Necessary Entrepreneurship Quorum, stand for a necessary base or sufficient number of rural enterprises to exist and to be a dynamic force for new start-up creation (Schumpeter., 1934), which is a prerequisite for growth and is important for employment. Besides, it facilitates the creation of markets and business opportunities (Norman 2009) and leads to business specialization (Walzer 2009). N.E.Q. has an overall positive effect on knowledge sharing and is shown to be of great importance to business creation (Walzer 2009) on empirical grounds.
Papzan, et al. (2008) extracts prominent determinants of rural entrepreneurship success depicted by market and positioning in joint with brand ideas and easing of red tape procedures, among many other factors.
Recent studies on the determinants of micro-enterprise success, by Schiebold (2011), includes following variables: features of entrepreneurs, infrastructure, institutional framework, culture, social capital, finance issues, terms of trade, informality. The set is used to develop a comprehensive model of a rural entrepreneurship econometric model.
4.3. Approaches to Rural Entrepreneurship Initiative
Rural entrepreneurship growth strategy, with its two approaches, bottom-up and top-down, plays important role in balancing community and individual entrepreneurial interest and goals. Bottom-up policy generates opportunities that are coming from the communities itself, spur entrepreneurial climate, with innovative processes and is oriented to enterprise growth (UNIDO, 2003), leaving ownership to members. Top-down approach, created for the local market, brings productiveness if supported by the creativity of bottom-up acters.
„ Focus on transformation and diversification of micro and small enterprises to growth oriented activities and on rising productive capacity in order to enable them to participate in the mainstream economy of the nation" (UNIDO, 2003)
Bottom up strategy allows rural entrepreneurial innovativeness and motivation to freely flourish into real activity and develop entrepreneurial behavior, contributing to entrepreneur's concentration and promotion.
Successful economies are the ones balancing the bottom up and top down approach.
4.5. Necessity Vs Opportunity Rural Entrepreneurship
If one strives to build rural growing regions, he/she has to acknowledge of two entrepreneurial types as activities, emerging from necessity or from opportunity. The difference in two, will shape the region and have an effect on production diversity and entrepreneurial climate. Necessity based activity, arises due to the lack of opportunities in the local region and in its nature does not impose growth, but subsistence production, while opportunity based (Schumpeterian) shows creative destruction (Goetz., Et al, 2010) and is based on the push factors (Alsos et al, 2011) represented by creativity and growth of enterprises, destroying the non efficient ones. This distinction of entrepreneur's activity is linked to the level of process activity depicted in Kuznets diagram:
Table 2: Kuznetz Diagram
Economic Development Base
Feature
Factor-based
Efficiency based
Innovation-based
Main organization
form
Self-employment
/Proprietorships/
Wage and
salary employment
Opportunity
or necessity entrepreneurship
Income level
Lower
Medium
Higher
Dominant Sector
Natural resources
Manufacturing
Services
Sources of growth
Abundance of resources
Gap-filling; copy cat
New products, processes, services
Firm size
Smaller
Larger
Small and large
Source: Goetz, J., et al, Evaluating U.S. Rural Entrepreneurship Policy, page 21, Journal of Regional Analysis and Policy, 2010, JRAP 40 (1): 20-33, adapted from Acs et al, 2008.
Subsistence rural entrepreneurship is linked with factor-based and as a process of evolution and diversification of rural enterprises arise, it leads to innovation-based entrepreneurial activity. Developed countries are characterized by innovation, while developing has a long path to pass, shifting from factor-based and efficiency based. The future for rural economy largely depends on countries' ability to develop innovative rural enterprises, especially knowing that revenues in classical agriculture are on the lowest return possible and usually does not offer work prospects.
The more dynamic and innovative enterprises the country has, the promising and fruitful environmental climate exists and the greater are the spillovers between them. The less favored employing conditions are in the region, the more subsistence entrepreneurship exists, streaming for job creation, while downturn (Goetz. et al., 2010) economic conditions prevail, but the drivers of economic activity are Schumpeterian entrepreneurs, who innovate processes and industries, products and markets.
4.6. Organizational Rural Entrepreneurship
In the situation of disperse and small size of rural production units (parcel of land), characterized by inefficient production, knowledge gap and a slow or non-existent capitalization, empirical evidence shows the positive influence of the One-District-One-Industry strategy (hereinafter ODOI) in organizing rural activity (Radiah, et al, 2009). Effectively implemented, ODOI, requires state involvement in marketing operations of the district, drafting marketing strategy, export orientation, supporting rural enterprises with necessary training in the fields of missing knowledge, that is highly lacking in the most of the donor's projects'. The empirical evidence from the Malaysian government in pursuing an ODOI strategy for food, services, crafts and industry, taken up from OTOP, One-Tambon-One-Product strategy, developed by Thailand (Radiah, et al, 2009) have provided real proof of success for rural enterprises. Data compiled from the ground list most important factors of success in ODOI strategy:
Government support in training and marketing activities,
Expert and advisors in the field of management and operations,
ICT (information and telecommunication technology) knowledge,
Personal motivation to success,
Rural infrastructure,
Domestic government incentive policies for conducive environment.
Encouraging production of one commercial product minimum per production unit, the government provides no direct support to rural firms, but new framework settings that come in support as:
Human resource training in managerial skills and operational skills,
Technical assistance and research and development assistance,
Marketing and fair trade support,
Development of distributive networks and trade centers negotiating management.
When it comes to distribution of the products of small rural firms, the difficulties arise if the proximity of trade centers, where low price products are readily available to consumers. If let alone, producers are forced to sell underpriced products, that in the short term causes losses to firms and lead to firm liquidation. State intervention in this field, by creating and spurring the creation of distribution networks and providing enterprises with information relevant for exporting and linking to external distributive networks, facilitates and contributes to rural entrepreneurship growth.
4.7. Country Case Studies
Among the plethora of different and successful models of regional and organizational rural enterepreneurship network, few of them are selected.
Moy Valley Resources, in Ballina, Australia as a successful example of the non-governmental organization in conjunction with local rural entrepreneurs, have contributed, mitigating effects of unfavorable environment for entrepreneurship creation, by developing a network of local communities at first and working on the solutions against depopulation and unemployment in the region. The organization supports local entrepreneurs with the training center, investment schemes, marketing and branding and management skills.
In order to tackle people's productive entrepreneurial thinking and creativity, the Norwegian government has decided to introduce a tool, that used in the early period of ones live has an important effect in years to come. It relates to the implementation of „Pupil Entreprise" (Petrin, 1994) as a part of the pupil course and it showed to be excellent in targeting the group needs and regional development.
Novn'na Studio in Slovenia in the Upper Mezza Valley, formed as a Community Partnership Pogramme (Petrin, 1994), combines locally based resources to overcome impediments to rural enterpreneuship, through creating a market for a single product and creating pools of producers of complex products.
Econometric Model of Rural Entrepreneurship Success in Bosnia and Herzegovina
Storey (1994) identified key components to the growth of SMEs: the characteristics of the entrepreneurs; the characteristics of the SMEs; and the type of strategy associated with growth (contextual variables).
We explore contextual elements of SME development.
Picture 8: Ubaciti izvor models
5.1. Limitations
Absence of total rural entrepreneurship database, made its impact on the detailed data analysis. The time frame of a month of data collection and a month of data analysis had effected the depth of results obtained and financial constraints had its impact on the scope of analysis. Nevertheless, we strongly believe that this research has made a significant step further in rural entrepreneurship analysis to which author strive to contribute in the future.
5.2. Data collection
Data collection has been divided in two phases:
I PHASE: pilot survey, conducted on 10 randomly selected rural enterprises (31.05-04.06.2012).
II PHASE: RES2012, (04.06.- 01.08.2012).
III PHASE: SSREI2012, (01.-05.08.2012).
5.3. Sampling
This master thesis draws on most recent empirical findings on determinants of rural entrepreneurship in Bosnia and Herzegovina, covering agro-business, rural tourism, agro- tourism and farming. For this purpose RES2012 (Rural Entreprise Survey has been developed, see appendix 7).
The database consists of various sources, of over 1.300 entities, and there exists no single database of rural entrepreneurhip in Bosnia and Herzegovina. The sources are confidential and part of the agreement with various institutions, NGOs and agencies in BiH. The usage of the database is solely used for the purpose of drafting a master thesis. From the database we have selected and mapped 300 rural businesses for our sample (response rate was 70 percent, we have end-up with 210 respondents/surveys). The constraints we have placed on the sample, are originality and number of employees of up to 50 in order to avoid big businesses, as those are linked to large corporations located in urban areas.
The sampling selection is done in two stages:
Geographical clustering of agricultural activities (see picture N). We selected 3 main clusters, one cluster in the northern part, one in the central part (close to the capital), and one in the southern part of the country. This method should help us collect more information about the structure of the businesses in the cluster (since all databases are not necessarily comprehensive) that should be used in the second stage of sampling.
For clustering see Appendix 4A and 4B.
Stratification of the sampling frames in each cluster was made by the type of business (see Picture N1) and for that we have considered how the rural businesses are distributed by business type according to statistical reports, databases available, as well as other sources. A random selection of the appropriate number of businesses is done from each stratum (see appnedix 4C).
The rural businesses are grouped into the following types:
Fruits and vegetables (grape production) = a, b (in STATA)
Meat and dairy production (dairy: milk, cheese, butter - cattle related product) = c (in STATA)
Fishery = d (STATA)
Other agriculture (honey =e, aromatic plants= f)
Rural tourism (b&B, hostel, hotel, restaurants) = j
Rural services (shops, ) = i
Other (production of fertilizer or raw materials, production of agriculture machineries, food processing (jam, wine)) = l
Tobacco = h
Vine and grapes = g
Pastry, bread and cookies = k
Sampling by the size of the business. Maximum number of employees is 49, as we want to avoid analysing large rural businesses.
5.4. Model of Rural Entrepreneurship
We have two models for rural entrepreneurship success. Model M1 has dependent variable dummy variable, showing the success through the percentage change in the number of employees. For M1, observable variables are factors, determining rural business success, including the list of obstacles they face, where the magnitude of the factors would be measured and its impact on survival and success of a rural business. Logistic model (Watson and Stock, 2007), is used for M1 and M2 respectively. Model M2 measures the intention to expand the rural business. Following variables might be endogenous:
Written business plan (one of the requirements for loan application),
Export or not,
Product advertisement,
Obstacles: less successful businessmen are more likely to perceive more obstacles- we resolve this using factor analysis and averaging across regions.
Extending the model developed by Brian Headd (2000) for variables business characteristics for rural entrepreneurship, and combining it with the recent research findings, we draft the following LOGISTIC model:
Due to the binary dependent variable (dummy variable, 1=increase in exmployment, 0= no change and negative change in employment), we will test for logistic and probit model. Several dependent variables that we may use as a proxy for the level of success are: the percentage change in the number of employees and the percentage change in annual sales (start-up sales and current sales). The model is estimated using maximum likelihood estimators. Our dependent variable is taken from the survey. Relying on the model, we are to expect.....signs of the variables. Variable listing is given in Appendix 4b.
6. FINDINGS
Using descriptive statistics, we draft rural entrepreneurship definition and desribe rural entrepreneur (see Appendix 8).
From the 300 selected rural businesses, we have reached 210 of them , with the 70 percent respondents rate. Out of 210 enterprises, 39.52 percent are rurally located, 36.19 percent are semi-urban and 25 percent is urban, which presents limiting factor in our analysis. Nineteen percent of them are located in the North Bosnia, 24 percent in the South and fifty percent in the central Bosnia (see map on p 53), 73.3 percent are involved in production, 27 percent in the service sector. Select by the type of activity, there are 20 percent of businesses in the fruit sector, 21.9 percent in the vegetable sector, 2.3 percent in meat and dairy sector, 6.1 percent in honey sector, 6.1 percent in retail, 4.2 percent in the fisheries sector, 4.2 percent in herbal sector, 7.6 percent in pastry sector and 9.5 percent in rural tourism (around 10 percent are all other accompanied activities). Entrepreneurs are mostly men (in 86.95 percent of cases), who are 47.8 years old on average, have a secondary education level (in 57.76 percent of cases), with 19 years of total experience and 12 years of experience in the sector of their business.
Rural businesses are mainly established by one owner, using owner's savings and in a few cases, by using a combination of bank credit and owner savings. It employs 9 employees currently, have a 10 percent in growth employment, and a 4.5 percent growth in sales annually, on average, with a large standard deviation. It has written contracts (in 59.52 percent of cases) and sells to 2 different groups of customers.
Businesses are mostly established (82,43 per cent cases) from the owner's interest and only a few are inherited (11.2 percent) from the family, and are using the owner's asset (in 87.14 percent of cases). Successful rural businesses have written contracts (60 percent) with one or two crucial customers (68 percent).
The rate of the rural business progress can be seen in a positive change in the number of employees. Rural businesses in BiH on average employ one worker for every two years of a business existence. Ninety two percent of businesses are growing but the rate of its progress is very slow, particularly including average age of the business.
Rural businesses are 7 km away from the closest bank or microcredit affiliates and 5 km away from the road. Supply of water, electricity, internet and access to the road are supplied in the 97 percent of cases on average, with no impediments.
We have measured the effect of obstacles: administrative, infrastructure, market and finance, on the success of rural business, measured by the growth rate of employment (as a dummy variable), controlling for the entrepreneurs and business variables. Before the model has been run, we tested several relations among variables. When it comes to the owner's gender, beside the fact that owners are males, in majority, they are more successful, but in the harsh time, men are more easy to lay off workers, while females are not.
Rural businesses whose owners have not migrated, experienced positive but small increase in the number of employees. Rural businesses mostly have signed contracts and we have a situation where a group of business who signed no contract, in 48.57 percent of cases had no success, and businesses that signed a contract, by 22.11 percent faced the same situation. What makes those two groups distinct, is an uneven distribution of success. Micro businesses are burdened with the costs of transportation (51.41 percent).
Sixty eight percent of rural businesses who said their business faces complicated administrative procedures are micro businesses (employing 1 up to 10 employees), who are successful, employing 2 to 5 additional workers. Out of those hundert, 43 percent are those faced with this obstacle the most and have zero employment growth, meaning zero success. Real interest rate as an obstacle, has an impact on micro businesses („the slow growers") in 62.4 percent, affecting businesses that employ 1 to 5 employees the most.
What is very interesting is the nature of relations among owner's total experience, intention to expand the business and a written business plan. Fifty five percent of owners do not have a written business plan. Of those who do have, 15th and 20th year of the business is crucial in planning. Owners' express their intention and motivation to expand the business, but plan their activities every 10 years on average. Education of the owner does not particularly affect his/her motivation to write a business plan.
Owner of the successful business in 82.24 percent of cases had the intention to expand the business, and 72.2 percent of them had a written business plan. Only those established by the pure interest of the owner (77.14 percent) using owners' savings as a starting capital (63.7 percent) is the most successful (77.14 percent).
Following the analysis, we have developed confirmatory factor analysis using orthogonal varimax rotation, in order to avoid factor correlations and multicolinearity. In this purpose, five factors were created, Fadmin (Factor administration, Factor infrastructure, Factor market, Factor finance, Factor success, paying attention to have eigenvalues close to or higher than one and factor loadings over 0.70 (for more details on factor analysis see appendix 9).
Variables (Complicated procedures to obtain state transfer as support) and (Absence of state assistance) describe By 92.03 percent each, which is very high loading. Factor Is described by (Absence of water supply) and (Absence of phone connection, internet) by 87.42 percent (each variable). The same logic applies to other two factors.
We have tested for several logistic models and relevant STATA output results are given as follows:
Table 3: Results of various models
In the model 1, we wanted to control for the effect of obstacles: competition, credit, on the success, holding variable education, production and established business constant. All variables are statistically significant.
In the model LOGIT (1), for each additional unit increase in competition of small ruralenterprisess, we expecta 1.073261 increasee in log-odds ratio of employment growth, i.e. Success of rural enterprises. For each unit increase in difficulties to fulfill credit procedures, we expect a 1.091434 increase in log-odds ration of employment growth. Being a productive enterprise reduces log odds of business success by 1.147163 and being established reduces log-odds ratio of success by 1.31886. The chi2 of 50.60 is greater than 11.07 (critical value) and shows that this model is better than model with no variables. Sample size of 159>100 is statistically significant and good representative of the population. Variations in the independent variables explain 24 percent of all variations in the employment growth. The model correctly represents 76.10%>50% and fits the data (chi2=38. 1>32.67). In the model with factor analysis, model (4), we wanted to see how the dependent variable behaves in combination with independent variables, grouped in factors. In addition we wanted to see if there is any difference between businesses who are in production and located in rural areas. And the model showed this to be statistically significant variable with high log-odds ratio. In this model, there is significant impact of two factors to the success of rural businesses and those are: factor market and factor finance. Factor market consists of variables small and large competition, and factor finance of variable high interest rate and complicated procedures to obtain credit. For every unit increase in market obstacle or finance obstacle, we expect a 1.548812 increase in log-odds ratio, or 1.704107 respectively.
The probit model best describes the data, if we look for goodness of fit andadjustedt R square. The probability that the business will make success depends on small competition, credit procedures, education of the owner, purpose of its establishment and type of the business product or service). Since production sector grows relatively slowly compared to production, variable djl_dummy has negative sign. Rural areas are more prone to production than to service. Small competition is good as it rises the market battle among businesses to survive. Variables in the probit model tell that::
If a competition rises by 1 unit, rural enterprise success will rise by 0.67%
If credit procedures complication rises by 1 unit, rural business success will rise by 0.64%
If owner's education rises by 1 unit, the rural business success rises by 0.60%
For every business in addition in the production sector, rural business success decreases by 0.66%
Businesses established from owner's savings reduce rural business success by 0.74%
8. CONCLUSION
Summing up all the information reached through the models and literature, we see that several factors define success of rural enterprises in Bosnia and Herzegovina:
Service or production
Finance
Education
Competition
The model showed almost each factor to have a similar level of impact on the rural success, which means we need to work on those factors simultaneously, without prioritizing one over another.
We see that competition plays positive role in the rural business success, meaning we need to work on creating positive and conducive rural entrepreneurship environment. It is a process and involves more actor in the play.
The data show rural BiH tend to have more rural businesses in the sector of production, which is slow growing. The previous literature shows that it is due to inefficient and obsolete technology and knowledge as well.
Although we expected to have an administration and infrastructure as a major obstacle, those variables are not statistically significant nor will be shown in any of the models. What rural entrepreneurs really need and what rural businesses need? Giving the limitations we faced, above all the time limit which influenced possible further analysis of several successful businesses through indepth interviews, we draw further conclusions.
Rural business owners face knowledge constraint. Here we talk about specific, industry knowledge, process knowledge and business knowledge. The sporadic plans they make do not do much when it comes to the success and development. Knowledge here also plays a role. Knowing rural areas are faced with depopulation, it means knowledge is very important and at the critical level.
Beside this, businesses have a need for a source of finance, on a regular basis, especially when it comes to buying and implementing new machines, adapting old ones, getting to know-how. Usually, rural businesses in BihH are established using owner's saving and it is limited source of financing. These businesses usually are micro businesses, which does not exclude the possibility of other companies to grow.
They are disintegrated and standing alone, have minimal benefits acting on their own. This takes valuable energy and time out of them.
What could be done? We think of combination of rural knowledge and industry cluster (Munch et al., 2002), which appear to be also a way of organizing rural areas in Europe (example North Italy). The rural knowledge cluster has many benefits and above all, includes many organizations which are not all business types, such as a university that is acting as a source of knowledge and social capital, NGOs who are conducting research and actively in promoting the cluster.
Beside, gathering small firms under the umbrella of a cluster, stands as a stable structure in attracting investment and various sources of financing. Regular training institutions can play a role in the educational part, but usually they serve as a short time purpose or trainer's background is of no use to trainees. Knowledge clusters instead, gathers firms that are either linked by the same or similar technology or are part of the value chain.
This cluster than can do the marketing of the product, positioning, promotion, finding new buyers, all the functions small producers do not have time to do or do not know how to. This does not leave out knowledge sharing to and among producers. In this framework it is more easy to approach the market for finance and investments. In a global economy, venture capital, business angels or specialized agriculture or nonagriculture funds are not an unknown phenomenon in international finance but are new to Bosnia and Herzegovina. What we want to say, is that building rural entrepreneurship takes planning, vision and time, and it can be done.
Valentine (2003), places importance of social capital inclusion and a need for policy interventions in its successful integration in entrepreneruship rural development in transitional countries, that need to be supported by integration of authorities, communities and associations (Smallbone, 2006).
Picture 9. Clustering
C:\Users\Selma\Desktop\clustering\Slide1.JPG
This means we need to work on creating a conducive rural environment, which involves government institutions relevant to this field.
Creating conducive environment and spurring entrepreneurship in rural areas is thus more complex and requires strategic thinking and planning, where local rural policy and targets contribute as a foundation to national development strategy, where „strategic development alliances", named by Petrin (1994), comprised by a circle of university, nonprofit organization and rural entrepreneurship, contribute significantly.
Picture 1. Creating Conducive Enviroment
Institutional development in transitional economies is a missing substance in drafting policies, that create a conducive environment and fertile ground for diversified rural entrepereneurship. Rural strategies are less bound to locality and more for rural competitiveness.
Thus, policy creators need to take into account the needs of rural entrepreneurs and impediments to their success in policy creation, activating all resources needed for the success (Mecceri and Pelloni, 2006), creating networks of investors, professional and educational associations, building up non-traditional sources of financing and supporting government agencies.
APPENDICES
Appendix 1: Categorizing Rural Business, Source: Gary Bosworth, Categorizing rural businesses-Tales from the paper manpaper man 9th Rural Entrepreneurship Conference, p 9, 2011
TYPE A
TYPE B
TYPE C
TYPE D
Rural market, rural location
Rural market, rural product
Rural product, rural location
Rural product, rural location, rural market
Post Office
Farm suppliers
Farms
Farm produce shop
Village Shops
Farm consultants
Food processing
Thatchers
Village Pub
Vet
B&B/hotels
Fence-making
Paper delivery
Milkman
Nature reserves, visitor centers
Gamekeeper
Village garage
Hiking supplies
Shearers
Village school
Livery stables
Drystone-Wallers
Foresters
Appendix 1A: BiH Agricultural Land and Land Use, source: Agency for Statistics BiH, FBiH, RS and BD
Appendix 1B /source: FARMA Milk Production in Bih 2010/
Appendix 1C: Agricultural production, Intermediate Consumption and Gross Value Added to Agriculture in BiH (million KM),2005-2010, Estimation, source: Source: Unofficial data on the basis of experimental calculation of Economic accounts for agriculture for Bosnia and Herzegovina prepared by the Agency for Statistics of Bosnia and Herzegovina
Appendix 1D: Fruit sorts in BiH, source: Source: gathered information from UNDP sector analysis, 2011, available at: <http://www.undp.ba/index.aspx?PID=36&RID=121>
No of trees/ hectares
Total
Yield
Average
Yield per
Tree
No of
Sorts
Seeding places
PLUM
11.604.612
155.767
13.4kg
16
Majevica, M.Podrinje, Gradacac, Brcko, Potkozarje, Gracanica, central Bosnia
APPLE
4.610.256
71.507
15.5kg
13 imports
9 indigenous
Through BiH
PEAR
1.928.605
24.784
12.9kg
9
Through BIH
CHERY
744.812
10.690
14.4kg
15
Herzegovina, central Bosnia
SOUR CHERRY
454.897
3.803
8.4kg
2
Herzegovina, central Bosna
PEACH
581.104
8.872
15.3
7
Central Bosnia, Herzegovina
STRAWBERRY
4.193 ha
19.107
4.6t open house
40t in-house
10
Widespread
RASPBERRIES
1.031 ha
8.487t
95% sold as export
GRAPES AND GRAPE VINES
3.500 ha
34
Herzegovina
Appendix 1E: Vegetables sorts in BiH, Source: gathered information from UNDP sector analysis, 2011
Available at http://www.undp.ba/index.aspx?PID=36&RID=121
VEGETABLE
LAND
AVERAGE YIELD
PLACE OF PRODUCTION
POTATOES
37.119 ha
11.3t/kg
Posavina, central Bosna, Fojnica, GlamoÄ, Bugojno, Tracvnk, Romanija, Semberija, Lijevce Polje
PEPPER
3.600 ha
TOMATO
3.700 ha
CUCUMER
2.800 ha
South of BIH
LETTUCE
35.000 kg/ha
Posavina
BEANS
9.465 ha
1.4 t/ha
CABBAGE & KALE
5.864 ha
13.87 t/ha
Whole BiH
ONIONS
5.400 ha
6.3 t/ha
Herzegovina, Travnik, Posavina
Appendix 1F: Farms and Cows in BiH, Source: Size Structuer of Livestock Farms in Federation of BiH in 2007, Federal Ministry of Agriculture, water management and forestry, taken from the Bajramovic et al 2009, page 7
Appendix 1G: Tourism Product and Service, source: Žunić, The Impact of Agritourism on Sustainable Development of Bosnia and Herzegovina, 2011, p4.
RURAL TOURISM ATTRACTIONS
EVENTS
SERVICES
Souvenir and craft products
Festivals
Harvesting
Agro-museums
Fairs
Product tasting
Crop plantations
Religious events
Tourist tours
Eco-house
Production
Eco-village
Rrual accommodation
Appendix H: Agri-food trade (in EUR million), 2000-2008 BiH, Source: Volk (2008), Agriculture in Western Balkan Countries, IAMO
Appendix 1I: Roads and railway lines, 2009, Source: FBiH's and RS' Statistical Yearbooks of 2010
Modern roads (km)
Local roads (km)
Railway lines (km)
State level
11,951
n/a
n/a
FBIH
7,267
5825
601
RS
4,652
651
416
BD
(32)
n/a
n/a
Appendix 1J: Availability of internet connection in BiH, in (%),Source: Pilot Agricultural Census 2010, Rural Development
Brcko District
FBiH
Republica Srpska
Internet connection
12
29
20
Appendix 1K: Employment per sector in 2011, source: BHAS
Sector
All
Women
Women (%)
Total
693,964
283,538
40.86
Agriculture, hunting and forestry
17,315
3,707
21.41
Fishing
410
92
22.44
Manufacturing
135,008
46,975
34.79
Appendix 1L: Employees according to groups of area of activity; In 000; Period 2007-2010, Source: Ministry of Foreign Trade and Economy Relation of Bosnia and Herzegovina (2011)
Area of activity
2007
2008
2009
2010
Agriculture
168
183
182
166
Industry
277
289
270
261
Service
404
418
406
416
Total
850
890
859
843
Structure in % (Total employees = 100)
Agriculture
19.8
20.6
21.2
19.7
Industry
32.6
32.5
31.5
31.0
Service
47.6
47.0
47.3
49.3
Total
100.0
100.0
100.0
100.0
Appendix M: Selected poverty indicators for BiH, Source: HBS, 2007
POVERTY
National
Rural
Urban
Poverty Gap index at 1.25 $/day
Poverty Gap analysis National
Gini coefficient
14.4
17.78
8.23
<0.5
4.9
33.3
Appendix N: Selected poverty indicators per entity, Source: HBS, 2007
Appendix 1P: Structure of banks in BiH, in (%), Source: Agency for Banking FBIH, Agency for Banking RS
Federation
Republika Srpska
2011
2011
Number of banks
19
10
Number of branches
Number of microcredit organizations
16
8
Private banks
18
State bank
1
Sectoral and microcredit structure of Microcredit organizations, 31.12.2012, Source: Agency for banking FBiH
Short term credits
Long term credits
Legal entity
Services
1.286
3.751
Commerce
1.293
2.534
Agriculture
37
113
Production
544
825
Real estate
518
420
Apendix 2: Milk Purchase 2006-200, source: ITA BiH
Appendix 3
Appendix 4A
Clustering
Cluster 1 - BiH1: Banovici, Celic, Doboj Istok, Gracanica, Gradacac, Kalesija, Kladanj, Odzak, Orasje, Samac, Srebrenik, Sapna, Tuzla, Velika Kladusa.
Cluster 2 - BiH2: Bihac, Bugojno, Buzim, Bosanska Krupa, Banja Luka, Breza, Cazin, Capljina, Donji Vakuf, Fojnica, Gorazde, Hadzici, Ilijas, Jajce, Kiseljak, Konjic, Kresevo, Maglaj, Olovo, Sarajevo, Sanski Most, Travnik, Tesanj, Vogosca, Visoko, Vitez, Velika Kladusa, Zavidovici, Zepce, Zenica,
Cluster 3 - BiH3: Bosansko Grahovo, Capljina, Citluk, Grude, Glamoc, Jablanica, Kupres, Livno, Ljubuski, Mostar, Posusje, Prozor-Rama, Ravno, Stolac, Siroki Brijeg, Tomislavgrad.
Appendix 4b
Geographical Sampling, 2012
BIH REGIJE 001.jpg
Appendix 4C
Sampling by the type of rural business, 2012
Appendix 5
Appendix 7. RES2012
RURAL ENTREPRENEURSHIP QUESTIONNAIRE (RES 2012)
Thank you for participating in the survey. Questionnaire is anonymous and
data collected upon are used for the purpose of master thesis drafting solely.
If responded unknown (not taken from the database):
1. Location of your business: a.Village b. City c. Suburb
d. Other, please specify_______________
2. Municipality your business is located in: ___________________________
3. Type of the business: a. Fruits b. Vegetables c. Meat and dairy production d. Fisher
e. Honey f. Aromatic Herbs g. Vine h. Tobacco i. Trade/local
shop j. Rural tourism/hospitality j. Other, please specify_______
k. pastry, bread, pasta
Characteristics of entrepreneur (the one who makes strategic decisions in the business):
A1. Sex
A2. Age
A3. Place of birth
A4. Place of current residence
A5. Highest education level completed
A6. Work experience
(years)
A7. Migration experience
(time spent abroad in years)
1. Male
2. Female
1. Rural
2. Urban
1. Rural
2. Urban
a. in BiH_______
b. Out of BiH:___________
c. Type of school/college:_________
Elementary
Secondary
University
Masters
Ph.D
Total: ______
In this sector:________
Out of BiH in this sector: ____________
a.Time spent out of BiH:____________
b.Year of departure first time:__________
c.Year of return to BiH:______________
Characteristics of the business
B1. Year when started
B2. Number of owners
B3. Number of employees
B4. Your employees are (in numbers)
B5. Business is:
B7. Average annual production (in KM) yields:
B12B. Do You Advertise
a) Currently: _____
b) At the firm establishment: _____________
Full-time: ____
Part-time: ___
Seasonal: ____
Established
Inherited from parents
Bought
Other, please specify:______________
a) Today: ____________
b) when started: _______
a. radio
b. tv
c. internet
d. fair
B8. Assets you use for your business (land, buildings, etc.) are
B9. Do you plan to expand the production
B10. Do you have written business plan
B11. What was the main source of income when you started your business
B12. Do you receive money from abroad
B12A. Have your property been destroyed in the war
a) your ownership
b) rented
c) other: __________
Yes
No
Yes
No
My savings
Family savigns
Friends savigns
Bank credit
Savings from abroad
Yes
No
Yes
No
B13. Distance to closest bank/microcredit branch
B14.Distance to a regional road or highway (in km)
B15. Do you have access to:
B15b.Do you have multiannual contract to buyers:
B16. Do you export
B17. Main buyers of your products:
B18. Distance to your largest customer/market
B19. Are you member of coop/association
a. Roads: YES NO
b. Internet: YES NO
c. Water: YES NO
d. Electricity: YES NO
a.Yes
b.No
a.Yes
b.No
a. Restaurants
b. Processing industry
c. Retailers
d. Public services
e. Individuals
f. own consumption
g. International buyer
h. Government
g. Other, please specify: ________________________
Yes
No
C. In running your business, you face following obstacles:
Obstacle
Never
Rarely
Frequently
I Administrative
1. Complicated procedures for obtaining subsidies
2. Lack of support by the government
3. High taxes and contributions
4. Lack of local community support
5. Difficulties in obtaining standards, certificates
6. Other, please specify:
II Infrastructure
7. High transportation costs
8. No access to water
9. No access to phone, internet, etc.
10. Other, please specify:
III Skills
11. Lack of trained labour force
12. Other, please specify:
IV Access to market
13. Difficulties in selling the products
14. Low price of products offered by resellers
15. Too volatile exchange rates
16. Large number of small competitors
16a. Large number of large competitors/trade centres
17. Expensive/large prices of / raw materials
18. Distant larger groceries or discount centre
19. Other, please specify:
V Access to finance
20. High interest rates
21. Difficulties in obtaining a loan
22. Other, please specify:
The following factors were, for the success of your business:
Factor
Not important at all
Important
Extremely important
Education and training of the owner
Loans received
Business connections and friendships
Personal contacts with owners of companies to which we sell our products
Availability of raw materials
Support by the government
Support by family members living abroad
Other, please specify:
Appendix 8
STATA Output
summarize
Variable
Obs
Mean
Std. Dev.
Min
Max
location_r
210
.3952381
.49007
0
1
location_s~b
210
.3619048
.4816997
0
1
north
209
.1913876
.3943379
0
1
south
209
.2440191
.4305354
0
1
djl
210
462.381
339.665
1
11
fruits
210
.2
.4009558
0
1
vegetables
210
.2190476
.4145893
0
1
wine
210
.1238095
.3301511
0
1
meat_dairy
210
.0238095
.1528196
0
1
honey
210
.0619048
.2415583
0
1
retail
210
.0619048
.2415583
0
1
fish
210
.0428571
.2030189
0
1
herbs
210
.0428571
.2030189
0
1
tourism
210
.0952381
.2942449
0
1
pastry
210
.0761905
.2659366
0
1
djl_dummy
210
.7333333
.4432733
0
1
o_gender
207
.821256
.3840666
0
1
a2
201
1.964.199
9.349.877
1939
1987
o_age
201
47.801
9.349.877
25
73
a3
203
1.630.542
.6722603
1
3
birth_rural
203
.4778325
.5007432
0
1
birth_urban
203
.4137931
.4937299
0
1
a4
210
1.766.667
.81718
0
3
res_r
210
.3333333
.4725309
0
1
res_u
210
.4238095
.4953417
0
1
a51
210
.8
.4009558
0
1
a52
210
.0333333
.1799344
0
1
a53
210
.3285714
1.309.386
0
9
a54
206
234.466
.6109933
1
5
primary
206
.0485437
.2154356
0
1
secondary
206
.5776699
.4951338
0
1
terciary
206
.3737864
.4849865
0
1
exp_tot
182
1.906.593
9.274.574
1
45
exp_s
165
1.222.727
7.471.787
1
40
a63
210
.0952381
.8642148
0
10
a71
210
.5714286
1.561.236
0
10
duration_m~n
210
.5714286
1.561.236
0
10
a72
27
1.994.111
6.482.719
1981
2006
a73
28
1.997.571
6.100.568
1984
2010
b1
200
1999.35
1.209.624
1912
2012
b_age
200
dec.65
1.209.624
0
100
owners
201
1.358.209
1.476.836
1
15
b31
204
9.485.294
1.662.258
1
200
b32
172
4.261.628
8.027.448
1
60
b31b32age
170
.5366471
1.521.573
-2.33
13.64
change_no_~l
174
5.218.391
1.440.771
-32
150
ch_empl_age
192
103.599
1.794.389
.04
18.18
b41
192
7.760.417
1.657.017
0
200
b42
193
.3056995
1.445.113
0
15
b43
193
6.041.451
2.380.013
0
250
inherited
205
.1121951
.316379
0
1
established
205
.8243902
.3814192
0
1
sales1
135
422185.2
944602.9
2000
6000000
sales2
88
274142
1142324
1000
1.00e+07
asset
210
.8714286
.3355248
0
1
b9
210
.7857143
.4113064
0
1
b10
206
.4126214
.4935051
0
1
savings
198
.7525253
.4326388
0
1
no_of_sour~s
198
1.444.444
.5467434
1
3
b12a
65
.7692308
.4246039
0
1
b12
205
.2
.4009792
0
1
b12b
111
.9099099
.2876093
0
1
advert
111
.9099099
.2876093
0
1
bank
206
6.849.272
9.432.189
0
100
road
207
apr.82
3.485.288
0
500
b151
198
.9242424
.2652806
0
1
acc_road
203
.9950739
.0701862
0
1
acc_net
202
.9158416
.278315
0
1
acc_wat
203
.9901478
.0990123
0
1
acc_el
203
1
0
1
1
contract
210
.5952381
.4920188
0
1
export
210
.1952381
.397331
0
1
no_customer
208
20.625
1.121.135
1
8
market
204
4.890.882
3.510.451
0
500
cooperatives
196
.2397959
.4280522
0
1
ci_1
209
245.933
.7528928
1
3
cl_1_ima
209
.8421053
.3655178
0
1
ci_1_nema
209
.1578947
.3655178
0
1
ci_2
209
2.516.746
.7145177
1
3
ci_2_ima
209
.8708134
.3362117
0
1
ci_2_nema
209
.1291866
.3362117
0
1
ci_3
208
2.235.577
.8089378
1
3
ci_3_ima
208
.7644231
.4253826
0
1
ci_3_nema
208
.2355769
.4253826
0
1
ci_4
207
2.130.435
.742203
1
3
ci_4_ima
207
.7826087
.4134709
0
1
ci_4_nema
207
.2173913
.4134709
0
1
ci_5
182
1.752.747
.7575536
1
3
ci_5_ima
182
.5604396
.4977028
0
1
ci_5_nema
182
.4395604
.4977028
0
1
droped
210
.0095238
.0973563
0
1
cii_7
207
2.140.097
.7338461
1
3
cii_7_ima
207
.7922705
.4066655
0
1
cii_7_nema
207
.2077295
.4066655
0
1
cii_8
204
1.215.686
.4786568
1
3
cii_8_ima
204
.1862745
.3902858
0
1
cii_8_nema
204
.8137255
.3902858
0
1
cii_9
198
1.075.758
.3011285
1
3
cii_9_ima
198
.0656566
.2483086
0
1
cii_9_nema
198
.9343434
.2483086
0
1
v101
207
0
0
0
0
ciii_11
207
1.589.372
.6900704
1
3
ciii_11_ima
207
.468599
.5002227
0
1
ciii_11_nema
207
.52657
.5005039
0
1
v105
210
.0047619
.0690066
0
1
civ_13
205
1.926.829
.6336265
1
3
civ_13_ima
205
.7609756
.4275317
0
1
civ_13_nema
205
.2390244
.4275317
0
1
civ_14
200
2.055
.7515311
1
3
civ_14_ima
200
.745
.436955
0
1
civ_14_nema
200
.255
.436955
0
1
civ_15
195
1.476.923
.7130816
1
3
civ_15_ima
195
.3487179
.4777912
0
1
civ_15_nema
195
.6512821
.4777912
0
1
civ_16a
204
1.867.647
.8046248
1
3
civ_16a_ima
204
.6029412
.490492
0
1
civ_16a_nema
204
.3970588
.490492
0
1
civ_16b
202
1.732.673
.7966483
1
3
civ_16b_ima
202
.5148515
.5010211
0
1
civ_16b_nema
202
.4851485
.5010211
0
1
civ_17
200
2.nov
.707391
1
3
civ_17_ima
200
.8
.4010038
0
1
civ_17_nema
200
.2
.4010038
0
1
civ_18
168
1.464.286
.655633
1
3
civ_18_ima
168
.375
.4855702
0
1
civ_18_nema
168
.625
.4855702
0
1
v127
210
0
0
0
0
cv_20
207
2.391.304
.8340577
1
3
cv_20_ima
207
.7729469
.4199428
0
1
cv_20_nema
207
.2270531
.4199428
0
1
cv_21
208
1.975.962
.8537494
1
3
cv_21_ima
208
.625
.4852909
0
1
cv_21_nema
208
.375
.4852909
0
1
v134
210
.0047619
.0690066
0
1
d1
205
1.avg
.7302967
1
3
d1_ima
205
.6146341
.487873
0
1
d1_nema
205
.3853659
.487873
0
1
d2
206
2.106.796
.7178915
1
3
d2_ima
206
.7912621
.4073967
0
1
d2_nema
206
.2087379
.4073967
0
1
d3
205
2.458.537
.6297275
1
3
d3_ima
205
.9268293
.261054
0
1
d3_nema
205
.0731707
.261054
0
1
d4
206
2.320.388
.6657267
1
3
d4_ima
206
.8883495
.3157032
0
1
d4_nema
206
.1116505
.3157032
0
1
d5
204
2.465.686
.6228799
1
3
d5_ima
204
.9313725
.2534415
0
1
d5_nema
204
.0686275
.2534415
0
1
d6
206
2.281.553
.7111307
1
3
d6_ima
206
.8495146
.3584176
0
1
d6_nema
206
.1504854
.3584176
0
1
d7
204
1.245.098
.5048782
1
3
d7_ima
204
.2107843
.4088689
0
1
d7_nema
204
.7892157
.4088689
0
1
v156
210
.0047619
.0690066
0
1
empl
172
1.953.488
4.177.094
-1
45
sales
88
7.352.273
1.973.796
-1
134
empl_growt~d
170
.6176471
.4873977
0
1
empl_decre~d
172
.3895349
.4890686
0
1
sales_dummy
89
.7977528
.4039514
0
1
. tabulate secondary b10
| B10
Secondary | 0 1 | Total
-----------+----------------------+----------
0 | 50 35 | 85
1 | 68 49 | 117
-----------+----------------------+----------
Total | 118 84 | 202
. tabulate terciary b10
| B10
Terciary | 0 1 | Total
-----------+----------------------+----------
0 | 77 50 | 127
1 | 41 34 | 75
-----------+----------------------+----------
Total | 118 84 | 202
tabulate o_gender change_no_empl
tabulate
empl_growth_d b9
Empl_growt
B9
h_D
0 1
Total
0
19 44
63
1
19 88
107
Total
38 132
170
. tabulate
empl_growth_d b10
Empl_growt
B10
h_D
0 1
Total
0
42 19
61
1
56 51
107
Total
98 70
168
. tabulate
empl_growth_d established
Empl_growt
Established
h_D
0 1
Total
0
4 59
63
1
24 81
105
Total
28 140
168
. tabulate
empl_growth_d savings
Empl_growt
Savings
h_D
0 1
Total
0
16 45
61
1
24 79
103
Total
40 124
164
tabulate empl_growth_d no_customer
Empl_growt
No_Customer
h_D 1 2
3 4
5
6
8
Total
0 17 26
17 3
0
0
0
63
1 47 30
15 7
4
1
1
105
Total 64 56
32 10
4
1
1
168
. tabulate no_customer contract
No_Custome Contract
r 0 1
Total
1 32 45
77
2 31 40
71
3 14 26
40
4 2 12
14
5 2 2
4
6 1 0
1
8 1 0
1
Total 83 125
208
Appendix 9
Stata Factor Analysis, PCF
. factor cl_1_ima ci_2_ima, pcf
(obs=209)
Factor analysis/correlation Number of obs = 209
Method: principal-component factors Retained factors = 1
Rotation: (unrotated) Number of params = 1
--------------------------------------------------------------------------
Factor Eigenvalue Difference Proportion Cumulative
-------------+------------------------------------------------------------
Factor1 1.69389 1.38778 0.8469 0.8469
Factor2 0.30611 . 0.1531 1.0000
--------------------------------------------------------------------------
LR test: independent vs. saturated: chi2(1) = 136.28 Prob>chi2 = 0.0000
Factor loadings (pattern matrix) and unique variances
---------------------------------------
Variable Factor1 Uniqueness
-------------+----------+--------------
cl_1_ima 0.9203 0.1531
ci_2_ima 0.9203 0.1531
---------------------------------------
. rotate
Factor analysis/correlation Number of obs = 209
Method: principal-component factors Retained factors = 1
Rotation: orthogonal varimax (Kaiser off) Number of params = 1
--------------------------------------------------------------------------
Factor Variance Difference Proportion Cumulative
-------------+------------------------------------------------------------
Factor1 1.69389 . 0.8469 0.8469
--------------------------------------------------------------------------
LR test: independent vs. saturated: chi2(1) = 136.28 Prob>chi2 = 0.0000
Rotated factor
