land (own + rented)

1 % primary sector in NI* 7 % arms without income romagriculture

2 % secondary sector in NI 8 % part time arms

3 % tertiary sector in NI Farm distribution per size4 NI Serbia =100% 9 less than 1 ha

5 NI/otal number o employees 10 1 - 3 ha

6 % households with social payments 11 3-10 ha

7 % persons with social payments 12 over 10 ha

Structure of employment indicators Age structure of active farmers

1 % employees in primary sector 13 % o active armers >65

2 % employees in secondary sector Average yields

3 % employees in tertiary sector 14 wheat

4 % employees in public sector 15 maize

5 % sel employees 16 potato

6 unemployment rate

Human capital indicators

1 % without ormal education

2 % with primary school

3 % secondary school

4 % Faculty or college

*NI national income


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Beore classiying rural municipalities, it was necessary to undertake correlationanalysis to assess the independence o each o the indicators relative to the others.

Given the number o variables and nature o socio-economic data, one requentlynds relationships between two or more variables. Te identication and removalo redundant highly correlated variables simplies the interpretation o complexdata sets and prevents specic groups o variables rom unduly inuencing theresults o statistical analysis. Te biggest problems were located in the statisticaldatabase:

1. Some districts (5-6) were declared municipalities only aer the 2002 Censusand or this reason lack the data needed or the comparison study.

2. Unreliable data on population in the south o Serbia.3. Missing indicators (GDP, inrastructure, migration, employment).

Having identied 49 common indicators, seven were excluded, and the naldataset o 41 variables were to classiy rural municipalities in Serbia using PCA.

Te result o the analysis is a correlation matrix which separates statisticallysignicant variables and measures their relative dependence. Te variablesrelated to agricultural land and arm structure showed a high degree o inter-

relationship and were thereore excluded rom subsequent analysis.

3.3 Classifying Rural Municipalities in Serbia

Extraction o principal components, also known as actor analysis, enabledthe classication o rural municipalities based on core characteristics. Factoranalysis assesses whether the selected variables can be explained with reerenceto a smaller number o variables called actors or components. Using thistechnique the 41 variables were reduced into eight principal components witheigenvalues greater than 1, also known as Kaisers criterion. Factors with valuesless than one are excluded rom urther analysis as they are not signicant inexplaining the variance in the dataset. Cumulatively, the eight components withvalues greater than one account or almost 78% o the variance observed in thedataset o 41 variables. able 2 highlights that the rst three components accountor approximately 53% o the total variance indicating that these are the mostimportant components in terms o understanding the characterises o ruralareas in Serbia. Conversely, the last three components account or just 7% o thevariance and are thus o least signicance.


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Table 2. otal Variance Explained (Rotated Solution4)

Component Initial

EigenvaluesExtraction Sums o Squared

LoadingsRotation Sums o Squared

Loadings

otal% o

VarianceCumula-

tive % otal% o

VarianceCumula-

tive % otal

% oVari-ance

Cumu-lative %

1 8.754 26.528 26.528 8.754 26.528 26.528 6.169 18.693 18.693

2 5.089 15.422 41.950 5.089 15.422 41.950 5.515 16.712 35.405

3 3.568 10.813 52.762 3.568 10.813 52.762 4.138 12.540 47.946

4 2.746 8.321 61.083 2.746 8.321 61.083 2.856 8.655 56.601

5 1.707 5.171 66.255 1.707 5.171 66.255 2.292 6.946 63.5476 1.524 4.619 70.874 1.524 4.619 70.874 1.823 5.523 69.070

7 1.145 3.471 74.345 1.145 3.471 74.345 1.528 4.630 73.700

8 1.112 3.369 77.714 1.112 3.369 77.714 1.324 4.014 77.714

Extraction Method: Principal Component Analysis.

An interpretation o the signicance o each o the extracted components wasundertaken with reerence to the correlations between the 39 variables, thestructure o the component and the mapped component score. Brie proles othe eight components ollow.

Component One reects the intersection between topography, strongeragricultural areas, namely the northern area o Serbia, and municipalities withinward migration over the period 1990 2002. Te grouping o these variablesreects the signicance o agriculture in determining the broader socio-economic conditions within rural municipalities that give rise to and sustainimmigration.

Component wo highlights areas dependent on manuacturing and othersecondary sector activities. Tis component has a more random distributionand these statements are based on qualitative assessments by local experts. Teyreect the historical legacy o a centrally-planned economy and, in the case onorthern Serbia, the strength o the ood processing industry.

4 Un-rotated solutions requently display high loadings on more than one component makingtheir interpretation more dicult. It is however possible to rectiy this issue without loss ovariance or explanation, by rotating the solution so that variables have high loadings on somecomponents and zero or close to it on others.


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Component Tree is dominated by demographic and human capital variables.Municipalities with younger populations are strongly represented in this category.

Tey represent areas with high emales/male ratios, indicating unique local socio-cultural conditions. Based on international research, it is likely that these areasare highly dependent on external transers rom males working in other regionso Serbia, or even in other countries.

Component Four reects areas with higher levels o economic dependence onthe tertiary sector. Tese are not always located next to urban centres, whichsuggests a high level o dependency on the tourist industry in certain ruralmunicipalities.

Component Five includes areas with higher densities o roads in general and,more importantly rom an economic development perspective, major highwaysin particular. Tese municipalities are located along the primary transportationcorridors radiating southwards and westwards rom Belgrade.

Component six, seven and eight are o relatively minor importance. Tey haveeigenvalues greater than unity (one) but explain only approximately 8% o thetotal variance.

Component Six depicts a spread o municipalities that are dependent on publicsector employment. Tey tend to be located around urban areas. Te spatialpattern may well be distorted by public ownership o arms and other industriesbased in rural areas. In this instance it points to relative low levels o employmentin some municipalities.

Component Seven scored highly on the health care variable, calculated as thenumber o doctors per 1000 inhabitants. Unsurprisingly, this variable appears

to be spatially concentrated close to urban centres. However, there are someexceptions, particularly in western municipalities.

Component Eight, the least signicant actor, is dicult to interpret. Lowemployment and the presence o younger people are the characteristic eatureso this component. Notwithstanding the relative insignicance o this variable,international experience suggests that areas with large groups o young peopleand low employment are at increased risk o social and cultural conict, whichmay undermine economic development eforts.


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3.4 Grouping Rural Municipalities in Serbia

Te municipality level component scores were input into SPSS or statisticalanalysis. Te clustering procedure was undertaken and the results mapped andinterpreted with reerence to the mean values or each cluster. It was apparentrom an early stage that greater availability o data, particularly inormationpertaining to intra-municipality commuting, would improve the clusteringprocess. However, in the absence o this and other data, the team undertookan iterative process whereby 2+1, 3+1, 4+1, 5+1 and ultimately 6+1 clusters orregions were identied and mapped (the 1 region representing those areas thatwere classied as urban using the OECD denition). Ultimately, the 6+1 region

scheme was considered the most appropriate solution to Serbia or rural planningpurposes. Other regional options were rejected on the basis that they did not ullycapture the geography o Serbias population and economy.

able 3 depicts the nal cluster centres emerging out o the clustering process. Tisis a useul summary o the structure o each cluster and its relationship to otherclusters. aking the example o Cluster 1 (Multiunctional Rural Economies) wend that municipalities assigned to this cluster have an average score o + 0.82955standard deviations above the mean or the demography component as identiedthrough actor analysis.

Table 3. Final Cluster Centres

1 2 3 4 5 6Agriculture -0.627 -0.829 -1.548 1.020 -0.240 -0.601Industry -0.047 0.807 -0.700 0.239 -0.156 -0.681Demography 0.830 -0.875 -1.027 -0.094 -0.352 -0.185Public sector -0.235 -2.377 3.160 0.173 -0.197 -0.729Accessibility -0.200 0.675 -0.040 -0.682 0.807 -0.545

Service dependency -0.038 3.577 1.181 0.117 -0.533 0.334Health care -0.748 -0.160 -0.389 0.233 0.340 3.095Inrastructure 0.217 0.149 0.349 -0.035 -0.352 6.008

able 4 provides details regarding the distance between the nal cluster centres,which acilitates comparison between clusters. Clusters with similar scores aresimilar with respect to a number o actors, whilst greater divergence in the scoressuggests signicant diferences. So or example, regions three and our are verydiferent whilst our and ve are somewhat similar.


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Table 4. Distances between Final Cluster Centres

Cluster 1 2 3 4 5 61 4.738 4.231 2.257 2.080 7.0882 4.738 6.310 4.986 4.869 7.8993 4.231 6.310 4.398 4.290 7.8664 2.257 4.986 4.398 2.168 7.0055 2.080 4.869 4.290 2.168 7.1656 7.088 7.899 7.866 7.005 7.165

3.5 Assessment

Te data underpinning these clusters was extracted to produce regional prolesand this ormed the basis o the teams assessment and selection o a 6+1 regionsolution. Tis regional ramework was selected on the basis that it provides anaccurate reection o Serbias topography with an agriculturally dependentnorthern region, a multiunctional rural area to the south divided by an areathat is strongly inuenced by urban centres, their hinterlands and majortransportation routes. Tree urther area types were also identied. Tese representmunicipalities with specic socio-demographic proles. Tough statistically

they are signicant, they account or a very small number o municipalities (7%).Furthermore, assessment o these areas shows that their basic economic prolereects that o the surrounding municipalities. Given that the objective o thisresearch was to identiy rural regions or economic development purposes it is valid to group these areas into the surrounding regions. Efectively this leavesa 3+1 regional ramework or the purposes o rural development planning andprogramme implementation; an agricultural region, a multiunctional ruralregion and areas with economies strongly inuenced by urban centres andtransportation inrastructure.


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Map 3. Te territory o Serbia according to the types o rural areas identied


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Following a thorough review o the initial data and the results o the clusteranalysis, the original borders o some o the rural areas have been redened.

Tese adjustments were made or the ollowing reasons:

Te initial solution envisaged six types o rural area, two o which includedless then 5% o the total number o municipalities. Tis level o detail isunhelpul in the context o creating particular developmental solutions. Tisprocess ollowed the principle that baseline indicators must be consistent withthose o the areas they were merged with.

It became apparent that the relevant qualitative characteristics o certainregions cannot be ully expressed with statistical indicators. It is oen simply

not possible to draw clearly dened borders, especially in agricultural regions,even given the small scale o some o the units involved. Tus, in instanceswhere correlations were not particularly strong, municipalities on the edgeso certain regions were transerred to an apparently more suitable groupingon the basis o other, immeasurable actors.

Following the subsequent analysis and adjustment, our rural region types havebeen dened in Serbia, as shown in the Map 4.


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Map 4. Te territory o Serbia according to the types o rural areas identied


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Te characteristics o the our types o rural areas in Serbia are as ollows.

Highly productive agriculture and integrated economy (Region I) Tis regioncontains the most ertile land and is thus dominated by intensive, well-undedagricultural production. Compared to other parts o Serbia, it is characterizedby more benign demographic trends, increased entrepreneurship, a diversiedindustrial sector and a well developed physical and economic inrastructure.Economically, this region is the most developed and well integrated.

Small urban economies with labour intensive agriculture (Region II) Tis regioncomprises the areas around the major urban centres and the larger towns and

their immediate surroundings. Te economy is dominated by intensive arming(the production o ruit, vegetables and livestock) to eed the great number oconsumers in the adjacent cities and towns, and consequently has the lowestunemployment rate in Serbia. Compared to other parts o Central Serbia, theinrastructure, economy (especially with regard to productivity rates) and accessto communal and public services are better developed.

Natural resources oriented economies mostly mountainous (Region III) Due tovariation in geographical characteristics, this region is highly heterogeneous. Te

economic structure is based on the exploitation o natural resources, throughmining and agriculture. Unavourable demographic trends are typical o this area,with the highest rates o rural poverty and unemployment in Serbia. Facilities toprocess the raw materials produced are lacking, but their development ofers away to improve the local labour market.

High tourism capacities and poorly developed agriculture (Region IV) Tisregion comprises those parts o Serbia with the greatest tourism potential and thehighest rate o tertiary-sector contribution to the economy. Agricultural output

o eedstufs is underdeveloped.

able 5 provides detailed inormation on the dening characteristics o the ourtypes o rural region.


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Table 5. Te characteristics o dened rural regions in Serbia

SerbiaRuralareas

RegionsI II III IV

1. Geographical characteristics

otal area, km2 77508 65952 20229 12642 22278 10803

No. o settlements 4715 3904 471 993 1569 871

Average population/settlement 1590 1066 3300 1094 616 637

Population density 97 63 77 86 43 51

2. Population and human development indicators

% population changes 2002/1991 98,96 96,35 100,00 97,34 90,69 95,04

In - out migration rate 1,48 -0,14 5,81 0,43 -5,43 -7,43

Aging rate 1,05 1,08 1,02 1,17 1,28 0,78Educational structure o population >15:

Without education, % o total 21,84 28,19 24,16 28,67 34,74 27,14

Primary education, % o total 23,88 26,69 26,41 25,42 27,51 28,62

Secondary school, % o total 41,07 36,09 41,10 36,69 27,35 36,11

Faculty education, % o total 11,03 6,95 7,53 7,29 5,87 6,55

Unknown, % o total 2,18 2,07 0,80 1,94 4,53 1,59

3. Employment

Primary sector, % o total 23,36 32,98 30,75 32,68 36,30 34,20

Secondary sector, % o total 30,08 30,69 31,20 30,79 29,11 31,72

ertiary sector, % o total 24,82 18,60 20,28 19,41 15,35 17,80

Public sector, % o total 18,94 14,84 15,57 14,09 15,08 13,94

Unknown, % o total 2,80 2,89 2,20 3,03 4,17 2,34

Unemployment rate (%) 22,22 21,32 22,40 19,69 20,33 23,22

% o employed in agriculture 21,96 31,45 30,14 31,19 32,93 33,06

4. GDP

GDP per capita in Serbia = 100% 100,00 73,69 96,72 70,32 51,43 54,57

Primary sector, % o total 19,33 32,48 33,24 30,25 38,63 24,24

Secondary sector, % o total 39,48 41,12 42,36 39,71 38,16 43,36

ertiary sector, % o total 40,79 26,06 24,14 29,67 22,64 32,08

Public sector, % o total 0,40 0,34 0,27 0,36 0,57 0,32

% agriculture 16,33 29,81 29,93 28,19 36,48 22,35

Primary sector productivity Serbia=100% 100,00 87 128 74 69 47

Secondary sector productivity Serbia=100% 100,00 75 102 65 53 57

ertiary sector productivity Serbia = 100% 100,00 62 71 61 48 60

5. Agriculture

% agricultural land o otal area 65,97 65,30 83,29 64,34 55,03 53,95

Structure o agricultural land:

Arable land 65,40 62,78 87,79 60,48 47,52 25,79


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Orchards and vineyards 6,07 5,59 1,77 11,05 6,51 7,10

Meadows and pastures 27,80 30,88 8,64 28,34 45,93 67,11

Rest 0,74 0,74 1,81 0,13 0,04 0,00

Livestock

cattle/100 ha arable land 25,37 24,85 14,62 37,47 23,96 47,03

pigs/100 ha sown land 94,65 91,19 80,20 131,20 84,00 96,25

sheep/100 ha agriculture land 30,82 31,91 13,01 57,99 26,42 61,72

Average arm size 3,60 3,94 3,53 3,72 4,25 4,76

Land productivity (Serbia = 100%) 100 88,62 111,48 110,52 61,77 48,44

Labour productivity agric. (Serbia = 100%) 100 93,58 131,22 80,13 79,34 49,46

6. Tourism

No o beds/1000 residents 11,20 13,71 4,29 17,31 15,18 30,53

Overnights/no o beds 79,09 78,75 80,00 67,23 73,00 96,06

7. Infrastructure

No o telephone users/1000 residents 331 284 292 292 274 261

No o residents/1 doctor 369 512 566 457 470 584

Source: Te Census o population, households and ats 2002 - Population, books 2,4,5, 6,7,11,14,15,19

and Agriculture, books 1,2,3; Statistical annuals Te Municipalities in Serbia (2006); Bureau o

Statistics o the Republic o Serbia, Belgrade

4. Conclusion

Efective rural development policies must be based on an accurate classicationo the essential characteristics o the various regional types. Such a rameworkallows the identication o both needs and opportunities in the rural areas. In thepast, the lack o universal denition o rurality and widely diferent approachesin ormulating rural typologies, resulted in a territorial approach to developmentthat was ragmented and strongly ocused at just a very local level. Although itis now widely accepted that rural areas are not homogeneous, it is necessary to

establish sets o indicators that can both reect the specic characteristics o, anddistinguish between, the main types o rural landscape.

On the basis o all the analyses perormed, it is concluded that the Serbiancountryside can be divided realistically into our basic types. Te diferencesbetween the dened types are signicant and must be incorporated intothe preparation o a state-wide rural development policy and developmentplanning at local levels. . Te scheme described above highlights both problemsand opportunities or each o the rural types, enabling efective planning and

implementation o suitable measures.


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LITERATURE


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