Factors affecting rural development in turkey: Bartın case study

Forest Policy and Economics 12 (2010) 239–249

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Forest Policy and Economics j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / f o r p o l

Factors affecting rural development in turkey: Bartın case study Bülent Yılmaz a, İsmet Daşdemir b,⁎, Erdoğan Atmiş b, Wietze Lise c,d a

University of İnönü, Faculty of Fine Arts, Malatya, Turkey University of Bartın, Faculty of Forestry, Bartın, Turkey c IBS Research and Consultancy, İstanbul, Turkey d ECORYS Research and Consulting, Ankara, Turkey b

a r t i c l e

i n f o

Article history: Received 11 May 2009 Received in revised form 26 November 2009 Accepted 5 February 2010 Keywords: Sustainable rural development Village Economic–social and environmental indicators Resource allocation Turkey

a b s t r a c t The aim of this study is to establish the most important factors affecting rural development in Turkey by means of a multi-dimensional approach and to achieve realistic and practical rural development strategies using these factors. For this purpose, a total of 96 villages are selected fully covering two counties in the Bartın province located in the Western Black Sea Region of Turkey, which is among the provinces with the lowest per capita income and the highest share of village population. 36 variables which characterise the level of development in villages are developed. These variables are measuring environmental, economic and socio-cultural dimensions and the relationships among them. Principal component and regression analyses are applied determining that there are 12 factors affecting development of the villages. These are (1) geographical location, (2) size of a village, (3) productivity of land, (4) type of land use, (5) active population, (6) poplar production areas, (7) proximity to a river, (8) housing comfort, (9) characteristics of drinking water, (10) productive fruit areas, (11) cooperativization and (12) social infrastructure investments. Based on these 12 factors, a development index (DI) is developed consisting of the 12 variables with the highest factor loading in each derived factor. Villages are divided into three groups based on (1) the DI values and (2) 36 variables used in a discriminant analysis, showing that the proposed DI is a reliable index to measure variation in development. According to these results, development strategies for each village group are put forth. Subsequently, the methodology developed in this paper can be used to monitor village development and to assist in effective use of resources for sustainable forestry and development in Turkey. © 2010 Elsevier B.V. All rights reserved.

1. Introduction The basic problem for countries with emerging economies is to achieve a path of equitable and sustainable development. Such development can be achieved by using natural resources, capital stock, labour, technological information in a stimulating socio-cultural environment. The most popular measure for development is national income per capita. In the literature and in practice, increase in national income per capita has been used as a measure for development and capital stock has been seen as a key factor inducing development (Öney, 1987; Savaş et al., 1999). However, development is not a one dimensional issue, where development could also comprise an increase in quantity and efficiency of production, an increase in the share of the industrial sector in national income and exports, positive structural changes and improvements in the areas of social, cultural and institutional infrastructure (health, education, tourism, environment, natural resource management etc.). Hence, four major elements ⁎ Corresponding author. Tel.: +90 378 2235141; fax: +90 378 2235065. E-mail addresses: [email protected] (B. Yılmaz), [email protected] (İ. Daşdemir), [email protected] (E. Atmiş), [email protected] (W. Lise). 1389-9341/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.forpol.2010.02.003

of development can be distinguished: economic, social, cultural– human, and environmental. Development varies over time and by location. There is a connection between local distribution of income and resources and welfare of society. Equal local distribution of income and resources is not only important for underdeveloped areas but also for developed areas. An equal local distribution will prevent migration of population to big cities. Hence, this will reduce urban problems such as accommodation, education, health, water, energy, infrastructural services, traffic, noise and environmental pollution. Moreover it will increase the welfare of society and provide sustainable development (Dinçer, 1996). Every country develops policies and strategies targeted to their own economic and social structure to overcome regional disparities. Within the framework of five-year development plans in Turkey, in order to stimulate development in a balanced way, socio-economic development levels of provinces and districts are determined by the State Planning Organization (SPO) to determine the allocation of public resources to priority areas and to stimulate private sector investments in these areas, and to establish local development policies and strategies (Dinçer, 1996). In such studies certain social and economic criteria of the provincial urban center are taken into

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consideration, in order to determine the level of provincial development. Certainly, also villages which constitute a significant part of the provincial population (approximately 30–35%) play an important role in development. For this reason, during development planning in provincial terms, it is necessary to consider both urban and rural areas. Moreover, village development is directly related to urban development. However, according to the National Rural Development Strategy of Turkey (Anonymous, 2007), the basic goal for rural development is to bring the work and life conditions of the rural society in balance with urban areas, taking into consideration local resources and the need to protect natural and cultural assets. In this context, the designation of provincial resources to achieve a wellbalanced provincial level of development is also an important subject. Unfortunately, the allocation of resources for a balanced level of development has not yet been determined by using multi-dimensional approaches in Turkey. To achieve rural development a large variety of measures are needed aiming at improvement of the rural economy, the quality of life of the community, land-use, environmental protection, and the attractiveness to reside in rural areas (Elands and Wiersum, 2001). The perception of rural development has undergone considerable changes in the last 30 years and it has become a multi-dimensional issue. A number of studies show the importance of natural resources and environmental dimensions of rural development (Ashley and Maxwell, 2001; Farrington and Lomax, 2001; Mikos, 2001; Ellis and Biggs, 2001; Sanderson, 2005; Courtney et al., 2006; Lise, 2007; Narain et al., 2008), as well as economical dimensions (Rizov, 2005). Also, research has been performed in various countries using sociodemographic variables, social interactions, social ties, economic, educational, infrastructural and environmental data for rural development (Brenmand and Luloff, 2007; Tilt et al., 2007; Oddershede et al., 2007). On the other hand, in the context of rural development, a new approach named as Sustainable Livelihoods Approach based on human, social, natural, physical and financial capital has been developed (Cochrane, 2007). Moreover, factors influencing the development of land-use systems have been determined using analytical methods (factor and discriminant analyses) using data that consist of physical, land-use type, and socio-economic variables (Rasul et al., 2004). This is also the case for Turkey. For instance, concerning rural development, application of some techniques like Rapid Rural Appraisal and Participatory Rural Appraisal to collect information about development of villages and especially forest villages in mountainous districts and some research oriented to improve forest–village relationships have been performed (Yurt et al., 1971; Acun and Geray, 1980; Çağlar, 1986; Daşdemir, 2002; Tolunay, 2002, 2006; Yılmaz, 2003). However, these studies generally determine the existing conditions using subjective evaluations and suggestions. Moreover, all the criteria such as natural structure, landuse structure, demographic structure, infrastructure and socioeconomic structure have not been considered jointly and are not analysed numerically. Problems that are tried to be solved by rural development could be divided into two, namely physical and non-physical problems. Relative identification and definition of physical problems like deficiency of infrastructural, educational and health facilities, low agricultural productivity, insufficiency of drinking and irrigation water, presence of soil erosion are relatively easy to identify. But there have been difficulties in the identification and definition of nonphysical problems like shortage of cultivable land and insufficient government services (Oakley and Garford 1985; Tolunay, 2006). In order to overcome barriers for rural development, the socio-economic and socio-political structures of the rural region must be analysed (Tolunay, 2006). Furthermore, (1) the inability to develop plans and programs suitable to stimulate rural development in Turkey, (2) ignoring people's views and priorities in the field, (3) having a low number of cooperatives, (4) not taking into consideration the

characteristics of the region, (5) bureaucratic top-down governance, and (6) omitting follow-ups and assessments, have stood in the way for successful rural development (Gülçubuk, 2000). Therefore, investigation of development and particularly rural development, establishing factors affecting development and responding to this, carries great importance for a developing country like Turkey. The human development index (HDI), which consists of indices such as life expectancy, education and real national income per capita, of Turkey is 0.757 and it is in the 92th place among 177 world countries and it is just above world average (0.741) (UNDP, 2007). Also the real national income per capita of Turkey in 2006 with a population of 73,875,000 is 5400 USD (TÜİK, 2007) which is below world average (7439 US$) and Turkey is in the group of developing countries (WB, 2007). As in other countries, there are inter-regional development disparities in Turkey too. Marmara, Aegean, Central Anatolia and Mediterranean Regions which are located in the west of Turkey are relatively well developed, whereas East Anatolia, mountainous areas of the Black Sea and Southeast Anatolia are far below the Turkish average. In Bartın province, which is located in the Western Black Sea Region of Turkey, the national income per capita is approximately 2500 USD which is less than half the Turkish average. This value is even lower in rural areas. The Bartın province has a high share of village population. Therefore, determining of the factors affecting village development and grouping villages according to the level of development and determining strategies and policies of development directed to each village group carry great importance. This paper aims at a more detailed methodology than the UNDP (2007) to explain differences in development at the village level in Turkey. Turkey consists of 81 provinces, 924 districts, 2103 counties and 38,500 villages. Bartın province has been chosen, because it is mainly rural, with many forests, so that also environmental factors can be included in measuring the level of development at the village level. The Human Development Report also suggests that a measure of development is ideally composed of many factors (UNDP, 2007), whereas they do not do so themselves in the HDI. This paper suggests an econometric methodology for deriving a comprehensive development index (DI) and the validity of the DI is tested by a discriminant analysis. While the application of this paper is to one particular province in Turkey, the methodology could be applied to any other region and geographic level as well. The outline of the paper is as follows. Section 2 describes the used data, collection of data and methods of assessment. In Section 3 the most important factors affecting village development are determined by a principal component analysis and a development index (DI) is derived, where variations in the DI are investigated by a regression analysis. Villages are grouped both according to the DI and discriminant analysis, while development strategies and policies directed to each group of villages are determined and discussed. The last section presents conclusions and provides some policy suggestions to stimulate rural development. 2. Materials and methods 2.1. Study area and data Bartın province which is located in the Western Black Sea Region of Turkey was selected as the study area (Fig. 1), because its national income per capita is far below the average of Turkey, and it has the highest share of village population. The population of Bartın is 184,178 with a village population ratio of 74% and it has the highest village population ratio in Turkey. The ratio of agricultural employees to total employment is 71% and it is 12th among the 81 provinces of Turkey. Even though Turkey is not a member of the EU and has not yet adopted the NUTS/LAU geographic classification system, a comparison can be made. NUTS1 would be provinces, NUTS2 would be districts,

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Fig. 1. Location of the study area. Note: the village names associated with the numbers in the graph are presented in Table 5.

NUTS3 would be counties, whereas LAU1 would be classified as villages. Moreover, according to Village Law numbered 442, the places that have less than 2000 inhabitants and have no municipality are defined as villages. People who have a common mosque, school, grazing lands, fields, and who live in common or separate houses together with their vineyards, agricultural fields and gardens constitute a village. Villages are governed by a village headman and a commission of elderly which is directed by the village headman. Currently there are 266 villages with a population of 136,292. The village population in Bartın province has been declining in the last decade with a yearly rate of decrease of 0.17% (TÜİK, 2007). A total of 96 villages, fully covering the Center and Arıt counties, have been selected. These two counties are representative for the whole province, where the Center county hosts the province headquarters contributing the largest share to the total economic activity in the province. The sampling ratio of the selected villages to all villages in the province is 36% (96/266x100). All the villages that are in two counties (Merkez and Arıt) of the Bartın province are included in the sample. The ratio of the chosen villages to the whole number of villages within the province is 36% (96/266). The reason that these two counties were chosen among the seven other counties which are in the province is due to the fact that Center is the most developed and Arıt is among one of the least developed counties.

Data at the village level have been collected from 2007 records of Governorship of Bartın, Agriculture Directorship of Province, State Forest Management of Bartın, Directorship of Special Province Administration, Directorship of Education, Directorship of Health, Directorship of Meteorology, previous research (Yılmaz, 2006; Yılmaz and Atik, 2006) and these are complemented with interviews with village leaders. In addition, the variables with numbers 1–12 and 28 in Table 1 have been obtained from using a Geographic Information System (GIS). The variables determining rural development have been derived in two stages. In the first stage, after reviewing similar studies, 43 variables related to village development and which can be measured in village terms have been developed. Rural development is multidimensional and this can be quantified by measuring the natural structure of the villages, physical location, land-use structure, demographic structure, infrastructure, socio-cultural structure such as education, health and organization, economical condition and their relationships with each other. In the second stage, the number of 43 variables is decreased to 36 by selecting those that would most affect village development by means of using our logic and experience. The 7 variables out of 43 which were chosen during the beginning of the research has been excluded, because prior experiences, analyses and research indicated that these 7 variables were not useful and would

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Table 1 Groups, names and definitions, labels, units, and some statistics of the variables in the research. Group

No. Name and definition of variable

Label

Unit

Mean Sdt. Dev. Scale

Natural structure

1 2 3 4

ELEV SLOPE LAND SOIL

m – – –

146.6 155.0 3.0 0.7 2.8 1.1 3.6 1.5

LCC SDEEP EROS RIVR

– – – km

DIST TEMP RAIN HUM FAC ANC PAC NAC MPA GRA OVA FRA POP TOP AIRP POD WRT NUH APRT ROAD WATR TWC

km °C mm % da number da da da da da da da number %0 capita/km2 % number % – – –

IRT SPRT SCHE HSE COOP ILC

% % – – – ranking

5 6 7 8

Land-use structure

Demographic structure

Infrastructure

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Socio-economic structure 31 32 33 34 35 36

Elevation Slope Type of land-use (forest=1, range=2, dry farming=3, wet farming=4, settlement=5) Soil type (alluvial=1, coleville=2, gray–brown podzolic=3, red–yellow podzolic=4, forest soil without lime=5, brown forest soil= 6) Land capability class (VII=1, VI=2, V=3, IV=4, III=5, II=6, I=7) Soil depth (very shallow=1, shallow=2, middle deep=3, deep=4) Erosion level (intensive=1, middle=2, nothing/very little=3) Nearness to river (more than 5 km=0, 4–5 km=1, 3–4 km=2, 2–3 km=3, 1–2 km=4, 0–1 km=5) Distance from market Temperature Rainfall Humidity Forest area per capita Animal number per capita Productive agricultural area per capita Non-using agricultural area per capita Meadow and pasture area Greenhouse area Open vegetable area Fruit area Poplar area Total population Annual increment rapidity of population Population density Women ratio in total population Number of households Active population ratio to total population Type of road (path=1, winter earth road=2, stabilized road=3, asphalt road=4, main road=5) Type of drinking water (demijohn=1, network water=2, fountain=3, well=4, spring=5) Type of WC (connected to drain=1, with single pit=2, cesspool=2, without pit=4 without toilet=5) Illiterate ratio in total population School-age population ratio in total population School existence (yes=1, no=0) Health service existence (yes=1, no=0) Cooperative existence (yes=1, no=0) Income level ranking per capita (low=1, middle=2, high=3)

not add anything new. The remaining variables can be grouped under 5 main topics, namely (1) natural structure, (2) land-use structure, (3) demographic structure, (4) infrastructure, and (5) socio-economic structure. An overview of these groups and the 36 variables is given in Table 1 consisting of the variable name, definition of the index with logical order, label, unit, mean, standard deviation and scale of variation. 2.2. Evaluation of data To evaluate all variables simultaneously and, thus, to determine the most important factors affecting improvement of the villages, a principal component analysis is used (Harman 1967; Hair et al., 1992). A data matrix of N × n (96 × 36) is used as input into the principal component analysis. The Varimax criterion with Kaiser Normalization as the rotation method is used in the principal component analysis. The output of the principal component analysis is used to develop a development index (DI). The variables with the highest factor loading in absolute terms in each factor enter the DI (see Table 4). This reduces the initial set of variables to a smaller set of variables, which are considered as the key variables determining the level of development in the village. DI is then defined as the sum of the standardised Z-values of these selected variables multiplied with the factor loading as derived with the principal component analysis. We would like to study which variables best explain the variation in levels of development. Therefore, DI, being a measure of development, is taken as a dependent variable and the effects of the other variables not included in the calculation of the DI are investigated in a regression analysis. In addition, a regression analysis

3.9 2.4 1.6 4.4

1.9 0.6 0.5 0.8

16.6 9.5 11.9 0.8 58.0 6.5 78.0 4.5 6.3 8.1 0.6 0.3 3.5 1.7 1.0 1.0 57.7 87.4 7.9 10.5 75.9 50.9 60.8 78.5 105.3 155.6 611.2 265.6 − 4.8 21.4 110.9 75.7 52.9 3.8 118.2 61.8 64.2 9.5 3.1 0.9 3.1 1.2 2.2 0.5 12.2 91.0 0.4 0.1 0.1 2.2

4.8 15.1 0.5 0.3 0.3 0.4

10–580 1–4 1–5 1–6 1–7 1–4 1–3 1–5 2–40 9.9–12.4 52–65 72–81 0–41 0–1.75 0.8–12.8 0–6.2 1–436 1–58 1–239 1–523 1–1285 162–1727 − 60–125 10–469 28.2–59.3 31–530 40.3–90.6 1–5 1–5 1–3 3.1–25.3 49.9–174.5 0–1 0–1 0–1 1–3

is used to determine the variables that affect the income level per capita (ILC), which is a widely accepted variable for growth and development. In order to verify the accuracy of grouping villages according to DI values, a discriminant analysis is undertaken by using all the 36 variables. In applying these statistical techniques, Version 16.0 of Statistical Package for Social Science (SPSS), EViews 3.0 and Microsoft Excel is used. Finally, a table is presented with the most important factors affecting village development and strategies and policies for further development of each village group are suggested. 3. Results and discussion 3.1. The most important factors affecting development of the villages A principal component analysis is applied to divide the variables into distinct groups, and thus, to determine the most important factors affecting development of the villages. The first 12 factors (or components), of which the eigenvalues are larger than 1 (Kaiser Criterion), are extracted in a principal component analysis based on the 36 variables. Thus, the 36 variables were reduced to 12 factors with the loss of 25% of information. According to the results of the principal component analysis with rotation, 75% of total variance among the 36 variables was explained by these 12 factors (Table 2). In the principal component analysis, the component matrix was rotated using an orthogonal rotation (Varimax method), in which the factors are independent of each other (Hair et al., 1992), which are scientifically easier to explain. The rotated component matrix is given

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FAC, ILC, DIST and SOIL variables in order of priority. The common characteristics of these variables are related to physiographic, edaphic, climatic characteristics and distance to the market, in other words its relationship to natural climate and location. The elevation of the settlement place and its distance to the market directly affects the forest area and income level per capita. Moreover, as the elevation and distance to the market increases, the development of the village is affected negatively. Therefore, we name this factor as geographic location of the village regarding elevation and distance to the market and we take ELEV with the highest factor loading (−0.902) as representative for this factor. Factor 2 consists of the TOP, NUH, ANC, AIRP and POD variables. These variables which are related to population, population increase, population density and number of houses essentially represent village size. Because of migration in the region, while the population of the villages close to the city increases, people dealing with livestock rearing decreases. Moreover, villages close to the city are urbanising with social and cultural alterations (Çağlar, 1986; Acun and Geray, 1980). On the other hand, while the population decreases, villagers dealing with livestock rearing increases in the villages distant from the city. Villages close to the city derive their incomes from trade and services with the city rather than from agriculture and livestock, due to lack of modern livestock rearing practices in the region and insufficient government policies. Factor 2 is named as size of village and TOP which has the highest factor loading is taken as indicator for this factor. Factor 3, consisting of the EROS, SDEEP and LCC variables with positive correlation among each other, represents the productivity of

Table 2 Total variance explained. Factors Initial eigenvalues

Rotation sums of squared loadings

Total % of variance Cumulative % Total % of variance Cumulative % 1 2 3 4 5 6 7 8 9 10 11 12

8.09 3.15 2.34 2.23 1.93 1.67 1.48 1.43 1.28 1.15 1.10 1.04

22.48 8.74 6.50 6.21 5.36 4.63 4.10 3.97 3.55 3.20 3.05 2.89

22.48 31.22 37.72 43.92 49.28 53.91 58.01 61.98 65.52 68.73 71.78 74.66

5.57 3.35 2.53 2.10 2.02 1.91 1.80 1.61 1.58 1.53 1.52 1.36

15.48 9.32 7.04 5.84 5.60 5.29 4.99 4.48 4.38 4.25 4.23 3.77

243

15.48 24.79 31.83 37.67 43.27 48.57 53.56 58.03 62.42 66.67 70.90 74.66

The bold number represents total variance explained by 12 factors.

in Table 3 and the derived factors are named and interpreted based on the factor loadings in the rotated component matrix. In order to clearly see the variable groups, the dominating factors (with absolute factor loadings larger than 0.5) that determine the 12 factors are shown in bold in Table 3 (Harman, 1967; Bennet and Bowers, 1977; Mucuk, 1978; Daşdemir, 1996, 2005). Four of the 36 variables do not have dominating factor loadings, namely SLOPE, RAIN, ROAD and OVA, their largest factor loading is added respectively to factors 1, 3, 9 and 12 and this is indicated in italic format. The first component is the most important factor which explains over 15% of total variance. Factor 1 consists of the ELEV, TEMP, HUM,

Table 3 Rotated component matrix. Variables

Factors (components) 1

ELEV TEMP HUM FAC ILC DIST SLOPE TOP NUH ANC AIRP POD EROS SDEEP RAIN LAND SOIL LCC APRT SPRT WRT POP NAC PAC RIVR GRA TWC WATR IRT ROAD FRA MPA COOP SCHE HSE OVA

2

3

4

5

6

7

8

9

10

11

12

− 0.902 0.896 0.821 − 0.769 0.691 − 0.610 0.394 0.864 0.829 − 0.570 0.525 0.509 0.859 0.834 0.373 0.563 0.515

0.808 0.617 0.610 0.802 0.778 0.637 0.796 0.718 0.635 0.839 0.579 0.806

The bold numbers represent 'dominating factors' (larger than 0.5 in absolute value) and do not represent significance.

0.793 0.527 0.336 0.748 0.670 0.800 0.554 0.711 0.458

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village land. High productivity and income can be derived from soil which is deep, where the land capability class is high (classes I and II) and has low levels of erosion. Therefore, this factor is named as productivity of land and EROS is taken as representative for this factor. Factor 4 consists of the LAND, SOIL and LCC variables with positive correlations among each other. Matching the use of the land to the right capability class (forest, grassland, lawn, dry farming, wet farming etc.) and compatibility of soil type will increase production and income. Therefore, Factor 4 is named as type of land use and LAND is taken as representative for this factor. Factor 5, consisting of the APRT, SPRT and WRT variables with positive correlation among each other, is the ratio of the active, school-age and female population to total population. The main contribution to family income often comes from women, because mainly women work in areas of agriculture, livestock and marketing of the products (milk, butter, cheese, apple, chestnut, walnut, strawberry, other fruits and vegetables etc.) in the villages of Bartın province. In addition, the presence of the share of school-age population reveals the importance of a young population for development. An increase in the active population leads to higher production, productivity and village development. Therefore this factor is named as active population of village and APRT is taken as indicator for this factor. Factor 6, which consists of the POP, NAC and PAC variables, represents the share of individual agriculture area which is used and areas where villagers have planted poplar trees. Because income and development of villages close to the city largely depend on industrial and trade sectors, these villagers do not deal with agricultural work; they either leave the agricultural areas empty or plant poplar trees. In other words, unproductive non-agricultural areas do not contribute much to income and development and are assigned to poplar tree plantations to generate additional income. Therefore, Factor 6 is named as poplar production areas and POP is accepted as representative for this factor. Factor 7 consists of the RIVR and GRA variables which have a positive correlation among each other. This factor indicates that areas covered with greenhouses, which are generally located on flat and productive lands, are often close to rivers. It can be said that agricultural production, income and consequently economic village development increases, when the proximity to a river improves, due to access to irrigation, transport and possibly hydro energy as supplied by the river. Therefore, this factor is named as proximity to a river according to the dominant variable and RIVR is accepted as representative for the factor. Factor 8 consists of the TWC variable. Villages with houses equipped with modern toilets, which are connected to a drainage system and with water are more developed. This factor consisting of one variable only is labelled as housing comfort. Factor 9, which consists of the WATR and IRT variables with correlation among each other, shows that characteristics of drinking water (demijohn, network water, fountain, well etc.) vary together with the level of literacy. Clean and a good drinking water network are found in relatively developed villages with a high rate of literacy, whereas poor quality water from a well or stream are used as drinking water in villages with a low rate of literacy. Factor 9 is named as characteristics of drinking water and WATR is accepted as representative for the factor. The FRA and MPA variables represent Factor 10. This factor indicates that income and economic development of the village will increase when fruit areas, vineyards and relatively productive meadow and pasture areas for livestock are present. Because of this, Factor 10 is named as productive fruit areas following the FRA variable, with high factor loading (0.748). Factor 11, consisting of the COOP and SCHE variables, represents consciousness of organized action that benefit the village depending on the presence of schools and the education level in the village.

Organized action is needed for marketing of products serving a collective benefit. Cooperatives as an example of organized action, is an important indicator for the socio-economical and cultural dimension in village development (Çağlar, 1986; Daşdemir, 2002; Mülayim, 2003; Atmiş et al., 2007, 2009). Therefore Factor 11 is named as cooperativization and COOP is the indicator. Factor 12, consisting solely of the HSE variable, is mainly related to the presence of social infrastructure investments (nurse, doctor, village clinic, health organization, hospital etc.) in the village. Therefore Factor 12 is named as social infrastructure investments in the village and HEALT is accepted as representative for the factor. Based on the explication of the factor analysis, the most important factors affecting development in economic, social, human and environmental terms in the villages of Bartın province and the variables representing these factors with their weights are presented in Table 4. Development index (DI) is defined as the sum of the standardised Z-values of the indicator variables of the factors multiplied with the factor loading consisting of the weights of the variables, which is the factor loading of the particular variable as obtained in the factor analysis as shown in Table 3 and in the last column in Table 4. Thus, DI consists of weighted combination of 12 factors ranging from geographical location to social infrastructure investments in Table 4. It is a comprehensive and multi-dimensional index measuring village development. The resulting ranking of villages according to DI values is presented in Table 5. And this is presented graphically in Fig. 2. 3.2. Explaining variation in village development To explain variation in village development, the DI values, are first taken as the dependent variable and the effects of the remaining 24 variables minus ILC (see below) on DI are investigated by undertaking a multiple regression analysis. Furthermore, although it did not come forward as an important variable in the principal component analysis but which was part of the first factor, the classical indicator of development, namely income level per capita (ILC) will be used as a dependent variable as well, using the same 23 descriptive variables. Finally, the relationship of ILC with 12 variables, which constitute the DI will also be shown, in order to show the interrelationship between the two measures of development. The outcomes of the resulting 3 regression models are presented in Table 6. According to the first regression model, 65% (R2 = 0.654) of the development of the villages of Bartın which have 12 components (or DI) can be statistically explained by TEMP, NAC, and GRA, whereas ANC and NUH are weakly statistical explanatory. Hence, the

Table 4 Factors affecting development in the villages of Bartın, their weights and indicator variables, and the variables' weights. Factor no. Name of factor

Weight of Indicator variable Weight of factor (%) of factor variable

1 2 3 4 5 6 7 8 9

15.48 9.32 7.04 5.84 5.60 5.29 4.99 4.48 4.38

ELEV TOP EROS LAND APRT POP RIVR TWC WATR

− 0.902 0.864 0.859 0.808 0.802 0.796 0.839 0.806 0.793

4.25 4.23 3.77

FRA COOP HSE

0.748 0.800 0.711

10 11 12 Total

Geographical location Size of village Productivity of land Type of land use Active population Poplar production areas Proximity to a river Housing comfort Characteristics of drinking water Productive fruit areas Cooperativization Social infrastructure investments

74.66

B. Yılmaz et al. / Forest Policy and Economics 12 (2010) 239–249 Table 5 Ranking of the villages according to their DI values.

The four shaded villages, were in different groups according to the two grouping methods (discriminant analysis and the natural order according to the DI values).

Fig. 2. Map with the location of villages and their level of development.

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Table 6 Results of multiple regression analyses for explaining variation in village development.

(Constant) SLOPE SOIL LCC SDEEP DIST (10− 2) TEMP RAIN HUM FAC (10− 2) ANC PAC NAC MPA (10− 2) GRA (10− 2) OVA (10− 2) AIRP (10− 2) POD (10− 2) WRT NUH (10− 2) ROAD IRT SPRT (10− 2) SCHE R2 F-statistic

1. Model: dependent variable DI

2. Model: dependent variable ILC

3. Model: dependent variable ILC

Coefficient

Coefficient

Std error

Coefficient

Std error

1.271 0.046 0.039 0.035 0.060 0.497 0.075 0.106 0.048 0.585 0.139 0.023 0.038 0.036 0.326 0.074 0.247 0.052 0.010 0.075 0.037 0.008 0.225 0.068

2.424⁎⁎⁎ − 0.164⁎⁎⁎ 0.374⁎⁎⁎

0.428 0.025 0.130 0.068 0.028 0.343 2.054 4.133 0.063 0.026 0.040 0.106 0.125

− 37.395⁎⁎⁎ − 0.102 − 0.018 0.200 0.628 − 0.681 2.063⁎⁎⁎ 1.034 − 0.259 − 6.028 − 1.588⁎ − 0.094 0.838⁎⁎⁎ 0.379 5.437⁎⁎ 0.697 − 1.060 0.468 0.070 1.026⁎ 0.257 0.041 1.213 0.693 0.654 5.908

Std error

− 1.511 − 0.046 − 0.036 0.006 − 0.096⁎ 0.388 0.305⁎⁎⁎

10.959 0.398 0.336 0.300 0.517 4.284 0.649 0.910 0.410 5.040 1.199 0.195 0.330 0.307 2.812 0.636 2.130 0.446 0.088 0.651 0.320 0.066 1.941 0.590

− 0.024 0.109⁎⁎⁎ 0.540 − 0.013 0.012 − 0.001 0.039 0.277 0.112⁎ − 0.221 0.012 − 0.013 0.108⁎ 0.055⁎ − 0.031⁎⁎⁎ − 0.031 0.085 0.713 7.788

(Constant) ELEV(10− 2) TOP (10− 3) EROS LAND APRT (10− 2) POP (10− 4) RIVR (10− 2) TWC WATR FRA (10− 2) COOP HSE

− 0.065 − 0.038⁎ − 0.228 0.823 0.046 − 0.043 0.023 0.142⁎⁎⁎ 0.118 0.333⁎⁎⁎

0.629 11.717

⁎ Significant at the 0.20 level (P b 0.20). ⁎⁎ Significant at the 0.10 level (P b 0.10). ⁎⁎⁎ Significant at the 0.05 level (P b 0.05).

temperature, unused agricultural area per capita, greenhouse areas, animal number per capita, and numbers of households are important factors to determine village development in terms of DI. According to the second regression model, 71% (R2 = 0.713) of the variations in income level per capita (ILC) in the villages of Bartın can be explained by TEMP, HUM and IRT, whereas SDEEP, OVA, NUH and ROAD are weakly statistical explanatory. According to this model, temperature, humidity, illiterate ratio in total population, soil depth, the amount of open vegetable areas, numbers of households, and road type are important factors determining the level of village development in terms of income. The third regression model investigates the interrelationship between the two measures of development, namely the explanatory power of the main components of DI for variation in ILC. This model is essentially different from the first two ones in the sense that it studies the interrelation between the two measures of development. In this regression 63% (R2 = 0.629) of the variation in income level per capita can be explained by ELEV (from Factor 1), TOP (from Factor 2), FRA (from Factor 10), and HSE (from Factor 12), whereas LAND (from Factor 4) is weakly statistical explanatory. According to this model, the level of income is influenced by elevation, total population number, the amount of fruit area, health services and type of land use. From these regressions we learn that the set of descriptive variables somewhat better explains the variation in the classical indicator of development, rather than the newly proposed indicator of development, although the difference in R2 is relatively low. Interestingly, in both regressions the importance of temperature

Table 7 Village groups according to DI before discriminant analysis. Village groups

DI

Number of villages

Group 1 Group 2 Group 3 Total

≥3 3–(− 2) ≤−2

20 49 27 96

and, to a lesser extent, the number of households comes forward. This implies that villages with higher temperatures and more households are generally more developed. The result of the third regression has to be interpreted in a different light, because it is a somewhat unusual cross regression between two dependent variables. Five out of 12 variables are contributing to the explanation in variation in income. Hence, the other 7 variables are typically non-monetary and describe other dimensions of development. This is a motivation to use DI, as developed in this paper, rather than the classical measure of development ILC.

Table 8 Standardised canonical coefficients and some parameters of discriminant functions.

ELEV SLOPE LAND SOIL LCC SDEEP EROS RIVR DIST TEMP RAIN HUM FAC ANC PAC NAC MPA GRA OVA FRA

Function 1

Function 2

− 0.099 0.200 0.561 0.741 − 0.452 − 0.266 0.937 0.848 0.067 − 0.450 0.113 0.240 0.093 − 0.169 0.196 − 0.588 0.002 0.107 0.020 0.710

1.684 0.190 − 0.309 0.180 0.152 0.690 − 0.762 − 0.046 − 0.025 0.264 0.020 0.206 − 0.359 0.153 − 0.377 − 0.249 − 0.105 − 0.126 0.156 0.204

POP TOP AIRP POD WRT NUH APRT ROAD WATR TWC IRT SPRT SCHE HSE COOP ILC Eigenvalue % of variance C. correlation Chi-square

Function 1

Function 2

0.974 0.641 0.206 0.030 0.512 − 0.245 0.411 − 0.099 0.529 0.450 − 0.038 − 0.003 − 0.310 0.801 0.960 0.266 8.289 88.2 0.945 224.547

0.814 − 0.499 0.326 − 0.049 0.207 0.349 0.053 − 0.150 − 0.240 − 0.328 0.146 − 0.274 − 0.575 0.478 0.553 0.236 1.107 11.8 0.725 56.272

B. Yılmaz et al. / Forest Policy and Economics 12 (2010) 239–249

247

Table 9 Results of classification of village. Group

Original count

Percentage

Predicted group membership

1 2 3 1 2 3

1

2

3

19 0 0 95.0 0 0

1 47 1 5.0 95.9 3.7

0 2 26 0 4.1 96.3

Total

20 49 27 100 100 100

3.3. Grouping of the villages according to development degree The villages were divided into three groups as shown in Table 7 according to DI values as shown in Table 5. This section tests the appropriateness of this grouping by employing a discriminant analysis based on the 36 variables. As a result of analysis, two discriminant functions emerged for grouping the villages according to degree of development. Standardised canonical coefficients and some statistical parameters of discriminant functions are presented in Table 8. The classification results according to the discriminant analysis are given in Table 9 and the new village grouping is presented in Table 10, Figs. 2 and 3. Comparing the discriminant analysis with the DI classification (Table 5), there are only four mutations, namely a village (Gözpınar) in the first group shifted to the second group; two villages (Çakırömerağa and Doğaşı) in the second group shifted to the third group and a village (Büyükkızılkum) in the third group shifted to the second group. Hence, the success rate of classification based on DI, which uses only one third of all variables, is 96%. Interestingly, while inspecting Fig. 1, it can be seen that 12 of the 13 villages in the Arıt county belong to the lowest development group, showing the relative remoteness of this county from the urban center. There is no clear geographical pattern among villages in the Center county, which has a mix of developed, developing and underdeveloped villages. 3.4. Strategies for improvement of rural and village developments Table 11 presents the main characteristics of the three village groups by presenting the average values of the main variable of each factor. From these averages strategies and policies which can increase the development of a village in each village group can be determined. In addition the table shows the income level ranking per capita (ILC), the average DI value and the proportion that it should take from 100 units' resource allocation or resource allocation index for balanced development in provincial terms. The resource allocation indexes of village groups are calculated as follows: DI value of first village group is 4.94 and one unit increase in welfare means that DI value becomes 5.94. DI value of second village group is 0.41 and its DI value should increase to 5.53 (5.94–0.41) to reach the same welfare with first village group. DI value of third village group is

Fig. 3. Villages' distributions according to discriminant functions 1 and 2.

−3.95 and its DI value should increase to 9.89 (5.94+3.95) as absolute value to reach the same welfare with first village group. Total coefficient of resource allocation is 16.21 (1+5.53+9.89). To reach the same welfare level and for a balanced development; proportions that the village groups should take from 100 units' resource allocation are (100/16.21)⁎1=6, (100/16.21)⁎5.53=34 and (100/16.21)⁎9.89=60 for first, second and third village groups respectively. The characteristics in Table 11 show the disparity among the three groups in a quantitative manner. According to this, three separate rural development strategies and policies tailored towards each village group can be developed.

3.4.1. Strategy and policy for development of the first village group This group in general consists of villages that are close to the city center and rivers and have a relatively high total and active population. The lands are productive lands, situated at a low altitude and only lowly eroded. These lands are commonly used for settlement purposes and tourism, whereas a substantial part is used for poplar tree production, fruit plantations and vineyards. Income is often generated from the trade and services sector in the urban center rather than from agriculture and livestock. The people are literate, live in comfortable houses that are connected to the water network. There are various cooperatives and other infrastructure like health services. Moreover, the villages in the first group have a high income level ranking per capita and a high development index (4.94). Therefore, the first group is most developed. For this group, the strategy and policy for development are to eliminate the deficiencies in physical (way, bridge, drain etc.), health and social (village clinic, hospital, school, cooperatives etc.) infrastructures and retain their functionality. However, since these villages

Table 10 Village groups according to development degrees after discriminant analysis. Village groups

Number of villages

1st degree developed 19 (developed) 2nd degree developed 49 (developing)

3rd degree developed (underdeveloped)

28

Names of villages Epçiler, Geriş, Kızılelma, Kayadibikavlak, Şiremirçavuş, Güzelcehisar, Terkehatipler, Esenyurt, Dallıca, Kurtköy, Karasu, Gürpınar, Gürgenpınarı, Gecen, Çakırkadı, Uğurlar, Kayadibiçavuş, Okçular, Budakdüzü. Gözpınar, Hasanlar, Kocareis, Hacıosmanoğlu, Uzunöz, Çayır, Karahüseyinli, Terkehaliller, Akçalı, Akbaba, Büyükkıran, Çeştepe, Tabanözü, Muratbey, Kabagöz, Akpınar, Epçilerkadı, Kutlubeytabaklar, Ağdacı, Kaşbaşı, Serdar, Alibaş, Derbent, Arıönü, Tuzcular, Yanaz, Beşköprü, Kutlubeydemirci, Karainler, Bayıryüzü, Küçükkızılkum, Kutlubeyyazıcılar,Yıldız, Karamazak, Sipahiler, Hatipler, Çakırdemirci, Şahne, Akmanlar, Ören, Ahmetpaşa, Kayadibi, Akgöz, Gerişkatırcı, Fırınlı, Kaman, Çiftlikköy, Çukurbük, Büyükkızılkum. Çakırömerağa, Doğaşı, Şiremirtabaklar, Çamaltı, Topluca, İmamlar, Çamlık, Kümesler, Karaköyşeyhler, Ulugeçitambarcı, Gençali, Kirlik, Kumaçorak, Aydınlar, Ulugeçitkadı, Saraylı, Kışlaköy, Gökçekıran, Darıören, Mekeçler, Çöpbey, Şahin, Esbey, Hacıhatipoğlu, Kayacılar, Söğütlü, Turanlar, Yeniköy.

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Table 11 Characteristics of village groups in terms of the 12 indicator variables, ILC, DI and resource allocation index. Factor no.

Name of factor

Indicator variable

1st group

2nd group

3rd group

Average of 96 villages

1 2 3 4 5 6 7 8 9 10 11 12

Geographical location Size of village Productivity of land Type of land use Active population Poplar production areas Proximity to a river Housing comfort Characteristics of drinking water Productive fruit areas Cooperativization Social infrastructure investments ILC DI Resource allocation index (%)

ELEV TOP EROS LAND APRT POP RIVR TWC WATR FRA COOP HSE

74.74 806.16 1.84 3.42 68.43 226.91 4.79 2.00 3.32 115.50 0.32 0.32 2.53 4.94 6.00

88.16 603.67 1.67 2.84 65.17 92.28 4.53 2.20 3.22 58.40 0.04 0.02 2.24 0.41 34.00

306.21 489.93 1.34 2.48 59.68 45.82 4.07 2.21 2.66 28.50 0.03 0.00 1.76 − 3.95 60.00

146.56 611.16 1.61 2.82 64.24 105.30 4.44 2.16 3.05 60.77 0.09 0.07 2.16 0.00 100

are already quite developed a relatively lower share of the provincial resources should be allocated in order to move towards more equality. 3.4.2. Strategy and policy for development of the third village group This group in general consists of villages that are distant from the city center and rivers and have a relatively low total and active population. The lands are less productive, due to poor soil quality, high altitudes, mountainous and erosion. These lands are commonly used for grazing, forests and rain-fed agriculture, whereas there is hardly any poplar tree production, fruit plantations and vineyards. Income is often generated from agriculture and livestock. The people are often illiterate, live in primitive houses and are not connected to the water network. The number of cooperatives is low and there is hardly any other infrastructure like health services. Moreover, the villages in the third group have a low income level ranking per capita and a low development index (−3.95). Therefore, the third group is the least developed. For this group, the strategy and policy for development are to accelerate investments in physical, health and social infrastructures. Moreover, since these villages are the most backward the highest share of the provincial resources should be allocated, in order to move towards more equality. 3.4.3. Strategy and policy for development of the second village group This group is situated between the two extreme groups as mentioned above and has characteristics of groups 1 and 3 mentioned in Table 11 at an average level. In this group more than half of all the villages are situated, which are developing villages where the physical infrastructure, economical, health and social infrastructure investments (way, bridge, watering installations, drain, village clinic, hospital, school etc.) are being undertaken in moderate amounts and the DI of this group is slightly positive (0.41). For this group, the strategy and policy for development are to complete investments in physical, health and social infrastructures. Moreover, since these villages are in the process of development an average share of the provincial resources should be allocated, in order to move towards more equality. 4. Conclusion Development, which is generally expressed in gross domestic product (GDP) per capita terms, is limited; there is a need for a more comprehensive measure (UNDP, 2007). This paper uses a method that can handle a great level of detail of quantitative information and distil out the main characterising factors and converting this into a more refined index. The resulting grouping of villages can be statistically confirmed to be precise and reliable. While establishing the DI, factors driving rural development in Turkey can be derived as well. Grouping

the villages according to their relative development level can be used to develop rational development strategies and policies targeted to each group of villages, to form a model for evaluating data regarding rural development, to help effective utilization of resources and providing sustainable development. This study handled 96 villages in the Bartın province in Turkey and derived important factors affecting rural development with an objective multi-dimensional approach. Moreover, 75% of the village development depends on the following 12 factors ordered by level of significance: (1) geographical location, (2) size of village, (3) productivity of land, (4) type of land use, (5) active population, (6) poplar production areas, (7) proximity to a river, (8) housing comfort, (9) characteristics of drinking water, (10) productive fruit areas, (11) cooperativization and (12) social infrastructure investments. A multidimensional development index (DI) is developed based on these 12 factors and the 96 villages are divided into three groups (developed, developing, and underdeveloped). A discriminant analysis confirms the accuracy of this approach. A multi-dimensional rural development strategy is developed consisting of a welfare maximising resource allocation. A new index (DI) was developed to measure village development in the study. DI consists of weighted combination of twelve factors ranging from geographical location to social infrastructure investments, whereas the HDI of the UNDP consists of three variables, namely life expectancy, level of education and real national income per capita. So, DI is a more comprehensive index than HDI, its meaning is different from HDI, and it is suitable to measure village development, whereas HDI is generally used to measure the level of development at the country level. Both urban and rural development needs to be considered for a balanced development in Bartın province. This study shows that underdeveloped villages need to have the highest priority for development. Investments of physical, social and health infrastructures in developed villages are already sufficiently undertaken and only their functionality needs to be maintained. However, the current level of infrastructure investments is insufficient for underdeveloped villages and these investments need to be prioritised. Prioritising the least developed villages will lead to the maximum increase of welfare, because it has the highest marginal benefit (Pigou Criterion). This approach is also in harmony with an effective use of the scarce resources, the strategy of balanced development (Dinçer, 1996) and the strategy of rural development of Turkey. The villages with a high number of livestock are relatively less developed, because of lack of modern livestock rearing practices and insufficient governmental support on this issue. Therefore, encouragement of modern livestock rearing practices, an assessment of the unoccupied agriculture areas, which could be used for hayfield and grassland to support livestock, could improve village development. In

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addition, allocation of land for poplar tree production, especially in the areas where intensive agriculture is not possible could enhance village development. Moreover, development can be encouraged by improving the availability of fertilizers, irrigation, the number of greenhouses and vineyards. Finally, the presence of marketing cooperatives and improved education of the people would stimulate village development. In this study, factors affecting rural development are determined using a multivariate statistical analysis. This approach or methodology is based on the multi-criteria assessment of village development rather than being based on only one criterion (national income per capita etc.). It simultaneously measures development in terms of multi-dimensions. For this reason, this methodology does not have the shortcomings of the single-criterion methods. Moreover, this methodology includes not only economic variables but also social, demographical, human–cultural, infrastructural and especially the use of environmental and natural resource variables. Hence, the environment and natural resources have an important role in rural development (Farrington and Lomax, 2001; Rizov, 2005; Narain et al., 2008). Therefore, this methodology is well-suited for determining the development level of villages in Turkey. Moreover, it is scientific, objective, consistent and uses multiple variables, and it is also readily applicable and understandable, where the derived factors can be quantified in a deterministic manner. However, the names, definitions and weights of the variables can conceivably change over time and place in the country. Hence, the names, definitions, and weights of the variables should be discussed and revised periodically according to the changing conditions. Consequently, the results of this study can be a guide for similar rural development studies. It contributes to determining effective rural development strategies and policies towards an increasing social welfare. Also, the methodology developed in this paper can be used to monitor village development and to assist in effective use of resources for sustainable forestry and development in Turkey. Acknowledgments We would like to thank the staff of the Bartın Governorship who helped in providing data for this study and Banu Bayramoğlu-Lise for the translations. We are also grateful to two anonymous referees and the editor of this journal whose valuable comments have contributed to improve this article. Remaining errors are ours. References Acun E., and A. U. Geray. 1980. Forest villagers to become urbanized and relationships between forest and village (Example of Safranbolu). İ.U. Publication of the Faculty of Forestry No. 279, Istanbul. 85 pp. Anonymous, 2007. National rural development strategy. www.tarim.gov.tr/duyurular/ ukks.pdf, 29.09.2007, 43 pp. Ashley, C., Maxwell, S., 2001. Rethinking rural development. Development Policy Review 19 (4), 395–425. Atmiş, E., Daşdemir, İ., Lise, W., Yıldıran, Ö., 2007. Factors affecting women's participation in forestry in Turkey. Ecological Economics 60 (4), 787–796. Atmiş, E., Günşen, H.B., Bayramoğlu-Lise, B., Lise, W., 2009. Factors affecting forest cooperative's participation in forestry in Turkey. Forest Policy and Economics 11 (2), 102–108. Bennet, S., Bowers, D., 1977. An Introduction to Multivariate Techniques for Social and Behavioural Sciences. MacMillan Press, London. 149 pp. Brenmand, M.A., Luloff, A.E., 2007. Exploring rural community agency differences in Ireland and Pennsylvania. Journal of Rural Studies 23, 52–61. Çağlar, Y., 1986. Forest Villages and Activities Related to Development of Them. MPM Puclication No. 340, Ankara. 216 pp.

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