215 10 3494 https://omeka.ibu.edu.ba/files/original/9edeeaf430df68abae993679141b43df.pdf 15bb98e5a755a17e23f5656aff2fa2e1 PDF Text Text 3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Erkoyuncu, I., (1995). Fisheries biology and population dynamics. Ondokuz Mayıs University, Faculty of Fisheries, Sinop, Turkey, pp. 265 (in Turkish). Froese, R. (2006). Cubelaw, condition factor and weight-length relationships: history, metaanalysis and recommendations. J.Appl.Ichthyol. 22, 241-253. Gonçalves, J.M.S., Bentes, L., Lino, P.G., Ribeiro, J., & Canaroo, A.V.M., (1997). Weightlength relationships for selected fish species of the small-scale demersal fisheries of the south and south and southwest coast of Portugal. Fish. Res., 30(3), 253-256. Koutrakis, E.T., & Tsikliras, A.C., (2003). Length-weight relationships of fishes from three northern Aegean estuarine systems (Greece). J. Appl. Ichthyol. 19, 258-260. Lalèyè, P.A., (2006). Length-weight and length-length relationships of fshes from the Ouémé River in Bénin(West Africa). J. Appl. Ichthyol. 22, 330-333. Moutopoulos, D.K., & Stergiou, K.I., (2002). Length-weight and length-length relationships of fish species of the Aegean Sea (Greece). J. Appl. Ichthyol. 18(3), 200-203. Pauly, D., (1993). Fishbyte section editorial. Naga, the ICLARM Quarterly, 16, pp. 26. Petrakis, G., & Stergiou, K.I., (1995). Weight-length relationships for 33 fish species in Greek waters. Fish. Res. 21, 465-469. Wootton, R.J., (1990) Ecology of teleost fishes. Chapman and Hall, London. Could government legalize illegal settlement by improving their energy efficiency? Janjusevic Jelena, Begovic Radojevic Milica, UNDP, Podgorica; Montenegro Abstract In recent months we are faced with serious budget problems in Montenegro, the solution of which, among other things is seen in reducing the number of employees in state administration. On the other hand, the costs of living are significantly above the disposable budget of households. Particular problem is the high cost of electricity, which recently 132 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo resulted in the street protests of discontented citizens. On one hand we have a government that alerts the lack of electricity, and on the other hand we have citizens that may hardly cover these costs. In addition, Montenegro is dealing with a double-challenge of inefficient space use (country features over 100,000 illegal homes, if distributed evenly implying that every other family lives in an illegal home) and inefficient energy use (Montenegro needs on average 8.5 times more energy per unit produced than an average EU country). 1.How to solve a problem and please both sides? Is that feasible at all? UNDP office in Montenegro came up with the idea to link solving the big problems in Montenegro, such as the problem of illegal construction, with increasing the level of energy efficiency in households, businesses and other facilities. Namely, UNDP proposes an integrated policy solution to the double-challenge in providing energy efficiency measures to incentivize households to legalize their homes. The idea and research that was recently conducted show how the legalization of illegal buildings by the introduction of mandatory energy efficiency measures in them, may at the same time result in the increase of revenue to the central and local budgets, reduction of negative impact on the environment, increase of employment, engagement of the economy, reduction of electricity consumption and thereby to reduce the need to import electricity, and ultimately to contribute to the welfare of the population. Our research (energy audits) conducted on 30 illegal houses in three pilot municipalities showed that significant savings in energy consumption could be realized (up to 60%). Based on these results, we propose an approach to formalizing informal settlements in Montenegro through implementing an energy efficiency incentive system for the households. The scheme is broken down into 2 steps: (1) a household receives a loan to improve energy efficiency. On average for a 100m2 household, €3,800 loan (with 4.5% interest rate) results in 59% of energy savings or €630 per year at the current energy prices; (2) a household enters into a contractual agreement with the Government/municipality to use the savings from energy efficiency to pay off the low-interest loan it received for the retrofit and the formalization cost. The benefit for the household is dual- a title to the house and improved energy efficiency/resulting financial savings. The benefit for the municipality/Government is the steady supply of funding for the property tax. The benefit for the private sector is the increase in demand for retrofits and upgrading of the infrastructure that services informal settlements. Keywords: energy efficiency, sustainable development, illegal construction, energy audits, retrofitting 133 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo 2.INTRODUCTION The world is experiencing three inter-related crises at the moment. One regards the rising trend of resource prices. The resource price index in the 20th century fell by 50% even though the population quadrupled, economic output rose 4 times, and demand for fossil fuels and water increased by 16 and 9 percent respectively. The first decade of 21st century reversed this trend, and relative to the beginning of 20th century in 2010 the index rose by 147%1. This is a result of a combination of factors: rising demand and population, decreasing sources of supply, volatility of supply (most fossil fuel deposits are located in conflict prone locations such as Iran, Saudi Arabia, Venezuela). If we continue on this path, by 2050 we would need three times more resources and this is simply no longer an option, which brings us to the second crises. This crisis regards the rising inequality globally within countries. During the last several decades, millions of people around the world have been lifted out of poverty. In Central and Eastern Europe, some 90 million people or 18% of its population moved out of poverty since 1999. Despite this, 30% of the region’s population is still considered poor or vulnerable, with the number rising by 5 million for each 1% of decline in GDP2. The recent ILO report echoes this in noting that the ‘society is becoming increasingly anxious about the lack of decent jobs. The findings show that Social Unrest Index in 2011 rose in 57 out of 106 countries, as more people were pushed out of labor market, predominantly impacting youth and women. So what does this mean for societies across the world? The recent research shows that more unequal societies feature far more social problems including high rates of suicide, obesity, teenage pregnancy, imprisonment, and low levels of literacy, trust, life expectancy3. In short, the economic growth does not yield human development returns in those high developed countries that features high levels of inequality and that subsequently invest the bulk of their public resources into prisons, policy, and defence and health services to deal with the growing amount of social problems. Finally and linked with the other two crises, the world is at a tipping point in regard to the loss of vital ecosystem services and extreme events- both connected to the changing climate. Some 60% of ecosystem services that underpin our economies and life on earth have been degraded, some beyond the point of return. Recently published research in the Journal of Nature that for the first time compared effects of biodiversity loss to other human-caused environmental changes analyzed 12 peer-reviewed articles and concluded that reduced biodiversity affects ecosystems at levels comparable to those of pollution and global 1 McKinsey’s ‘Resource Revolution’ The report last accessed on May 4th 2012. http://www.mckinsey.com/Features/Resource_revolution 2 This World Bank study quoted in ‘The Economic and Financial Crisis in CEE and CIS: Gender Perspectives and Policy Choices’ last accessed on May 4th 2012 at: http://www.levyinstitute.org/pubs/wp_598a.pdf 3 Richard Wilkinson, Kate Pickett ‘The Spirit Level: Why More Equal Society Almost Always Do Better’ Allen Lane, 2009 134 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo warming4. In layman terms, this means that environment’s ability to provide clean water, food and stable climate is seriously undermining the quality of life and human development globally. In terms of disasters, in November last year IPCC published first scientific proof that the changing climate results in an increase in frequency and intensity of extreme weather events5. Our region experienced some $70 billion disaster-related losses during the last two decades6. The three crises are related, mutually reinforcing one another and creating a vicious cycle that impacts all segments of sustainable human development- economic competitiveness, social inclusion and environment. Any viable solution must match the complexity of the crises, addressing them in an integrated manner that will unleash economic growth and job creation, while at the same time conserving the biodiversity and maintaining the balanced environment. This paper will present one such integrated solution that aims to resolve the multi-dimensional development challenge of informal housing (low economic empowerment, rising pressure on environment, high exposure to extreme events, inefficient resource use, low quality of life). It will demonstrate how UNDP plans to utilize main principles of green economy to provide economic empowerment to the citizens in Montenegro. 3.What is a Green Economy? UNEP defines a green economy as one that results in improved human well-being and social equity, while significantly reducing environmental risks and ecological scarcities. In its simplest expression, a green economy can be thought of as one which is low carbon, resource efficient and socially inclusive. In a green economy, growth in income and employment should be driven by public and private investments that reduce carbon emissions and pollution, enhance energy and resource efficiency, and prevent the loss of biodiversity and ecosystem services. These investments need to be catalyzed and supported by targeted public expenditure, policy reforms and regulation changes.7 The development path should maintain, enhance and, where necessary, rebuild natural capital as a critical economic asset and as a source of public benefits, especially for poor people whose livelihoods and security depend on nature. 4 http://www.clickgreen.org.uk/research/trends/123462-biodiversity-loss-is-as-damaging-as-climatechange-and-pollution.html 5 The IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation, PDF presentation last accessed on May 4th 2012 http://www.ipcc.ch/news_and_events/docs/srex/SREX_slide_deck.pdf 6 From Transition to Transformation: Sustainable and Inclusive Development in Europe and Central Asia, report last accessed on May 4th 2012 at http://www.unece.org/fileadmin/DAM/publications/oes/RIO_20_Web_Interactif.pdf 7 UNEP, Towards a Green Economy, Pathways to Sustainable Development and Poverty Eradication, 2011 135 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo It is very important to understand that the concept of a “green economy” does not replace sustainable development. However, there is a growing recognition that achieving sustainability rests almost entirely on getting the economy right. Perhaps the most widespread myth is that there is an inescapable trade-off between environmental sustainability and economic progress. There is now substantial evidence that the “greening” of economies neither inhibits wealth creation nor employment opportunities, and that there are many green sectors which show significant opportunities for investment and related growth in wealth and jobs. Also, many theorists and practitioners believe that green economy is a luxury only wealthy countries can afford, or worse, a developed-world imposition to restrain development and perpetuate poverty in developing countries. Contrary to this perception, there are numbered examples of greening transitions taking place in various sectors in the developing world, which deserve to be emulated and replicated elsewhere. The last two years have seen the idea of a “green economy” float out of its specialist moorings in environmental economics and into the mainstream of policy discourse. It is found increasingly in the words of heads of state and finance ministers, in the text of G20 communiqués, and discussed in the context of sustainable development and poverty eradication. Over the last quarter of a century, the world economy has quadrupled, benefiting hundreds of millions of people. In contrast, however, 60% of the world’s major ecosystem goods and services that underpin livelihoods have been degraded or used unsustainably. Indeed, this is because the economic growth of recent decades has been accomplished mainly through drawing down natural resources, without allowing stocks to generate, and through allowing widespread ecosystem degradation and loss. Meanwhile, for the first time in history, more than half of the world population lives in urban areas. Cities now account for 75% of energy consumption and 75% of carbon emissions. Rising and related problems of congestion, pollution, and poorly provisioned services affect the productivity and health of all, but fall particularly hard on the urban poor. With approximately 50% of the global population now living in emerging economies that are rapidly urbanizing and will experience rising income and purchasing power over the next years – and a tremendous expansion in urban infrastructure – the need for smart city planning is paramount. 4.Energy efficiency People have always used energy to do work for them. Thousands of years ago, early humans burned wood to provide light, heat their living spaces, and cook their food. Later, people used 136 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo the wind to move their boats from place to place. A hundred years ago, people began using falling water to make electricity. Today, people use more energy than ever from a variety of sources for a multitude of tasks and our lives are undoubtedly better for it. Our homes are comfortable and full of useful and entertaining electrical devices. We communicate instantaneously in many ways. We live longer, healthier lives. We travel the world, or at least see it on television and the internet. In 1973, when Americans faced their first oil price shock, people didn’t know how the country would react. How would Americans adjust to skyrocketing energy prices? How would manufacturers and industries respond? We didn’t know the answers. Now we know that Americans tend to use less energy when energy when energy prices are high. We have the statistics to prove it. When energy prices increased sharply in the early 1970s, energy use dropped, creating a gap between actual energy use and how much the experts had thought Americans would be using. The same thing happened when energy prices shot up again in 1979, 1980, and 2008—people used less energy. When prices started to drop, energy use began to increase. In 2009, the United States used 27 percent more energy than it did in the 1970s. That might sound like a lot, but the population increased by over 43 percent and the nation’s gross domestic product (the total value of all the goods and services produced by a nation in one year) was 2.6 times that of the 1970s. If every person in the United States today consumed energy at the rate we did in the 1970s, we would be using much more energy than we are - perhaps as much as double the amount. Energy efficiency technologies have made a huge impact on overall consumption since the energy crisis of 1973. Mankind is facing one of the greatest challenges in its history: developing in order to “meet the needs of present generations without compromising the ability of future generations to meet their needs”8. Increasing demands for natural resources, weakening of ecosystems, global warming and soaring population growth are just a few of the global issues confronting us. Since the end of the 1960s there have been more and more global initiatives to reduce social and ecological imbalances. The movement is now speeding up: those involved are becoming aware of the role they can play within their sphere of influence and of the interdependence between the various aspects of sustainable development. Improving energy efficiency is mostly connected with buildings, both residential and business, changes and the main challenge now is to design, build and renovate buildings to 8 Our Common Future, Brundtland Report, 1987 137 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo reduce their environmental impact and create areas that are healthy and comfortable for the occupants. Throughout their life cycle, buildings consume natural resources, generate waste and emit large amounts of CO2, contributing significantly to global warming. A large proportion of the world's population, particularly in the developed countries, spends 90% of its time indoors (source: OECD). In this context, questions of hygiene standards inside buildings and the comfort of occupants are also central issues in the debate. At building level, energy efficiency covers all the methods used to reduce the energy used for a given service (heating, lighting, operating machines, etc.). Two types of energy efficiency are generally taken into consideration: Energy efficiency associated with the framework This corresponds to the structural properties of the building that will reduce energy requirements (and in particular heating and lighting). This category includes: optimized insulation, double glazing, treatment of heat bridges, management of openings (doors and windows) and coverings (blinds and shutters). Energy efficiency from high-performance equipment and as a result of the management of this equipment. High-performance equipment is that providing the best efficiency. Equipment management is used to adapt the level and duration of the provision of energy to requirements. It corresponds to the installation of products and systems that will regulate and automate energy consumption in the building in order to avoid unnecessary consumption. Energy efficiency retrofits provide an opportunity to reduce greenhouse gas emissions, generate economic activity, save billions in energy costs, and ensure the long-term viability of affordable housing. However, there is insufficient data on how much energy these upgrades actually save, and therefore little data on what the return on investment would be for lenders. Without this data, it is very difficult to secure upfront capital investments in retrofits, inhibiting this sector’s capacity to scale. 5.Montenegro and legalization problem In the past decade, Montenegro has witnessed rapid urbanization fuelled by foreign direct investment on the Adriatic coast and in mountain resorts. This growth, which has significantly increased the GDP of the country for several years has, in parallel, caused negative effects such as urban sprawl in previously natural landscapes along the coast and around the capital Podgorica, resulting in large numbers of informally built constructions (that is without a construction permit), both commercial and residential, that have very low energy efficiency characteristics, resulting in an overall increase in CO2 emissions due to rising energy demand in buildings. According to one estimate, there are approximately 100,000 such informal constructions, though there are no clear statistics. Approximately 62% of the population of Montenegro lives in urban areas and the quality of their life is under pressure from urban development problems. Uncontrolled urbanization, especially in the central area (around 138 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Podgorica and other cities) and the coastal areas (seaside tourist development), is having negative impacts, such as overcrowded settlements and inaccessibility to infrastructure. Informal constructions in Montenegro generally fall under three broad categories: A building constructed on a parcel of land that legally belongs to the owner. The owner obtained the necessary ‘construction permit’ but did not secure the ‘use permit’ from the municipal authorities, which is required by law to ensure that the housing unit was built according to specifications approved in the ‘construction permit’. Owners are required to pay specified municipal fees to obtain the ‘use permit’. A building constructed on own land by the owner of the land, but without both the ‘construction permit’ and the ‘use permit’. A building constructed on state or municipal land without the express consent of the owner and without the necessary ‘construction or ‘use permit’. Nearly all Montenegrin households (>99%) are connected to the electricity grid and metered. Based on the latest available data, average monthly electricity consumption in Montenegro in 2001 was 367 kWh per household. This makes that average monthly bill for electricity per household amounts cca 100 euro. According to the estimation of Ministry of Economy of Montenegro 80% of the electricity in the household is used for the heating. Most homes are heated through an electric radiator system, an electric thermal accumulator or an individual heating system. Wood is one of the most popular heating sources in individual houses in Montenegro, especially in the North, but almost absent in the South and in apartment buildings. Assuming that the 100,000 informal constructions have the same average energy consumption profile as regular houses (a highly conservative assumption given their generally sub-standard workmanship and hence low EE), the informal housing sector is estimated to account for over one-quarter of Montenegro’s residential energy consumption and 7% of the country’s energybased GHG emissions. The irregular sector is also characterised by relatively high energy poverty: systematic data are scarce but some observations suggest that up to 40% of people living in the irregular housing sector do not have access to sufficient energy services to ensure a healthy lifestyle for themselves and their families. Buildings constructed without building permits in most cases have not been subject to the process of verification of application of standards, neither in the course of design development nor during performance of works, particularly from the aspect of seismic risk. Existence of a large number of informal buildings, primarily residential facilities, highlights the urgent need for organized approach to resolving the problem of regularization of such buildings and verifying achieved level of their static and seismic protection. 139 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo The Government of Montenegro has adopted a National Formalisation Program (NFP) and Action Plan to regularize the vast stock of informal individual housing. The new Regularization Law will mandate all owners of illegal houses to undergo mandatory building registration process; it will impose penalties (up to building demolition) for those property owners who fail to comply with the requirements. The Law and bylaws will also stipulate the administrative procedures and financial costs associated with legalization. 6.UNDP approach to the legalization problem National Formalization Program, will result in new policies, regulation and significant investment to transform illegal housing stock into regularized and law-compliant buildings. However, if implemented as designed, NFP will not bring in energy efficiency improvements in individual houses, which are now characterized by poor thermal performance, high energy use and offer major opportunities for cost-effective GHG emission reduction. In order to address this problem, UNDP design the National Formalization Program in such manner that it would incorporate mandatory requirements and financial support package for energy efficiency improvements as outlined in the following section. The formalisation of Montenegro’s large informal buildings sector represents a unique opportunity to not only insert EE considerations into regulation of this building stock (for the first time ever), but also to integrate informal neighbourhoods and settlements into municipal governments’ spatial planning in order to address urban-system GHG mitigation opportunities in a ‘joined up’ manner. 7.UNDP research in energy efficiency of the illegal houses In the beginning of 2011 Ministry of Sustainable Development and Tourism of Montenegro and UNDP agreed on join implementation of three new pilot projects which deal with problem of transformation of informal settlements to formal. This is related to three municipalities: Zabljak, Bijelo Polje and Bar. Projects activities resulted in: identifying alternative solutions for formalization of informal settlements giving initial study on the energy efficiency characteristics of the informal building sector in Montenegro and an assessment of the economic mitigation potential of the sector, with particular focus on the Government’s Formalization Programme and how the Programme can be harnessed to maximize mitigation outcomes – in terms of the buildings themselves and also how they can be best integrated into broader urban planning. proposing different economical scenarios for formalization process 140 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo encouraging housing opportunity for people of low and moderate income by creative, flexible, and innovative approach to resolving this issue Purpose of the energy audits was to determine a baseline for consumption and potential savings giving the most basic renovation/retrofit measures. Every energy audit consisted of basic information about the existing object, its current use, dimensions, number of inhabitants, heating periods during the day and the whole year, local climate characteristics etc. Data on average yearly consumption of electricity and consumption of water was collected from Public Utility Companies. This was provided with assistance of municipal officials9. Figure 1: The appearance of used software Energy audit team used the following measuring equipment during the inspection of the buildings :Thermal Imager-3 Testo880 PROSet; Data loggers for measuring temperature and humidity Testo 175 and Testo 635-2 Luksmetar. 9 Calculation of building energy performance was performed using: ENSI (Energy Savings International AS) "ENSI EAB CG 8.1". The algorithm for calculation in the current version of the Key Number software relies mainly on the EN ISO 13790:2004 standard. Economic calculation is done in the "ENSI Profitability Software - Version 7.0". 141 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Figure 2: Results of thermal camera imaging (one of the audited buildings in Bijelo Polje) After revision of all provided audits, a general conclusion regarding possibilities for EE retrofitting in informal settlements is that, on average, with €3,800 investment in retrofits the annual savings are €700 (payoff in less than 6 years), and this is in accordance with current energy prices (€ 0.7/kWh as opposed to € 0.17 kWh which is average within liberalized energy market in Europe). More detailed average results are, as follows: Average building (heated) area Average electricity bill [€/god] (for 2009/2010/2011) Baseline (kWh/m2 year) Baseline (kWh/year) After EE retrofit measures (kWh/m2year) After EE retrofit measures (kWh/year) Calculated savings (kWh/m2year) Lowering of CO2 emission (tons/year) 142 116.80 1240.32 468.81 52771.05 169.74 20122.85 303.49 0.82 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Assessment of the investment in EE retrofit measures 4458.20 [€] Net savings [€/year] Return on investment [year] 736.15 5.60 Savings in delivered energy / wooden logs 32945.80 (kWh/ year) Savings in delivered energy / electrical energy (kWh/ year) 574.65 The most cost effective and most often basic EE measures that have been suggested are: appropriate isolation of external walls replacement of windows/doors isolation of roofs EE audits also suggested implementation of additional measures, such as installation of central heating, which will not significantly improve EE performance, but will in general raise a living comfort for the inhabitants. These measures are relatively expensive, and with longer return on investment, but they are also included in narrative part of audits, in order to be considered by the owners as possibility for additional improvement of living conditions. General conclusion is that energy efficiency measures can be used as a tool for encouraging owners of the informal object to apply for legalization. Calculation showed that that each household that apply for formalization will have almost the same cost as it pay regularly for electricity today, but now this cost covers electricity bill, but also retrofitting and formalization. This means that with the same amount of financial resources, they will have legal object, energy efficient and safer house. Energy efficiency measures can be used as a tool for encouraging owners of the informal object to apply for legalization. The main idea is to increase number of applicants, and on the other side to provide solution that would be in line with principles of sustainable development and status of Montenegro as ecological state. Below is explained one of possible the scenarios for formalization using energy efficiency measures as incentive, for average residential building of 100m2. 143 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo EE measure as incentives – calculation: (Example – residential house of 100m2, with average monthly energy bill – 100 euros.) Size of Houshold Cost for Saving energy per month (euro) Formalization (50e per m2) 100 90 5000 € 59% cost Retrofitting cost(interest rate 4.5% on investment 3800eur) 5760 Scenario after retrofitting (costs) Electricity bill Monthly Monthly Total (euro) formalization retrofit cost, 15 cost, 20 yr yr period period Monthly 36.9 20.9 32 89.9 The idea is to use possibility of getting soft loan with no or very low interest rate, with 20 years period for repayment that will be used for retrofitting the object. The main condition for loan is IF household apply for formalization process. This calculation shows that each household that apply for formalization will have almost the same cost as it pays regularly for electricity today, but now this cost covers electricity bill, but also retrofitting and formalization. This means that with same amount of financial resources, they will have legal object, energy efficient and safer house. Revenue from formalization to government Monthly Yearly After 20 years 2,083,333.33€ 25,000,000€ 500,000,000€ Through identifying alternative solutions for formalization of informal settlements and integration of sustainable development principles into planning process, this project will contribute to establishment of the link between economic growth, poverty reduction and environmental sustainability. 144 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo 8. CONCLUSION The paper demonstrates potentials for using energy efficiency as an incentive for formalization of illegal households. Building on the wealth of research on decision making and behavioral economics, the solution features a revenue-neutral option that addresses dual challenges from the consumers’ perspectives (households: inefficient use of energy and illegal house) and dual challenge from the providers’ perspective (Government: low real estate tax collection and low investment in infrastructure). This solution has never been tested before. It will require a multidimensional approach to systemic level change (new regulation and policy development), institutional level change (establishing novel links between the municipal and national level, designing novel processes for financial management) and individual level (capacity building, behavioral change). On the positive note, regardless of its success, this proposal is likely to yield important lessons on the potential for manipulating incentives for green economy. Implications for future research include consideration of incentives related to clean energy production (e.g. solar and wind power) and sustainable urban development (e.g. municipality’s capacity to manage incoming funding for a greener and sustainable urbanization). LITERATURE „More Urban—Less Poor, Fighting poverty in an urban world“, Göran Tannerfeldt and Per Ljung, August 2006 „Trade and Development Report, UNCTAD, 2011, “Trends and Progress in Housing reforms in South Eastern Europe, Sasha Tsenkova, CEB, October 2005 „Towards a Green Economy, Pathways to Sustainable Development and Poverty Eradication“, UNEP, 2011 “Energy Efficiency: Engine of Economic Growth”, Jamie Howland & Derek Murrow, Lisa Petraglia & Tyler Comings, Economic Development Research Group, Inc, October 2009 “Our Common Future”, Brundtland Report, 1987 “Why More Equal Society Almost Always do Better’ Richard Wilkinson, Kate Pickett ‘The Spirit Level: Allen Lane, 2009 145 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo “From Transition to Transformation: Sustainable and Inclusive Development in Europe and Central Asia”, report, 2011 Web: http://www.mckinsey.com/Features/Resource_revolution http://www.clickgreen.org.uk/research/trends/123462-biodiversity-loss-is-as-damaging-asclimate-change-and-pollution.html http://www.levyinstitute.org/pubs/wp_598a.pdf http://www.ipcc.ch/news_and_events/docs/srex/SREX_slide_deck.pdf http://www.unece.org/fileadmin/DAM/publications/oes/RIO_20_Web_Interactif.pdf www.undp.org.me www.mek.gov.me www.energetska-efikasnost.me Situation Of The Dikili Gulf Fishes For Sustainable Fisheries Mehmet İkiz1, Hatice Koç Torcu 2, Fatih Güleç1 1- Ege Üniversitesi, Su Ürünleri Fakültesi, 35080 İzmir 2- Balikesir University, Faculty of Science and Arts, Balikesir-Turkey E-mails: mikiz@mynet.com, htorcukoc@hotmail.com, mc305@live.com Abstract Conservation fish stocks in the aquatic ecosystem is important for sustainable fish production. Continuation of the fish species generations in a habitat is affected by environmental conditions and hunting pressure. For the sustainability of the reproductive abilities of fishes, it is essential to know interactions with the the other species that live in habitat. In this way the production models, that encourage the fish to grow in its natural habitat, can be developed. In this study, the fish species that live in Dikili Bay of Izmir City and their economic features were investigated. Fish species that live in Dikili Bay were examined systematically and biologically; also identification keys of the species were formed. Morphometric and meristic characters of obtained species were identified. In the examination, 70 species belonging to 39 families were identified. 9 species of these belong to chondrichythyes and 61 to osteichtyes. 31 of these species are economically important species and are hunted. 2 of them (Sea bream and sea bass) are farmed in Turkey, also. As a result of inadequate protection measures and mindless hunting, it was observed 31 economically important and identified species, that live 146 � Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Extent The size or duration of the resource. 1238 Title A name given to the resource Could government legalize illegal settlement by improving their energy efficiency? Author Author Janjusevic, Jelena Abstract A summary of the resource. In recent months we are faced with serious budget problems in Montenegro, the solution of which, among other things is seen in reducing the number of employees in state administration. On the other hand, the costs of living are significantly above the disposable budget of households. Particular problem is the high cost of electricity, which recently resulted in the street protests of discontented citizens. On one hand we have a government that alerts the lack of electricity, and on the other hand we have citizens that may hardly cover these costs. In addition, Montenegro is dealing with a double-challenge of inefficient space use (country features over 100,000 illegal homes, if distributed evenly implying that every other family lives in an illegal home) and inefficient energy use (Montenegro needs on average 8.5 times more energy per unit produced than an average EU country). Date A point or period of time associated with an event in the lifecycle of the resource 2012-05-31 Keywords Keywords. Conference or Workshop Item PeerReviewed S Agriculture (General) https://omeka.ibu.edu.ba/files/original/68a47120ae18ec74cd7d533e54a2be14.pdf f7ba5d25d9c3b40841699af4cf75665e PDF Text Text 3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Talinli, I., Topuz, E. and Akbay, M.U. (2010) Comparative Analysis for Energy Production Processes (EPPs): Sustainable Energy Futures for Turkey, Energy Policy, 38, 44794488. Toksarı, M. and Toksarı, M.D. (2011) Bulanık Analitik Hiyerarşi Prosesi (AHP) Yaklaşımı Kullanılarak Hedef Pazarın Belirlenmesi, ODTÜ Gelişme Dergisi, 38, 51-70. Tseng, M-L., Lin, Y-H. and Chiu, A.S.F. (2009) Fuzzy AHP-Based Study of Cleaner Production Implementation in Taiwan PWB Manufacturer, Journal of Cleaner Production, 17, 1249-1256. Wang, L., Xu, L. and Song, H. (2011) Environmental Performance Evaluation of Beijing's Energy Use Planning, Energy Policy, 39, 3483-3495. Zheng, G., Jing, Y., Huang, H., Shi, G. and Zhang, X. (2010) Developing a Fuzzy Analytic Hierarchical Process Model for Building Energy Conservation Assessment, Renewable Energy, 35, 78-87. Zheng, J. (2011) Enterprise Knowledge Management Application Evaluation Based on Cloud Gravity Center Model and Fuzzy Extended AHP, Journal of Computers, 6(6), 11101116. Using Artificial Neural Networks To Forecast Gdp For Turkey Karaatli Meltem, Göçmen Yağcilar Gamze, Karacadal Hüseyin, Sezer Fırat Suleyman Suleyman Demirel University, Isparta, Turkey E-mails: meltemkaraatli@sdu.edu.tr,gamzeyagcilar@sdu.edu.tr, huseyin_karacadal@hotmail.com,cihangir_07_@hotmail.com Abstract Artificial Neural Networks (ANN) is a system resembling biological neural systems and uses working principles of human brain as a base. ANN can be applied in various fields for the purposes of forecasting, classification, optimization, data binding and so on. ANN has been frequently used in financial applications in recent years. In this study, ANN is used in forecasting Gross Domestic Product of Turkey. Gross Domestic Product (GDP) refers to the market value of all final goods and services produced within a country in a given period. GDP can be thought as the size of an economy and it is the foremost important measure of macroeconomic performance of a country, a country’s health and standard of living. Therefore, expectations about future GDP can be the primary determinant of investments, employment, wages, profits and even stock market activities. With respect to its economic 326 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo significance mentioned above, the purpose of this study is to forecast Gross Domestic Product (GDP) for Turkey and to test the ability of ANN Method in forecasting GDP. Keywords: Importance of Gross Domestic Product, Forecasting, Artificial Neural Networks. 1. INTRODUCTION Gross Domestic Product (GDP) is the total market value of all the final goods and services produced within a country’s boarders in a given year. This production is generated by both citizens of the country and foreigners living in its borders. GDP is one of the most important indicators of an economic growth, health and welfare. Therefore, it tells us a lot about the real economic activity. Calculation of GDP can be basically done in one of two ways: either by adding up what everyone earned (income approach), or by adding up what everyone spent (expenditure method) in a year. Logically, both measures should arrive at roughly the same total (www.investopedia.com). In Turkey, GDP is measured quarterly by TUİK. To compute economic growth, each quarter is compared to the previous one. Considering its large impact on almost everybody in an economy, forecasting GDP has a great importance both theoretically and practically. First of all, GDP represents economic production and growth. So it gives a signal about the future employment and wages. GDP also determines stock market return rates. If GDP growth rate is positive, then investors may expect to gain revenue (www.investopedia.com).By using GDP reports, it can be seen which sectors of the economy are growing and which ones are declining. This would help investors to determine whether they should invest in or which sectors they should invest in (http://useconomy.about.com/od/grossdomesticproduct/p/GDP.htm). The GDP statistics can help the economists a lot in solving the problems of inflation in the country. The national income figures throw light as to how much general price level has increased or decreased, how much of their income people spend on consumption goods and how much they save? Government can devise measures of controlling inflation or deflation on the basis of these figures of consumption, saving and investment in the country (http://www.economicsconcepts.com/gdp_as_a_measure_of_welfare.htm). In the existing literature, forecasting GDP is widely studied with different methods. In this paper, we wish to determine whether the forecasting performance of this variable can be improved using neural network models. In this context, the purpose of this study is to forecast GDP of Turkey using Artificial Neural Networks (ANN) Method. The rest of this paper is organized as follows: Section 2 reviews some of the literature on GDP forecasts. Section 3 describes the methodology, while Section 4 presents the results. Finally, section 5 concludes the paper. 2. Literature review 327 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Tkacz and Hu (1999) have determined whether more accurate indicator models of output growth, based on monetary and financial variables, can be developed using neural networks. The authors have used ANN model to forecast GDP growth for Canada. The main findings of this study are that, at the 1-quarter forecasting horizon, neural networks yield no significant forecast improvements. At the 4-quarter horizon, however, the improved forecast accuracy is statistically significant. The root mean squared forecast errors of the best neural network models are about 15 to 19 per cent lower than their linear model counterparts. Marcellino (2007) has evaluated whether complicated time series models can outperform standard linear models for forecasting GDP growth and inflation for the United States. In the study, it is considered as a large variety of models and evaluation criteria, using a bootstrap algorithm to evaluate the statistical significance of the results. The main conclusion is that in general linear time series, models can be hardly beaten if they are carefully specified. Schumacher and Breitung (2008) have employed factor models to forecast German GDP using mixed-frequency real-time data, where the time series are subject to different statistical publication lags. In the empirical application, the authors have used a novel real-time dataset for the German economy. Employing a recursive forecast experiment, they have evaluated the forecast accuracy of the factor model with respect to German GDP. Guegan and Rakotomarolahy (2010) have conducted an empirical forecast accuracy comparison of the non-parametric method, known as multivariate Nearest Neighbor method, with parametric VAR modeling on the euro area of GDP. By using both methods for now casting and forecasting the GDP, through the estimation of economic indicators plugged in the bridge equations, the authors have got more accurate forecasts when using nearest neighbor method. It is also proven the asymptotic normality of the multivariate k-nearest neighbor regression estimator for dependent time series, providing confidence intervals for point forecast in time series. Mirbagheri (2010) has investigated the supply side economic growth of Iran by estimating GDP growth. In this study, the predictive results of Fuzzy-logic and Neural-Fuzzy methods are also compared. According to the findings of the study, forecasting by the Neural-Fuzzy method is recommended. Ge and Cui (2011) have used process neural network (PNN) into the GDP forecast and established the forecast model based on PNN by choosing the main factors influencing GDP and using the dual extraction capacity on time and space cumulative effect of PNN. By means of comparing and analyzing with traditional neural network forecast model, the result shows that GDP forecast model which bases on PNN has a better performance. Liliana and Napitupulu (2010) have also used ANN method in forecasting GDP. In this study, authors have forecasted GDP for Indonesia and they put forward many advantages and disadvantages of the method. According to the results, the authors have concluded that the ANN model has better ability in forecasting the macroeconomic indicators. 3. Methodology 328 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Artificial neural networks (ANN) may be identified as computing technologies containing performances and general features of biological neural networks (Deng v.d., 2008:1118). ANN, developed by imitating the human brain's operating mechanism with the aim of realizing the basic operations performed by the brain, is a logical computer programming technique. In a computer media, an algorithm, which attempts to operate as the brain does, makes a decision, makes a conclusion, arrives at a conclusion on the basis of the existing data when data are missing, accepts new data input constantly, learns and remembers, is called as "Artificial Neural Networks".(Kaltakçı, 1997:411-420) Artificial neural networks consist of many simple processing elements called as nodes or nerves. Each nerve is attached to the other nerves with weights. These weights indicate the information used by the network to solve a problem. Nerves are located in each layer and these layers are interconnected to the other nerves in adjacent layers. A weight gives the mathematical value of the relative power of information's connections that have been transferred from one layer to another. Addition function calculates the sum of all the weighted inputs of a nerve. Activation function is used for the conversion of output in an acceptable range. (usually 0-1 range). Input layer is identified with the independent variables while output layer is identified with the dependent variables (Deng v.d., 2008:1118). Networks having one layer are called single-layered neural networks while networks having more than one layer are called multilayered neural networks. In a multilayered neural network, number of neurons in each layer may vary (Hines, 1997; 206). While a singlelayered network consists of an input and output layer, a multilayered network may consist one or more middle (hidden) layers. As the number of middle layers increases, the ability of artificial neural network to get statistics from input data also increases (Nygren, 2004). If an artificial neural network is required to solve a nonlinear problem, a more sophisticated type of network is needed for these types of problems. Multilayered sensors (MLS) are network architectures developed for this purpose. This network has a forward network architecture and a supervised learning method is used (Deng, 2008:1118). MLS consists of an input layer, one or more middle layers and an output layers. Each layer has one or more processing elements. All processing elements in a layer is interconnected to all processing elements in a top layer. The flow of information is forwards and there is no feedback. Therefore, these types of networks are called as feed-forward neural network model. There is no information processing in input layer. The number of processing elements in input and output layers is totally dependent on the practiced problem. The number of middle layers and the number of processing elements in middle layers are found by trial and error method (Lippmann, 1987; 24-25). Each produced output in these types of networks is compared with the target output in each learning iteration and errors are calculated. By propogating backwards in neural network, this error is used to correct the weights. This process goes ahead so long as the mean squared error between target output and output produced by network is minimized (Deng, 2008:1118). For this reason, this type of network is also called as error propogation model or backpropogation 329 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo network model (Öztemel, 2003:76). These types of networks are illustrated (exemplified) in Figure 1). Figure 1: A Multilayered Network Model Output Backward Error Flow Forward Activation Flow Output Layer ….. Middle Layer Input ….. Layer Input 1 Input 2 Input N Kaynak: (Hamid ve Iqbal, 2004:1118) 4. Forecasting Gross Domestic Product with ANN In this study, by the method of artificial neural networks, the gross domestic product has been estimated on the basis of the calculated data by the method of three-monthly expenditures for the years of 1998-2010. Data have been drawn from the website of Turkish Statistical Institute. In the study, 52 pieces of data have been used for each variable covering the threemonthly periods of 13 years. 20% of the data consists of tests and 80% of it consists of trainings which thus randomly creates 4 different groups. Gross domestic product consists of a composite of macroeconomic variables such as resident household consumption, government final consumption expenditure, gross fixed capital formation, stock exchanges, export and import of goods and services. Gross domestic product is considered to be dependent variable while household consumption, government final consumption expenditure, gross fixed capital formation, stock exchanges and export and import of goods and services and time are considered to be independent variable. Together with their symbols, the dependent and independent variables used in the study are shown below. Gross National Product: GDP Time: T 330 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Resident Household Consumption: RHC Government final consumption expenditure: GFCE Gross fixed capital formation: GFCF Stock Exchanges: SE Goods and Services Expenditures: GSE Import of Goods and Services: IGS In the study, as the values of independents are unknown during the desired terms accept the time variable, GDP ,which is a dependent variable, has been predicted after each independent variable has been estimated separately depending on the time. Namely, each independent variable has been considered as dependent variable and they have been predicted depending on the time variable. Different neuron numbers and hidden layer numbers have been tested to find the most appropriate network which will be used in the prediction of all variables. The estimated performance metrics have been evaluated in determining the most appropriate network. The network structure, of which forecasting measurements are the smallest, is identified as the most suitable one. The most appropriate network structures used to predict the all variables are illustrated in Table 2. Yet, as the stock exchanges, taken as independent variable, have so many sharp rises and falls, each quarter is estimated and combined within itself. The estimation performance metrics; MSE (Mean Square Error), RMSE (Root mean square) and MAPE (Mean absolute percentage error), which are commonly used in the literature, are shown in Formula 1,2 and 3 (Zhang ve Hu, 1998:500, Cho, 2003:328, De Lurgio, 1998:53).  (y RMSE   t  yt )2 T (1)  MAPE  1 T  yt  yt yt  100 (2)  MSE    y t    yt   2 T Here; yt = The actual observation values,  yt = Estimated values, T = Estimated numb 331 (3) �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Table1: The network structures used for estimation of variables Number of neurons in the input layer The number of intermidiate layer neurons Number of neurons in the output layer R MAPE µ ( The number of iteration) RHC 1 3 1 0,97 3,73 3 GFCE 1 4 1 0,96 3,94 5 GFCF 1 3 1 0,81 9,32 10 1 0,83 70,5 20 SE Independent variables 2 3 2 SE1 1 SE2 1 3 1 0,86 88,6 20 SE3 1 5 1 0,78 6,6 15 SE4 1 1 0,97 19,5 20 2 3 GSE 1 2 1 0,94 5,20 15 IGS 1 3 1 0,95 6,6 12 GDP 7 3 1 0,99 2,77 2 Estimation performance metrics of Gross domestic product (GDP) are obtained as MSE=0,000042, RMSE=0,006451 ve MAPE=2,775746%. On the basis of these measurements, Witt and Witt (2000) classified the estimation models and called those whose MAPE values are under 10% as the models having " high accuracy" and those whose values are between 10% nd 20% as the "correct predictions". Similarly, Lewis classified the models and called those hose MAPE values are less than 10% as "very good", those between 10% and 20% as "good", those between 20% and 50% as "acceptable" and those under 50% as "false and erroneous" (Aktaran, Çuhadar ve Kayacan, 2005:6). 332 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Figure2: The optimum network structure to estimate the GDP Input layer RHC Hidden layer GFCE Outputlayer GFCF SE GDP GSE IGS T In this study, Matlab 7.9 computer package program has been used. For training function 'trainlm' , for learning function 'learngdm', for performance function 'MSE' and for the transfer function 'tansig' have been selected. In the study, predicted and actual values have been given in Table 2. Table 2: Actual and Estimated Values of GDP 333 Actual(1.000TL) Estimated (1.000TL) 2011 GDP 85.139.293 109.708.230 2011-Q1 26.205.423 26.070.548 2011-Q2 27.904.922 27.911.332 2011-Q3 31.028.948 28.430.643 2011-Q4 --------- 27.295.707 2012 GDP --------- 111.233.502 2011-Q1 --------- 26.813.588 2011-Q2 --------- 28.153.427 2011-Q3 --------- 28.500.755 2011-Q4 --------- 27.765.733 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo 5. CONCLUSIONS Gross Domestic Product is an important indicator for all economic units including companies, investors and households. Because it determines their future incomes, returns of their investments, cost of capital and so on. So economic units make their decisions and set economic policies depending on future economic conditions determined by what the future GDP will be. Here the question is which methods can be more suitable and successful in forecasting GDP. In this paper we applied Artificial Neural Networks method as a prediction model. Results suggest that forecasting performance of this variable can be improved using neural network models. REFERENCES Cho, V. (2003). “A Comparison of Three Different Approaches to Tourist Arrival Forecasting”, Tourism Management, 24: 323-330. Çuhadar, M. ve Kayacan C. (2005), “Yapay Sinir Ağları Kullanılarak Konaklama İşletmelerinde Doluluk Oranı Tahmini: Türkiye’deki Konaklama İşletmeleri Üzerine Bir Deneme”, Anatolia:Turizm Araştırmaları Dergisi, 16(1): 1990-2005. De LURGIO, A. S. (1998), Forecasting Principles and Applications, Irwin McGrawHill:Singapore. Deng, Wei-Jaw, Wen-Chin Chen, Wen Pei “Back-propagation neural network based importance–performance analysis for determining critical service attributes”, Expert Systems with Applications 34 (2008) 1115–1125. Ge, L., Cui, B., (2011), “Research on Forecasting GDP Based on Process Neural Network”, IEEE 2011, 7. International Conference on Natural Computation, 821-824. Guegan, D., Rakotomarolahy, P., (2010), “Alternative Methods for Forecasting GDP”, University of Paris, CES Working Papers, 2010.65. Hamid, Shaikh, A. ve Zahid Iqbal (2004), “Using Neural Networks for Forecasting Volatility of S&P 500 Index Futures Prices”, Journal of Business Research, 57: 1116-1125. Hines, J, W., MATLAB, Supplement to Fuzzy and Neural Approaches in Engineering, John Wiley&Sons, Inc., 1997. Kaltakci, M, Y., Dere, Y., Yapay Sinir Ağları Uygulamalarının İnşaat Mühendisliğinde Kullanımı, Prof. Dr. Rifat Yarar Sempozyumu, Editör: Semih S. Liliana, Napitupulu, T.A., (2010), “Artificial Neural Network Application in Gross Domestic Product Forecasting- an Indonesian Case”, 2. International Conference on Advances in Computing, Control and Telecommunication Technologies, IEEE 2010, 89-93. Lippmann, R., “An Introduction to Computing with Neural Nets”, Vol.4, 1987. Marcellino, M., (2007), “A Comparison of Time Series Models for Forecasting GDP Growth and Inflation”, http://www.eui.eu/Personal/Marcellino/1.pdf. 334 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Mirbagheri, M., (2010), “Fuzzy Logic and Neural Network Fuzzy Forecasting of Iran GDP Growth”, African Journal of Business Management, Vol.4, No.6, 925-929. Nygren, K., Stock Prediction: A Neural Network Approach, Master Thesis, Royal Institute Of Technology, KTH, 2004. Öztemel, E., Yapay Sinir Ağları, Papatya Yayıncılık, İstanbul, 2003. Schumacher, C., Breitung, J., (2008), “Real-time Forecasting of German GDP based on Large Factor Model with Monthly and Quarterly Data”, International Journal of Forecasting, Vol. 24, 386-398. Tkacz, Greg, Hu, Sarah, (1999), “Forecasting GDP Growth Using Artificial Neural Networks”, Bank of Canada Working Papers, 99-3. Zhang, G., Hu, M.Y. (1998) “Neural Network Forecasting of the British Pound/US Dollar Exchange Rate”, Omega Int. J. Mgmt. Sci, 26(4): 495-506. (http://useconomy.about.com/od/grossdomesticproduct/p/GDP.htm). (http://www.economicsconcepts.com/gdp_as_a_measure_of_welfare.htm). (www.investopedia.com). The Importance And The Place Of Ombudsman In Law State Feyzullah Ünal Dumlupinar University, Faculty of Economics and Administrative Sciences E-mail: feyz_unal@mynet.com Abstract In analyzing the ombudsman from the respesct of its historical roots, it is understood that this institution has been inspired by Islam state system and Otoman state system. The institution ombudsman has been implemented in countries more than 100 today and overtaken the mission of protecting the citizens against the maladministration, securing the fundamental rights and liberty and constituted security for both governing and governed. In this study, it is offered that the fundamental rights and freedoms should be under the security, all activities of the government should be under the control of jurisdiction and the significance of this institution sould be awared in realizing the legal governance. Keywords: Ombudsman, law state, fundamental rights and freedoms, justice, control and judicial control. 335 � Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Extent The size or duration of the resource. 1129 Title A name given to the resource Using Artificial Neural Networks To Forecast Gdp For Turkey Author Author Karaatli, Meltem Abstract A summary of the resource. Artificial Neural Networks (ANN) is a system resembling biological neural systems and uses working principles of human brain as a base. ANN can be applied in various fields for the purposes of forecasting, classification, optimization, data binding and so on. ANN has been frequently used in financial applications in recent years. In this study, ANN is used in forecasting Gross Domestic Product of Turkey. Gross Domestic Product (GDP) refers to the market value of all final goods and services produced within a country in a given period. GDP can be thought as the size of an economy and it is the foremost important measure of macroeconomic performance of a country, a country’s health and standard of living. Therefore, expectations about future GDP can be the primary determinant of investments, employment, wages, profits and even stock market activities. With respect to its economic significance mentioned above, the purpose of this study is to forecast Gross Domestic Product (GDP) for Turkey and to test the ability of ANN Method in forecasting GDP. Keywords: Importance of Gross Domestic Product, Forecasting, Artificial Neural Networks. Date A point or period of time associated with an event in the lifecycle of the resource 2012-05-31 Keywords Keywords. Conference or Workshop Item PeerReviewed H Social Sciences (General) https://omeka.ibu.edu.ba/files/original/b26595efc28ad1061041d54c95c6f372.pdf 194b2e51a734a9a514c96b5039f69e48 PDF Text Text 3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Clustering Balkan Countries Based on Competitiveness Factors: A Strategic Perspective Kazim Develioglu1, Kemal Kantarci2 1Akdeniz University, Alanya Faculty of Business,Department of Human Resource Management 2Akdeniz University, Alanya Faculty of Business Department of Tourism Management E-mails: kdevelioglu@akdeniz.edu.tr, kantarci@akdeniz.edu.tr Abstract Prior to directing their investments, strategy makers at national and firm level need to know competitive advantages and disadvantages in a country or region. By bearing this need in mind, this study aims to examine competitive factors in Balkan countries to develop a road map for investors. To do this, we used World Economic Forum’s “Global Competitivenes Index” to analyse the case of Balkan countries as a region to cluster and compare them based on Global competitiveness factors. Analysis results pointed out that Balkan countries were clustered in two groups and scored lower or medium level on almost all competitive factors as the region. Based on these findings, authors suggested various strategic recommendations at micro and macro level. Keywords: Cluster, Competitiveness, Strategic Management, Balkan Countries 1.Literature review In an era of great competition among nations and firms, it is vital for firms’ strategy makers to develop strategies to adapt to environmental changes and speed their processes. Vietor (2006) indicates that, in national level, as a result of globalizaton, countries compete each other in terms of markets, technology, skills, and investment to grow and raise their standards of living. Although, macroeconomic competitiveness creates the potential for high productivity, it is not sufficient. Productivity ultimately depends on improving the micro economic capability of the economy and sophistication of local competition (Porter, 2009). Economic Forum (2011) defines competitiveness as the set of institutions, policies, and factors that determine the level of productivity of a country. The level of productivity, in turn, sets the level of prosperity that can be earned by an economy. The productivity level also determines the rates of return obtained by investments in an economy, which in turn are the 199 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo fundamental drivers of its growth rates. In other words, a more competitive economy is one that is likely to grow faster over time. “Competitive strategy is the search for a favorable competitive position in an industry, the fundamental arena in which competition occurs. Competitive strategy aims to establish a profitable and sustainable position against the forces that determine industry competition” (Porter, 2004: 1). To be competitive, nations are struggling to remain competitive by having regional specializations in terms of hihger value added – non manufacturing industries and Research & Development intensive manufacturing niches (OECD, 2007). Similarly, Porter (2009) indicates that competitiveness depends on the productivity with which a nation uses its human, capital, and natural resources. Economic coordination among neighboring countries can significantly enhance competitiveness. By the similar vein, as developing countries, economic collaboration among Balkan countries is expected to enhance sustainable competition. At this point, it has to be noted that competition policies of advanced countries might not be appropriate for the stage of development of most developing countries (Singh, 1999). Singh (1999) indicates that “It is important for developing countries to have a competition policy which is designed to take appropriate account of their level of development and the long term objective of sustained economic growth. This is in part due to the potential effects of the international merger movement and also because of privatization, deregulation and liberalization which have occurred in the domestic economies of most developing countries” (pp. 1). As a developing region, the Balkan peninsula is becoming recovered and develop after postsocialist and instable period because of the war among some of states. “The Balkan Peninsula is an important area, having witnessed important historical and political experiences and incidents for ages” (Çelebioğlu 2011: 112). Having a population of, nearly, 140 million citizens, the Balkan region provides a promising market for firms from international arena and especially Balkan countries. As it is indicated in WEF’s (2011-2012) Global Competitiveness Report, “national competitiveness, we note that despite much work in the area of sustainability, there is not yet a well-established body of literature on the link between productivity (which is at the heart of competitiveness) and sustainability. However, at the World Economic Forum we believe that the relationship between competitiveness and sustainability is crucial (pp. 52). Developing economically sound strategies, especially for international firms and firms from the region, it is crucial to examine competitiveness indicators of Balkan countries. This will help firms to develop a sustainable competitive edge by investing and selling in the region. Taking this neccessity into account, this study aims to fill the gap for lack of comparative studies for Balkan countries. More specifially, we analyse Balkan countries’ competitiveness factors by, first, clustering them and, second, compare the clusters to grasp which cluster perform in which competitive factor well. 200 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo In this study, we used the data of The World Economic Forum’s (WEF) classification of “Global Competitiveness Index” factors to examine indicators that are expected to influence sustainable competition in the region. for the years between 2008-2011. WEF’s classification consists of three subindexes and 12 factors that measure these subindexes, which are reported below:     Basic requirements (Institutions, Infrastructure, Macroeconomic environment, and Health and primary education) Efficiency enhancers (Higher education and training, Goods market efficiency, Labor market efficiency, Financial market development, Technological readiness, and Market size) Innovation and sophistication factors (Business sophistication and Innovation) 2.Methodology As it is mentioned above, in this study, we used the data of The World Economic Forum’s (WEF) “Global Competitiveness Index” for the years between 2008-2011. By using the secondary data, we aimed, first, to cluster the Balkan countries in terms of above mentioned “Global competitiveness index factor”s and second to compare these clusters to reveal which of them are more competitive in subindexes and factors. 3.Findings In order to cluster the Balkan countries in terms of Global competitiveness factors, we employed a k-means cluster analysis and derived two clusters, which is reported in Table 1 below. One of these clusters (Cluster 1) includes countries: Bulgaria, Croatia, Greece, Romania, Serbia, and Turkey. The second cluster (Cluster 2) countries are Albania, Bosnia and Herzegovina, Macedonia, Montenegro, and Slovenia. Scores in Table 1 betray that only in market size competitiveness factor, Cluster 1 countries have a competitive advantage compared with Cluster 2 countries. Table 1: Cluster Analysis Results Cluster Global Competitiveness Factor 1 2 F p Institutions 3,63 4,35 1,784 0,214 Infrastructure 4,00 3,38 0,401 0,542 Macroeconomic environment 4,70 4,93 1,827 0,209 Health and primary education 5,45 5,90 0,033 0,860 Higher education and training 3,95 4,38 0,022 0,885 201 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Goods market efficiency 4,33 4,35 0,396 0,545 Labor market efficiency 3,60 4,58 3,599 0,090 Financial market development 4,18 4,83 0,021 0,889 Technological readiness 3,78 4,05 0,105 0,754 Market size 5,20 2,05 15,499 0,003 Business sophistication 4,20 3,80 0,018 0,897 Innovation 3,13 3,30 0,120 0,737 Table 2: t-test Results for Cluster Membership and Global Competitiveness Subindexes Std. Deviation Variable Basic requirements Efficiency enhancers Innovation and sophistication factors Cluster Mean 1 4,38 0,246 2 4,47 0,449 1 4,06 0,161 2 3,87 0,326 1 3,39 0,214 2 3,34 0,473 t p -0,858 0,396 2,547 0,015 0,479 0,634 In order to compare Cluster 1 and Cluster 2 countries, we used t-test analysis and obtained the results, which are reported in Table 2 and Table 3. In table 2, we compared two clusters in terms of Global Competitiveness subindexes. Results in Table 2 portray that Cluster 1 (Mean= 4,06) and Cluster (Mean= 3,87) countries both had medium-level but statistically significant difference (t= 2,547; P= 0,015) in efficiency enhancers subindex. For the other two subindexes, namely basic requirements (t= 0,858; P= 0,396) and innovation and sophistication factors (t= 0,479; P= 0,634), both of the clusters showed no statistically significant results. It has to be noted that in both, basic requirements and innovation and sophistication factors, Cluster 1 and Cluster 2 countries had medium level competitiveness scores. Table 3: t-test Results for Cluster Membership and Global Competitiveness Factors Variable 202 Cluster Mean Std. t p �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Deviation Institutions Infrastructure Macroeconomic environment Health and primary education Higher education and training Goods market efficiency Labor market efficiency Financial market development Technological readiness Market size Business sophistication Innovation 1 3,53 0,233 2 3,84 0,515 1 3,70 0,691 2 3,43 0,851 1 4,55 0,482 2 4,89 0,435 1 5,73 0,228 2 5,76 0,319 1 4,21 0,254 2 4,17 0,625 1 4,00 0,239 2 4,12 0,376 1 4,04 0,325 2 4,34 0,208 1 4,04 0,224 2 4,07 0,504 1 3,82 0,286 2 3,74 0,616 1 4,20 0,579 2 2,83 0,479 1 3,75 0,313 2 3,72 0,427 1 3,45 0,131 2 2,97 0,507 -2,657 0,011 1,158 0,254 -2,406 0,021 -0,332 0,741 0,305 0,762 -1,194 0,239 -3,592 0,001 -0,255 0,800 0,597 0,554 8,427 0,000 0,268 0,790 0,705 0,485 Examination of Table 3 revealed mixed results for Cluster 1 and Cluster 2 countries. In Table 3, the results betray that Cluster 2 countries scored better in three of twelve Global Competitiveness factors than Cluster 1 countries. Only for market size competitiveness factor, Cluster 1 countries had statistically significant difference scores (t= 8,427; P= 0,000). 203 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo 4.Discussion Analysis results at the findings section pointed out that competitiveness scores of Balkan countries, whether it belongs Cluster 1 or Cluster 2, are relatively low or medium and need to be developed. Specifically, Cluster 2 countries (Albania, Bosnia and Herzegovina, Macedonia, Montenegro, and Slovenia) should have a national strategic plan to improve their competitive position in infrastructure (quality of roads, railroads, ports, and airtransport infrastructure), higher education and training (secondary education enrollment, tertiary education enrollment, quality of the educational system, math &science education, management schools, internet access in schools, availability of research and services), goods market efficiency (intensity of local competition, extent of market dominance, effectiveness of anti-monopoly policy, extent and effect of taxation, total tax rate, number of procedures to start a business, agricultural policy cost, buyer sophistication), labor market efficiency (cooperation in labor-employer relations, flexibility of wage determination, hirin and firing practices, women in labor force), financial market development (availability of financial services, effordability of financial services, ease of access to loans, ventur capital availability), technological readiness (availability of latest technologies, firm-level technology absorption, FDI and technology transfer, internet related factors), business sophistication (local supplier quantity and quality, state of cluster development, nature of competitive advantage, control of international distribution, extent of amrketing, willingness to delegate authority), and innovation (capacity for innovation, quality of scientific research institutions, company spending on R&D, utility patents granted). Similarly, Cluster 1 countries should emphasize on development of institutions, infrastructure, financial market, and technological environment and better conditions in macroeconomic environment, higher education and training, goods market efficiency, business sophistication, and innovation. It seems from analysis results that the major advantage for these cluster is their population and market size. This picture warns us that firms plan to invest in the Balkan region should be aware of disadvantageous competitive factors in both cluster countries. It seems that eventhough both clusters have disadvantages for investors they also offer certain advantages for them. We believe that for strategy makers in national governments and firms, these findings provide useful insights to develop their strategic plans. REFERENCES Çelebioğlu, F. (2011). Investigation of Development Indicators in the Balkan Countries for the Post-Socialist Period, Journal of Economic and Social Studies, Volume 1, Number 1, 111-122. Porter, M. E. (2004). Competitive Advantage, Free Press, New York. 204 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Porter, M. E. (2009). The Competitive Advantage of Nations, States, and Regions, Harvard Business School, Advanced Management Program. OECD, (2007). Competitive Regional Clusters: National Policy Approaches, (http://www.oecd.org/document/2/0,3746,en_2649_33735_38174082_1_1_1_1,00.html), (22.04.2012). Singh, A., (1999). Competition Policy, development and developing Countries, Indian Council for research on international economic relations, New Delhi. Vietor, R.H.K. (2006). Strategy, Structure, and Government in the Global Economy, Harvard Business School Press ,Boston, Massachusetts. World Economic Forum, The Global Competitiveness Report, (2008-2009). World Economic Forum, The Global Competitiveness Report, (2009-2010). World Economic Forum, The Global Competitiveness Report, (2010-2011). World Economic Forum, The Global Competitiveness Report, (2011-2012). Implementation Of Critical Path Method And Project Evaluation And Review Technique Ali Göksu, Selma Ćatović International Burch University,Faculty of economics Management and information technologies Sarajevo, Bosnia and Herzegovina Abstract Because of the growing effects of the globalization in various business environments, the manufacturing industry is expected to be effective and yet efficient. According to this, in 205 � Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Extent The size or duration of the resource. 1113 Title A name given to the resource Clustering Balkan Countries Based on Competitiveness Factors: A Strategic Perspective Author Author Kazim, Develioglu Abstract A summary of the resource. Prior to directing their investments, strategy makers at national and firm level need to know competitive advantages and disadvantages in a country or region. By bearing this need in mind, this study aims to examine competitive factors in Balkan countries to develop a road map for investors. To do this, we used World Economic Forum’s “Global Competitivenes Index” to analyse the case of Balkan countries as a region to cluster and compare them based on Global competitiveness factors. Analysis results pointed out that Balkan countries were clustered in two groups and scored lower or medium level on almost all competitive factors as the region. Based on these findings, authors suggested various strategic recommendations at micro and macro level. Keywords: Cluster, Competitiveness, Strategic Management, Balkan Countries Date A point or period of time associated with an event in the lifecycle of the resource 2012-05-31 Keywords Keywords. Conference or Workshop Item PeerReviewed H Social Sciences (General) https://omeka.ibu.edu.ba/files/original/b7bc98d09981b7daf894effbf5781231.pdf b40a53b0771d61acf2bad3e7fa9bf8d4 PDF Text Text 3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Clustering Balkan Countries Based on Competitiveness Factors: A Strategic Perspective Kazim Develioglu1 ,Kemal KantarcI2 1Akdeniz University, Alanya Faculty of Business,Department of Human Resource Management Alanya-Antalya / TURKEY 2Akdeniz University, Alanya Faculty of Business,Department of Tourism Management Alanya-Antalya / TURKEY E-mails: kdevelioglu@akdeniz.edu.tr ,kantarci@akdeniz.edu.tr Abstract Prior to directing their investments, strategy makers at national and firm level need to know competitive advantages and disadvantages in a country or region. By bearing this need in mind, this study aims to examine competitive factors in Balkan countries to develop a road map for investors. To do this, we used World Economic Forum’s “Global Competitivenes Index” to analyse the case of Balkan countries as a region to cluster and compare them based on Global competitiveness factors. Analysis results pointed out that Balkan countries were clustered in two groups and scored lower or medium level on almost all competitive factors as the region. Based on these findings, authors suggested various strategic recommendations at micro and macro level. Keywords: Cluster, Competitiveness, Strategic Management, Balkan Countries 1.Literature review In an era of great competition among nations and firms, it is vital for firms’ strategy makers to develop strategies to adapt to environmental changes and speed their processes. Vietor (2006) indicates that, in national level, as a result of globalizaton, countries compete each other in terms of markets, technology, skills, and investment to grow and raise their standards of living. Although, macroeconomic competitiveness creates the potential for high productivity, it is not sufficient. Productivity ultimately depends on improving the micro economic capability of the economy and sophistication of local competition (Porter, 2009). Economic Forum (2011) defines competitiveness as the set of institutions, policies, and factors that determine the level of productivity of a country. The level of productivity, in turn, sets the level of prosperity that can be earned by an economy. The productivity level also determines the rates of return obtained by investments in an economy, which in turn are the fundamental drivers of its growth rates. In other words, a more competitive economy is one that is likely to grow faster over time. 125 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo “Competitive strategy is the search for a favorable competitive position in an industry, the fundamental arena in which competition occurs. Competitive strategy aims to establish a profitable and sustainable position against the forces that determine industry competition” (Porter, 2004: 1). To be competitive, nations are struggling to remain competitive by having regional specializations in terms of hihger value added – non manufacturing industries and Research & Development intensive manufacturing niches (OECD, 2007). Similarly, Porter (2009) indicates that competitiveness depends on the productivity with which a nation uses its human, capital, and natural resources. Economic coordination among neighboring countries can significantly enhance competitiveness. By the similar vein, as developing countries, economic collaboration among Balkan countries is expected to enhance sustainable competition. At this point, it has to be noted that competition policies of advanced countries might not be appropriate for the stage of development of most developing countries (Singh, 1999). Singh (1999) indicates that “It is important for developing countries to have a competition policy which is designed to take appropriate account of their level of development and the long term objective of sustained economic growth. This is in part due to the potential effects of the international merger movement and also because of privatization, deregulation and liberalization which have occurred in the domestic economies of most developing countries” (pp. 1). As a developing region, the Balkan peninsula is becoming recovered and develop after postsocialist and instable period because of the war among some of states. “The Balkan Peninsula is an important area, having witnessed important historical and political experiences and incidents for ages” (Çelebioğlu 2011: 112). Having a population of, nearly, 140 million citizens, the Balkan region provides a promising market for firms from international arena and especially Balkan countries. As it is indicated in WEF’s (2011-2012) Global Competitiveness Report, “national competitiveness, we note that despite much work in the area of sustainability, there is not yet a well-established body of literature on the link between productivity (which is at the heart of competitiveness) and sustainability. However, at the World Economic Forum we believe that the relationship between competitiveness and sustainability is crucial (pp. 52). Developing economically sound strategies, especially for international firms and firms from the region, it is crucial to examine competitiveness indicators of Balkan countries. This will help firms to develop a sustainable competitive edge by investing and selling in the region. Taking this neccessity into account, this study aims to fill the gap for lack of comparative studies for Balkan countries. More specifially, we analyse Balkan countries’ competitiveness factors by, first, clustering them and, second, compare the clusters to grasp which cluster perform in which competitive factor well. In this study, we used the data of The World Economic Forum’s (WEF) classification of “Global Competitiveness Index” factors to examine indicators that are expected to influence sustainable competition in the region. for the years between 2008-2011. WEF’s classification consists of three subindexes and 12 factors that measure these subindexes, which are reported below: 126 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo    Basic requirements (Institutions, Infrastructure, Macroeconomic environment, and Health and primary education) Efficiency enhancers (Higher education and training, Goods market efficiency, Labor market efficiency, Financial market development, Technological readiness, and Market size) Innovation and sophistication factors (Business sophistication and Innovation) 2.Methodology As it is mentioned above, in this study, we used the data of The World Economic Forum’s (WEF) “Global Competitiveness Index” for the years between 2008-2011. By using the secondary data, we aimed, first, to cluster the Balkan countries in terms of above mentioned “Global competitiveness index factor”s and second to compare these clusters to reveal which of them are more competitive in subindexes and factors. 3.Findings In order to cluster the Balkan countries in terms of Global competitiveness factors, we employed a k-means cluster analysis and derived two clusters, which is reported in Table 1 below. One of these clusters (Cluster 1) includes countries: Bulgaria, Croatia, Greece, Romania, Serbia, and Turkey. The second cluster (Cluster 2) countries are Albania, Bosnia and Herzegovina, Macedonia, Montenegro, and Slovenia. Scores in Table 1 betray that only in market size competitiveness factor, Cluster 1 countries have a competitive advantage compared with Cluster 2 countries. Table 1: Cluster Analysis Results Cluster Global Competitiveness Factor 1 2 F p Institutions 3,63 4,35 1,784 0,214 Infrastructure 4,00 3,38 0,401 0,542 Macroeconomic environment 4,70 4,93 1,827 0,209 Health and primary education 5,45 5,90 0,033 0,860 Higher education and training 3,95 4,38 0,022 0,885 127 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Goods market efficiency 4,33 4,35 0,396 0,545 Labor market efficiency 3,60 4,58 3,599 0,090 Financial market development 4,18 4,83 0,021 0,889 Technological readiness 3,78 4,05 0,105 0,754 Market size 5,20 2,05 15,499 0,003 Business sophistication 4,20 3,80 0,018 0,897 Innovation 3,13 3,30 0,120 0,737 Table 2: t-test Results for Cluster Membership and Global Competitiveness Subindexes Variable Basic requirements Efficiency enhancers Innovation and sophistication factors Std. Deviation Cluster Mean 1 4,38 0,246 2 4,47 0,449 1 4,06 0,161 2 3,87 0,326 1 3,39 0,214 2 3,34 0,473 t p -0,858 0,396 2,547 0,015 0,479 0,634 In order to compare Cluster 1 and Cluster 2 countries, we used t-test analysis and obtained the results, which are reported in Table 2 and Table 3. In table 2, we compared two clusters in terms of Global Competitiveness subindexes. Results in Table 2 portray that Cluster 1 (Mean= 4,06) and Cluster (Mean= 3,87) countries both had medium-level but statistically significant difference (t= 2,547; P= 0,015) in efficiency enhancers subindex. For the other two subindexes, namely basic requirements (t= 0,858; P= 0,396) and innovation and sophistication factors (t= 0,479; P= 0,634), both of the clusters showed no statistically significant results. It has to be noted that in both, basic requirements and innovation and sophistication factors, Cluster 1 and Cluster 2 countries had medium level competitiveness scores. 128 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Table 3: t-test Results for Cluster Membership and Global Competitiveness Factors Mean Std. Deviation 1 3,53 0,233 2 3,84 0,515 1 3,70 0,691 2 3,43 0,851 Macroeconomic environment 1 4,55 0,482 2 4,89 0,435 Health and primary education 1 5,73 0,228 2 5,76 0,319 Higher education and training 1 4,21 0,254 2 4,17 0,625 Goods market efficiency 1 4,00 0,239 2 4,12 0,376 1 4,04 0,325 2 4,34 0,208 Financial market development 1 4,04 0,224 2 4,07 0,504 Technological readiness 1 3,82 0,286 2 3,74 0,616 1 4,20 0,579 Variable Institutions Infrastructure Labor market efficiency Market size 129 Cluster t p -2,657 0,011 1,158 0,254 -2,406 0,021 -0,332 0,741 0,305 0,762 -1,194 0,239 -3,592 0,001 -0,255 0,800 0,597 0,554 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Business sophistication Innovation 2 2,83 0,479 1 3,75 0,313 2 3,72 0,427 1 3,45 0,131 2 2,97 0,507 8,427 0,000 0,268 0,790 0,705 0,485 Examination of Table 3 revealed mixed results for Cluster 1 and Cluster 2 countries. In Table 3, the results betray that Cluster 2 countries scored better in three of twelve Global Competitiveness factors than Cluster 1 countries. Only for market size competitiveness factor, Cluster 1 countries had statistically significant difference scores (t= 8,427; P= 0,000). 4.Discussion Analysis results at the findings section pointed out that competitiveness scores of Balkan countries, whether it belongs Cluster 1 or Cluster 2, are relatively low or medium and need to be developed. Specifically, Cluster 2 countries (Albania, Bosnia and Herzegovina, Macedonia, Montenegro, and Slovenia) should have a national strategic plan to improve their competitive position in infrastructure (quality of roads, railroads, ports, and airtransport infrastructure), higher education and training (secondary education enrollment, tertiary education enrollment, quality of the educational system, math &science education, management schools, internet access in schools, availability of research and services), goods market efficiency (intensity of local competition, extent of market dominance, effectiveness of anti-monopoly policy, extent and effect of taxation, total tax rate, number of procedures to start a business, agricultural policy cost, buyer sophistication), labor market efficiency (cooperation in labor-employer relations, flexibility of wage determination, hirin and firing practices, women in labor force), financial market development (availability of financial services, effordability of financial services, ease of access to loans, ventur capital availability), technological readiness (availability of latest technologies, firm-level technology absorption, FDI and technology transfer, internet related factors), business sophistication (local supplier quantity and quality, state of cluster development, nature of competitive advantage, control of international distribution, extent of amrketing, willingness to delegate authority), and innovation (capacity for innovation, quality of scientific research institutions, company spending on R&D, utility patents granted). Similarly, Cluster 1 countries should emphasize on development of institutions, infrastructure, financial market, and technological environment and better conditions in macroeconomic environment, higher education and training, goods market efficiency, business sophistication, and innovation. It seems from analysis results that the major advantage for these cluster is their population and market size. This picture warns us that firms plan to invest in the Balkan region should be aware of disadvantageous competitive factors in both cluster countries. It seems that eventhough both clusters have disadvantages for investors they also offer certain advantages for 130 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo them. We believe that for strategy makers in national governments and firms, these findings provide useful insights to develop their strategic plans. REFERENCES Çelebioğlu, F. (2011). Investigation of Development Indicators in the Balkan Countries for the Post-Socialist Period, Journal of Economic and Social Studies, Volume 1, Number 1, 111-122. Porter, M. E. (2004). Competitive Advantage, Free Press, New York. Porter, M. E. (2009). The Competitive Advantage of Nations, States, and Regions, Harvard Business School, Advanced Management Program. OECD, (2007). Competitive Regional Clusters: National Policy Approaches, (http://www.oecd.org/document/2/0,3746,en_2649_33735_38174082_1_1_1_1,00.html), (22.04.2012). Singh, A., (1999). Competition Policy, development and developing Countries, Indian Council for research on international economic relations, New Delhi. Vietor, R.H.K. (2006). Strategy, Structure, and Government in the Global Economy, Harvard Business School Press ,Boston, Massachusetts. World Economic Forum, The Global Competitiveness Report, (2008-2009). World Economic Forum, The Global Competitiveness Report, (2009-2010). World Economic Forum, The Global Competitiveness Report, (2010-2011). World Economic Forum, The Global Competitiveness Report, (2011-2012). 131 � Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Extent The size or duration of the resource. 1372 Title A name given to the resource Clustering Balkan Countries Based on Competitiveness Factors: A Strategic Perspective Author Author Kazim , Develioglu Abstract A summary of the resource. Prior to directing their investments, strategy makers at national and firm level need to know competitive advantages and disadvantages in a country or region. By bearing this need in mind, this study aims to examine competitive factors in Balkan countries to develop a road map for investors. To do this, we used World Economic Forum’s “Global Competitivenes Index” to analyse the case of Balkan countries as a region to cluster and compare them based on Global competitiveness factors. Analysis results pointed out that Balkan countries were clustered in two groups and scored lower or medium level on almost all competitive factors as the region. Based on these findings, authors suggested various strategic recommendations at micro and macro level. Keywords: Cluster, Competitiveness, Strategic Management, Balkan Countries Date A point or period of time associated with an event in the lifecycle of the resource 2012-05-31 Keywords Keywords. Conference or Workshop Item PeerReviewed H Social Sciences (General) https://omeka.ibu.edu.ba/files/original/557a01d95dfab9ce26616e0e07c7014a.pdf e304bdfe06c06eb41b2549572016f466 PDF Text Text An Empirical On Knowledge Sharing In Learning Organizations In Kutahya, Turkey Kemal Demirci1, Nuray Mercan1, Yaşar Aksanyar1, Bayram Alamur2, Vasfi Kahya3 1Dumlupınar University Instıtute of Social Sciences, Kütahya, Turkey, 2Balikesir University Havran Vocational School Of Higher Education, 3Dumlupinar University Instıtute of Social Sciences, Kütahya, Turkey E –mails: mkdemirci26@hotmail.com, snmmercan@yahoo.com, ayyasari@gmail.com, alamur_bayram@hotmail.com, vasfikahya@hotmail.com Abstract Comunities today and in the future have to process, evaluate and internalize the information more than past. Comunities and enterprises, which don't understand the environment, and are unconscious about changes, and which don't read the world, are obliged to deteriorate, even to die. Fiber speed and continious changes of present world, makes compulsory to learn continiously and to educe information. Enterprises have to be open to continiously learning to carry on their growth and development and they have to gain capability to share knowledge.This paper undertakes to contribute to this search by addressing some fundamental questions about the nature, domain, conceptual foundations, and practical challenges of knowledge management and organizational learning. A positive relationship has been found between continiously learning which are learner dimensions of organization, dialog and research, team learning, sharing systems, empowered workers, connection between the systems, sharing information of supportive leadership and openness of in-house cognitive canals through the correlation and multiple regression analysis done in the result of the research. Keywords: Knowledge Management, Knowledge Share, learning organization. 1.INTRODUCTION The term organizational learning may refer to individual learning within the organization, the entire organization learning as a collective body, oranywhere in between these extremes. However, most organizational learning refers to team ororganizational level learning. Of 404 �course, individual learning, or learning in small or large groupsor as an entire organization may be needed for the firm to possess the requisite knowledge totake effective action. From a knowledge management perspective, all levels of learning areimportant and all must be nurtured and made a natural part of culture. To date, most of the knowledge management emphasis has been put on locating, creating and sharing knowledge. For this reason, we consider or ganizational learning to refer to the capacity of the organization to acquire the knowledge necessary to survive and compete in its environment. (Bennet and Bennet, 2006: 1-3). Knowledge sharing in an organization is an important issue. Because knowledge is considered as being the source of organizational competitive and a kind of strategic capital in an information economy, the more the knowledge is expanded in an organization, the more the capacity of competition is (Yaghi Et Al, 2011:20). Knowledge sharing can be defined as transferring knowledge from one place or one person to another (Sharrat and Usoro, 2003:4-5). It is possible to define knowledge sharing basically as making knowledge useable for the individuals in an organization. In other words, knowledge sharing is a process of bartering knowledge with other individuals so that they can understand, claim and use it (Ipe, 2003:341); knowledge sharing is that employees share their knowledge, thoughts, suggestions and experience in their organization with others (Bartol and Srivastava, 2002:65). The first section of the paper considers conseptual analysis of knowledge sharing.In the second section, we will try to explain conceptual analysis of learning organization. In the third section, the results and the findings of the study will be evaluate, in the conclusion section, the importance of knowledge sharing in learning organizations will be evaluate by using the findings. 2.Conseptual analysis of knowledge sharing Knowledge sharing is a social mutual interactive culture and involves knowledge, skill and experience exchange of employees in an organization. For an organization, knowledge sharing is capturing knowledge based on experience, organizing it, making it reusable and transferring it; it depends on making knowledge available for others in an organization or a business. Many studies have shown that knowledge sharing is compulsory because it allows organizations to increase their innovation performance and to decrease unnecessary learning efforts (Lin, 2007:315-316). 405 �Knowledge is about knowledge exchange between two individuals. It can also be expressed as “willingness of individuals in an organization to share their knowledge with others” (Mc Neish and Mann, 2010:19-20). Sharing knowledge also allows administrators and employees keep what they know and to practice it (Yang, 2007:84). The aim of sharing knowledge is either to create new knowledge out of existing knowledge or to improve it (Christensen, 2007:37). Knowledge sharing is thought as a social behaviour and many physical, technological, psychological, cultural and personal factors have effective roles in not only supporting but also limiting knowledge sharing. Despite many advantages of knowledge sharing, researchers and implementers often argue that in many cases, in fact, individuals abstain from sharing their knowledge with others (Davenport, 2007); moreover, they say that act of sharing knowledge is unnatural and there are many reasons for people to abstain from sharing their knowledge with others. Some of what obstruct sharing knowledge between colleagues are the following factors: the relations between the source of knowledge and the receiver of the knowledge aren’t extensive, according to Smith and McKeen (2003) rewards and motivation aren’t enough for sharing, according to Ikhsan and Ronald (2004) time is insufficient, and knowledge sharing culture is lacking. Furthermore, inadequacy in understanding what to share with whom, limited appreciation of sharing knowledge and fear of acquiring false knowledge may also hinder knowledge sharing acts (Cited in Majid and Wey, 2009:22). 2.1. Conseptual analysis of learning organizations Organizational learning can be said to occur when there is a change in the content,conditionality, or degree of belief of the beliefs shared by individuals who jointly act on those beliefs within an organization knowledge can be articulated and codifiedto create organizational knowledge assets. Knowledge can be disseminated (using information technologies)in the formof documents, drawings, best practicemodels, etc.Learning processes can be designed toremedy knowledge deficienciesthrough structured, managed, scientific processes (Sanchez, 2005: 3). Organizational learning requires a sharing of language, meaning, objectives and standards that are significantly different from individual learning. When the organization learns, it generates a social synergy that creates knowledge, adding value to the firm’s knowledge workersand to its overall performance. When such a capability becomes embedded within theorganization’s culture, the organization may have what is called a core competency. These areusually unique to each organization and can rarely be replicated by other firms. The 406 �knowledge behind a core competency is built up over time through experiences and successes and rests morein the relationships and spirit among the knowledge workers that is the sum of each workers knowledge (Bennet and Bennet, 2006: 1-3). 3.Research Method and Sample The “Questionnaire of Learning Organizations’ Dimensions” which we referred to was devbeloped by Watkins and Marsick (1997). The reliability and the validity of the questionnare, learning continuum, dialog and research, learning as a team, sharing system, connections between systems, empowered employees, supporting leadership. The data were collected through a questionnaire based on literature. Surveys of Chow, Deng and Ho (2000) were utilized in evaluating the employees' knowledge sharing. There were 24 questions by Chow, Deng and Ho (2000) in the questionnaire: 5 about the perspectives of the employees about knowledge, 5 about the cases requiring knowledge sharing, 9 about the cases obstructing knowledge sharing and 5 about the elements of knowledge sharing that is the basic variable of intellectual capital. This research was conducted by questionnaire method to totally 124 people who work in different segments of Altintas District Governorship. 3.1. Demographical Characteristics of the Subjects Shows demographic features of the subjects: Age Distribution: 20-25 Yaş %14,5; 25-30 age %36,5 ;30-35 age %16,5; 35-40 age %14,5 ; 40-45 age %8,9 ; Over 45 %13,7 Marital Status Distribution Married 92 - % 74,2 ; Single 32 - %25,8 Distribution According To Position Officer 47 - %37,9 , Office Boy 2 - %1,6 ;Teacher 50 - %40,3;Policeman 2 - %1,6; Sağlıkçı 5 - % 4 ;Health Worker 18 - %14,5. Distribution Accoding To Departments Land Registry 5 %4; Education 67 - %54; Governorship 15 - %14,1 ; Health 1 - %13,7; Forestry 13 %10,5; Treasury 7 - %5,6. Working Time Distribution 1-5 Years 77 %62,1 ; 5-10 Years 25 %20,2 ; 10-15 Years 10 %8,1 ; 15-20 Years 1 %8 ; Over 20 Years 11 %8,9 Distribution Of Education Level High School 24 - %19,4 ; University 99 - %79,8 ; Masters Degree 1 %0,8 4.Research Hyphothesis The hypothesis can be said like this; H1:There is a statistically significant correlation between the participants’ (officers’) viewpoints about sub-dimension of learning organization; knowledge management, dialog 407 �and research, learning as a team, sharing systems,empowered employees, connections between systems and supporter leadership. H2:There is a statistically significant correlation between the participants’ (officers’) viewpoints about openness of the internal channel and learning organizations, dialog and research, learning as a team, sharing systems, empowered employees, connections between systems and supporter leadership. 4.1.Findings and analysis 4.1.1. Reliability of the Questionnaire In order to testthe reliability of questionnaire after analyzing the findings the Likert type data of the questionnaire, Cronbach’s Alpha value was found as 0,95. Some 28 questions which take part in the questionnaire were analysed to test reliability and Cronbach’s Alpha value of Likert type questionnaire findings was found as 0,80. 1. Analysis of correlations between sub-dimensions of sharing information and learning organizations SITUATIONS Pearson Correlatio n DIALOG TEAM SHARING ,536** ,424** ,387** ,000 ,000 ,459** ,000 EMPOWERING SYSTEM SUPPORT ,388** ,405** ,360** 000 000 000 000 ,442** ,374** ,407** ,428** ,349** ,000 ,000 ,000 ,000 ,000 REQUIRING THE SHARING INFO OPENNESS of IN-HOUSE COGNITIVE CANALS Sig. (2tailed) Pearson Correlatio n Sig. (2tailed **İlişki 0,01 düzeyinde anlamlıdır (çift yönlü) Relationship is significant at the 0,01 level. (two ways) 408 �In the result of correlation analysis, at the 0,01 significance level situations requaring the sharing info and relationship in a positive way have been observed between dialog and research team learning, sharing systems, empowered workers, connection between the systems, sharing information of supportive leadership and openness of in-house cognitive canals which are dimensions of sharing information. 2. Multiple regression analysis between learner dimensions of organization and sharing information 2 R = 30,1 ADJUSTED 2 R =25,9 F=7,150 INDEPENDENT VARIABLES P VALUE =,000  P VALUE t VALUE PARAMETER -,038 -,377 ,707 DIALOG ,458 3,523 ,001 TEAM ,044 ,333 ,739 -,030 -,244 ,808 EMPOWERING ,018 ,144 ,886 SYSTEM ,087 ,589 ,557 SUPPORT ,049 ,400 ,690 CONTINUOUSNESS SHARING . Continiously learning which are learner dimensions of organization, dialog and research, team learning, sharing systems, empowered workers, connection between the systems and sharing information of supportive leadership explains 25,9 % part of total variance of sharing info perceptions. 3. Multiple regression analysis between learner dimensions of organization and openness of inhouse cognitive canals 2 R = 27,1 ADJUSTED INDEPENDENT VARIABLES 2 R =22,1 F=6,167  P VALUE =,000 t VALUE P VALUE PARAMETER -,127 -1,233 ,220 TEAM ,284 2,140 ,034 SHARING ,211 1,564 ,120 -,043 -,342 ,733 SYSTEM ,049 ,386 ,700 SUPPORT ,176 1,165 ,246 DIALOG EMPOWERING 409 �Dialog and research which are learner dimensions of organization, team learning, sharing systems, empowered workers, connection between the systems and sharing information of supportive leadership explains 22,1 % part of total variance of openness of in-house cognitive canals of perceptions. 5. CONCLUSION Named as a knowledge era and since 1990 and onwards which are the beginning of the new era it has been observed that many academic studies on knowledge management and knowledge sharing and also it is thought that this interest will become more dense in the following years. At the end of the study, a positive relationship has been found in the correlation analysis and regression analysis between learner organization and sharing information. Knowledge management has been influential both reaching the individual aims and organizational aims and targets by catalyzing.Today, knowledge society has become an economical system with new occupational structures, new production relationships and social structures in which knowledge is produced densely. In the knowledge society, the main motivation factor which leads the individuals and entrepreneurs to produce knowledge is to desire self realization. The race to success, as a success competition, it makes feel not only in local level but also in global level. Knowledge management- in learner organizations- is to provide a common language which will reflect the organization’s own identity for reaching the aims of organizations, adopting sharing vision which is desired to be composed, and abolishing the resistance against wanting to apply to administrative approaches. (Karahan and Yılmaz,2010). REFERENCES Bennet A. and Bennet D. (2003) The partnership between organizational learning and knowledge management. In Handbook on Knowledge Management (HOSAPPLE CW, Ed), Vol. 1, pp 439–455, Springer, New York. Ipe M. (2003). ‘Knowledge Sharing On Organizations: A Conceptual Framework’, Human Resource Development Review. Thousand Oaks: Dec. Vol:2, Iss.4. Bartol M. K. And Srıvastava A. (2002) “Motivation and Barriers to Participation in Virtual Knowledge-Sharing Comminities of Practice”, Journal of Leadership and Organization Studies, Vol.9, No.1, pp.64-75. 410 �Chow C.W. Deng F. J. Ho J.L. (2000) “The Openness of Knowledge Sharing Within Organizations: A Comparative Study in The United States And The People's Republic Of China”, Journal of Management Accounting Research; Vol.12, pp.65-95. Chrıstensen H. P. (2007) “Knowledge Sharing: moving away from the obsession with best practices”, Journal of Management, Vol.11, No.1. pp.36-47. Karahan A.and Yılmaz H. (2010) “Learning Organizations and Knowledge Management” Osmangazi University Instıtute of Social Sciences Review, Nisan 2010, 5(1), s.147-174 Lin H.F. (2007) Knowledge Sharing and Firm Innovation Capability: An Emprical Study, International Journal of Manpower, Vol.28, No:3/4, pp. 315-332. Majid S. and Wey S. M. (2009) Perceptions And Knowledge Sharing Practices Of Graduate Students In Singapore, International Journal of Knowledge Management, 5(2),pp. 21-32. Mc Neısh J. and Inder J. S. M. (2010) “Knowledge Sharing and Trust in Organizations”, The 20 IUP Journal of Knowledge Management, Vol. VIII, Nos. 1 & 2, pp.18-38. Sanchez R. (2005) “ Knowledge Management and Organizational Learning: Fundamental Concepts for Theory and Practice” Lund Institute of Economic Research Working Paper Series Sharrat M. and Usoro A. (2003), ‘Understanding Knowledge-Sharing in Online Communities of Practice’, Journal of Knowledge Management, Vol: 1, Issue: 2, Dec., pp. 4-5. Yaghi B. And Alfawaer S. N. (2011) Knowledge-sharing degree among the undergraduate students: A case study at applied science private university - Middle East University for graduate studies, Amman (JORDAN) Yang J.T. (2007) The Impact of Knowledge Sharing on Organizational learning and Effectiveness, Journal of Knowledge Management, Vol.11, No:2, pp.83-90. 411 �Watkıns K. And Marsick V. (1997) Dimensions of The Learning Organization Questionnaire [survey] (Warwick, RI: Partners for the Learning Organization) Civil Law Notaries in Bosnia and Herzegovina: Actors in Preventive Justice Bakšić Šukrija1, Oruč Esad2 1University of Zenica, Faculty of Law, Zenica, Bosnia and Herzegovina, 2International Burch University, Sarajevo, Bosnia and Herzegovina E –mails: sukrijabaksic@gmail.com,eoruc@ibu.edu.ba Abstract Civil law notaries are professional lawyers and public officials appointed by the State to confer authenticity on legal deeds and contracts contained in documents drafted by them and to advise persons who call upon their services. Institution of the notary was introduced for the first time in the legal system of Bosnia and Herzegovina in 2007. Introduction of the office of notary was one of the steps taken to ensure independent and impartial judiciary and to adapt legal system with European Union law. Before its introducing there was no institution or legal profession which acted impartially on behalf of all parties to a contract or transaction. Notarial services are very wide and complex. It encompasses all judicial activities in noncontentious matters, ensure legal certainty to clients, thus averting disputes and litigation. As a guarantor of legal certainty, notary is one of the most important actors of preventive justice which include all means of reducing resort to the courts for the settlement of controversies. In this study we analyzed contribution of notary office to preventive justice in Bosnia and Herzegovina. Keywords: civil law notary, preventive justice, legal certainty, realising justice, avoiding disputes 412 � Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Extent The size or duration of the resource. 1196 Title A name given to the resource An Empirical On Knowledge Sharing In Learning Organizations In Kutahya, Turkey Author Author Kemal, Demirci Abstract A summary of the resource. Comunities today and in the future have to process, evaluate and internalize the information more than past. Comunities and enterprises, which don't understand the environment, and are unconscious about changes, and which don't read the world, are obliged to deteriorate, even to die. Fiber speed and continious changes of present world, makes compulsory to learn continiously and to educe information. Enterprises have to be open to continiously learning to carry on their growth and development and they have to gain capability to share knowledge.This paper undertakes to contribute to this search by addressing some fundamental questions about the nature, domain, conceptual foundations, and practical challenges of knowledge management and organizational learning. A positive relationship has been found between continiously learning which are learner dimensions of organization, dialog and research, team learning, sharing systems, empowered workers, connection between the systems, sharing information of supportive leadership and openness of in-house cognitive canals through the correlation and multiple regression analysis done in the result of the research. Keywords: Knowledge Management, Knowledge Share, learning organization. Date A point or period of time associated with an event in the lifecycle of the resource 2012-05-31 Keywords Keywords. Conference or Workshop Item PeerReviewed H Social Sciences (General),T Technology (General) https://omeka.ibu.edu.ba/files/original/8f6acb07f42253e9d92cdf547d2e78d4.pdf 20319426275d286f511ae95d59bac5ff PDF Text Text 3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Burdur’un Sesi Newspaper, 27 February 1960, S.1459, p.1 ANOTHER REFERENCE 1.Keleş, R.(1992), Yerinden Yönetim ve Siyaset, İstanbul: Cem Yayınları, 2.Koçak, C(2007), “Siyasi Tarih 1923-1950”, Türkiye Tarihi 4, Çağdaş Türkiye 19081980,Yayın Yönetmeni Sina Akşin, İstanbul: Cem Yayınları. 3.http://tr.wikipedia.org/wiki/1957_T%C3%BCrkiye_genel_se%C3%A7im leri 4.http://www.msxlabs.org/forum/soru-cevap/319505-yetki-merkezi-yonetim-yerel-yonetimnedir.html#ixzz1pf2hRDvL. . An Empirical Research On Relation Between Learning Organization And Visionary Leadership In Kutahya, Turkey Kemal Demirci1, Nuray Mercan1, Yaşar Aksanyar1, Bayram Alamur2, Ayşenur Altinay3 1Dumlupinar University Institute of Social Sciences, Kutahya, Turkey 2Balikesir University Havran Vocational School Of Higher Education, 3Usak University Vocational School Of Higher Education, Kutahya, Turkey E-mails: mkdemirci26@hotmail.com, snmmercan@yahoo.com, ayyasari@gmail.com, alamur_bayram@hotmail.com, aysenuraltinay@hotmail.com Abstract EINSTEIN, has seen the future dream and information power, then performed his genius by dreaming. To dream the aimed future and to focus on, to endeavour on targets, to build a ''vision'' are the powers which a leader has to have. Visionary leadership is persuading the communities and formuizing the targets. Enterprises today, can not brand , grown and carry on without having a vision. In the organizations which aim continious development and continious learning, it will be easier to carry the enterprise to the future and to show visionary leadership qualifications if they achieve to become open to changes and should be in interaction with the others and if they should be a living organization. At the end of the study, by making a multiple regression analysis, a positive relationship has been found between learning organisation dimensions; (continious learning, dialog and research, learning as a team, sharing systems, empowered employees,connection between systems and supporting 192 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo leadership) and visionary leadership dimensions(planning, visionary organizational leadership, visionary creative leadership) Keywords: Learning organizations, Vision, Visionary Leadership, Living Organism, Future. 1.INTRODUCTION Peter Senge argues that learning organizations require a new view of leadership. He sees the traditional view of leaders (as special people who set the direction, make key decisions and energize the troops as deriving from a deeply individualistic and non-systemic worldview (1990: 340). At its centre the traditional view of leadership, ‘is based on assumptions of people’s powerlessness, their lack of personal vision and inability to master the forces of change, deficits which can be remedied only by a few great leaders’ (op. cit.). Against this traditional view he sets a ‘new’ view of leadership that centres on ‘subtler and more important tasks’. In a learning organization, leaders are designers, stewards and teachers. They are responsible for building organizations were people continually expand their capabilities to understand complexity, clarify vision, and improve shared mental models – that is they are responsible for learning…. Learning organizations will remain a ‘good idea’… until people take a stand for building such organizations. Taking this stand is the first leadership act, the start of inspiring (literally ‘to breathe life into’) the vision of the learning organization. “Leader as teacher” is not about “teaching” people how to achieve their vision. It is about fostering learning, for everyone. Such leaders help people throughout the organization develop systemic understandings. Accepting this responsibility is the antidote to one of the most common downfalls of otherwise gifted teachers – losing their commitment to the truth. Learning organizations demand a new view of leadership, leader as designer (Senge 1990: 356). Culture begins with leadership, but because culture is the result of a group’s accumulated learning the culture itself will later define the wanted leadership (Schein,2004). To be a LO has no value in itself, it must always serve the broader aims of the organization (Jensen,2005). A Learning Organization has a design and a culture which takes into account the needs of the individuals in the organization (Kline and Saunders,1993) and in a LO members know why. In other organizations they know how (Jensen,2005). 2. Conceptual Analysis Of Learning Organizations According to Peter Senge (1990: 3) learning organizations are: …organizations where people continually expand their capacity to create the results they truly desire, where new and expansive patterns of thinking are nurtured, where collective aspiration is set free, and where people are continually learning to see the whole together. Organizations learn. Just like individual people, organizations sense circumstances within their environment and they respond. They observe the results of their responses and 193 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo remember the results, along with information gathered from other sources, for reference in designing future responses. This process of sensing, responding, and observing/remembering goes largely unnoticed by the individuals working within the organization due to the complexity of the "anatomy" of organizations. But consciously or not, effectively or not, all organizations are doing these activities over and over. In studying the concept of learning organizations we seek the tools and methodologies that will help an organization learn consciously and proactively in pursuit of its goals. In a learning organization, our purpose for dialogue is to let the meaning of our words permeate through the group, or, to develop fully shared, even synergistic understanding of important information, experiences, goals, etc. among all the people involved (Agarval, 1999). 3. Conceptual Analysis Of Visionary Leadership Visionary leadership refers to the capacity to create and communicate a view of a desired state of affairs that clarifies the current situation and induces commitment to an even better future. A visionary leader as one who “established goals and objectives for individual and group action, which define not what we are but rather what we seek to be or do” (Colton:1985). The visionary leader inspires, challenges, guides, and empowers. This articulated link between dreams and action, between vision and leadership, is well documented in the literature. Bennis and Nanus (1985) claimed that a compelling vision is key to effective leadership in excellent organizations. The visionary leader is not a mystical person somehow connected to intelligences or powers beyond what others know. The visionary leader is one who can clearly articulate what is and what ought to be. But the person who can only articulate a set of descriptors of what ought to be is like the person who accurately predicts rain but cannot envision the need to build an ark. The visionary leader in action has the necessary skills and knowledge to build a new reality (Brown, Anfara, 2003). 4.Method And Sampling The data were collected through a questionnaire based on literature. It was conducted totally 124 officers who work in different departments of Altıntaş District Governership. Learning Organisation Dimensions' Questionnaire was developed by AnketiWatkins ve Marsick (1997) and the reliability of the questionnaire was tested. Seven dimensions of the Learning Organisation Dimensions Questionnaire are continious learning, dialog and research, learning as a team, sharing systems, empowered employees,connection between systems and supporting leadership. (Basım ve Şeşen, 2007; Basım vd., 2007). In Visionary Leadership Questionnaire, the phd thesis of Garry Forrest (2001), named “Investigation of the Relationship Among Leaders’ Responses on Four Leadership Inventories” was used by adapting into Turkish. (Öztürk, 2009). Dimensions, planning, motive of succeeding, 194 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo leadership of organizational, risk management, utilizing the opportunities, creative leadership and motivation. 4.1. Hypotheses Of The Research 4.1.1.The hypotheses of the research are as the following; H1: There is a statistically significant relationship between the participants’ (officers) viewpoints about ''planning in visionary leadership'' and some learning organisation's dimensions; ''dialog and research'', ''learning as a team'', ''sharing systems'', ''empowered employees'','' connections between systems'' and ''supporting leadership'' . H2: There is a statistically significant relationship between the participants’ (officers) viewpoints about visionary organizational leadership'' and some learning organization’s dimensions; ''dialog and research'', ''learning as a team'', ''sharing systems'', ''empowered employees'','' connections between systems'' and ''supporting leadership'' H3: There is a statistically significant relationship between the participants’ (officers) viewpoints about visionary creative leadership'' and some learning organisation's dimensions; ''dialog and research'', ''learning as a team'', ''sharing systems'', ''empowered employees'','' connections between systems'' and ''supporting leadership'' 5. Sampling & Data Collection Tools The sampling was composed of 124 officers(sivil servants) employed at different departments of Altıntaş District Governership. 5.1. Reliability of the Questionnaire In order to test reliability of the questionnaire, a pre-study was conducted. As a result of the analysis conducted to test consistency and reliability of 43 questions about Learning oarganisation, (N of items= 43), the Likert type questionnaire data was found to have Cronbach Alpha value of 0,95, which is very close to 1.00. This showed that the questions about Learning Organisation were reliable and could be used in the research. As a result of the analysis conducted to test consistency and reliability of 28 questions about Visionary Leadership (N of items= 28), the Likert type questionnaire data was found to have Cronbach 195 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Alpha value of 0,80, which is very close to 1.00. This showed that the questions about Visionary Leadership were reliable and could be used in the research. 5.2. Demographical Characteristics of the Subjects Shows demographic features of the subjects: Age Distribution: 20-25 Yaş %14,5; 25-30 age %36,5 ;30-35 age %16,5; 35-40 age %14,5 ; 40-45 age %8,9 ; Over 45 %13,7 Marital Status Distribution Married 92 - % 74,2 ; Single 32 - %25,8 Distribution According To Position Officer 47 - %37,9 , Office Boy 2 - %1,6 ;Teacher 50 - %40,3;Policeman 2 - %1,6; Sağlıkçı 5 - % 4 ;Health Worker 18 - %14,5. Distribution Accoding To Departments Land Registry 5 %4; Education 67 - %54; Governorship 15 - %14,1 ; Health 1 - %13,7; Forestry 13 %10,5; Treasury 7 - %5,6. Working Time Distribution 1-5 Years 77 %62,1 ; 5-10 Years 25 %20,2 ; 10-15 Years 10 %8,1 ; 15-20 Years 1 %8 ; Over 20 Years 11 %8,9 Distribution Of Education Level High School 24 - %19,4 ; University 99 - %79,8 ; Masters Degree 1 %0,8 1. Multiple Regression Analysis between ''Planning In Visionary Leadership'' and ''Learning Organisation's Dimensions'' 2 R = 41 2 Adjusted R =38 F=13,558 P Value=,000 INDEPENDENT VARIABLES  Parameter t Value P Value DIALOG AND RESEARCH ,026 ,234 ,815 LEARNING AS A TEAM 1,128 1,104 ,272 SHARING SYSTEMS ,-200 -1,770 1,079 EMPOWERED EMPLOYEES ,-087 ,759 1,450 CONNECTIONS BETWEEN SYSTEMS ,500 3,713 ,000 SUPPORTING LEADERSHIP ,274 2,446 ,016 The sub-dimensions of Learning Organisation; dialog and research, learning as a team, sharing systems, empowered employees, connection between systems and supporting leadership , can explain %38 of the total variance of the viewpoints about ''Planning in Visionary Leadership''. 2. Multiple Regression Analysis between Visionary Organisational Leadership'' and ''Learning Organisation's Dimensions'' 2 R = 79,3 Adjusted F=33,037 P Value =,000  Parameter t DIALOG AND RESEARCH ,-033 ,-371 ,711 LEARNING AS A TEAM ,072 ,778 ,438 SHARING SYSTEMS ,014 ,154 ,878 INDEPENDENT VARIABLES 196 2 R =61 t Value P Value �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo EMPOWERED EMPLOYEES ,058 ,637 ,526 CONNECTIONS BETWEEN SYSTEMS ,373 3,496 ,001 SUPPORTING LEADERSHIP ,388 4,359 ,000 The sub-dimensions of Learning Organisation; dialog and research, learning as a team, sharing systems, empowered employees, connection between systems and supporting leadership , can explain %61 of the total variance of the viewpoints about '' Visionary Organisational Leadership'' 3. Multiple Regression Analysis between ''Visionary Creative Leadership'' and ''Learning Organisation's Dimensions'' 2 R = 48,6 2 Adjusted R =46 F=18,459 P Value=,000  Parameter t Value P Value DIALOG AND RESEARCH ,-010 ,-093 ,926 LEARNING AS A TEAM ,-117 1,079 ,283 SHARING SYSTEMS ,010 ,099 ,922 EMPOWERED EMPLOYEES ,019 ,177 ,860 CONNECTIONS BETWEEN SYSTEMS ,207 1,645 ,103 SUPPORTING LEADERSHIP ,433 4,135 ,000 INDEPENDENT VARIABLES The sub-dimensions of Learning Organisation; dialog and research, learning as a team, sharing systems, empowered employees, connection between systems and supporting leadership , can explain 46 % of the total variance of the viewpoints about '' Visionary Creative Leadership'' 6.CONCLUSION After making a multiple regression analysis, there is a statisticallay significant relationship between visionary leadership and learning organisation's dimensions. In the future, the most successful people and the institutions will be the one, which learn easily and fast learn. Today, they have knowledge and experience, but it wll not be enough in the future. Information and technology are developing and the only way to gain speed is to identify the learning needs and then, try to achieve effective learning. (Braham, 1998: 13-14). Developing learning structure and capacity, and to keep up with fast imrovements and innovations will be the main qualifications of the successful organisations. An executive, who has visionary and innovative approach must have a qualification in reading different developments and cases different from the others. In learning organisations which have this 197 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo qualifications, it will be easier to show vision development and visionary leadership qualifications. REFERENCES Agarwal A. (1999) “Learning Organization” www.hrfolks.com Bennis, W. And Nanus, B. (1985). Leaders: The strategies for taking charge. New York: Harper & Row. Braham, Barbara J. (1998), A Learning Organizations Creative, Rota Press, İstanbul. Brown K. M. and Anfara V. A. (2003) “Paving the Way for Change: Visionary Leadership in Action at the Middle Level” Jr. NASSP Bulletin ,Vol. 87 No. 635 June ,p. 16-34. Colton, D. L. (1985) “Vision” National Forum, 65(2), 33–35. Davenport T.H. and Prusak L. (1998) Working Knowledge. Harvard Business School Press Boston. Jensen P.E.(2005) “A Contextual Theory of Learning and the Learning Organization” Knowledge and Process Management”, Vol.12, No.1, 53-64. Kline P. And Saunders B. (1993) Ten steps to a learning organization. Library of Congress Cataloging in Publication Data 1993, ISBN: 0-915556-24-3. Oztürk E. (2009) “Visionary Leadership Characteristics Of School Administrators” Academic dissertation, Çanakkale. Senge P.M. (1990) The Fifth Discipline The Art & Practice of The Learning Organization. Currence Doubleday, 1990, ISBN: 0-385-26095-4 Schein E.H. (2004) Organizational culture and Leadership. (3rd edition) John Wiley & Sons, Inc., 2004, ISBN: 0-7879-6845-5. Schultz, J. R. (1999) “Peter Senge: Master of Change” Executive Update Online, http://www.gwsae.org/ExecutiveUpdate/1999/June_July/CoverStory2.htm 198 � Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Extent The size or duration of the resource. 1112 Title A name given to the resource An Empirical Research On Relation Between Learning Organization And Visionary Leadership In Kutahya, Turkey Author Author Kemal, Demirci Abstract A summary of the resource. EINSTEIN, has seen the future dream and information power, then performed his genius by dreaming. To dream the aimed future and to focus on, to endeavour on targets, to build a ''vision'' are the powers which a leader has to have. Visionary leadership is persuading the communities and formuizing the targets. Enterprises today, can not brand , grown and carry on without having a vision. In the organizations which aim continious development and continious learning, it will be easier to carry the enterprise to the future and to show visionary leadership qualifications if they achieve to become open to changes and should be in interaction with the others and if they should be a living organization. At the end of the study, by making a multiple regression analysis, a positive relationship has been found between learning organisation dimensions; (continious learning, dialog and research, learning as a team, sharing systems, empowered employees,connection between systems and supporting leadership) and visionary leadership dimensions(planning, visionary organizational leadership, visionary creative leadership) Keywords: Learning organizations, Vision, Visionary Leadership, Living Organism, Future. Date A point or period of time associated with an event in the lifecycle of the resource 2012-05-31 Keywords Keywords. Conference or Workshop Item PeerReviewed H Social Sciences (General) https://omeka.ibu.edu.ba/files/original/45ac167ad3c579e598ac126fc5cf5ece.pdf b03ffda85c01921a6f426eedebf232ea PDF Text Text 3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Liu, S.S., Luo, X. and Shi, Y. (2003). Market oriented organizations in an emerging economy: A study of missing links. Journal of Business Research, 56(6), 481-491. Narver, J.C. and Slater, S.F. (1990). The effects of a market orientation on business profitability. Journal of Marketing, 54(4), 20-35. Reimann, F., Ehrgott, M., Kaufmann, L. and Carter, C.R. (2012). Local stakeholders and local legitimacy: MNEs; social strategies in emerging economies. Journal of International Management, 18(1), 1-17. Robinson, W.T. (1988), Marketing mix reactions to entry. Marketing Science, 7(4), 368-85. Sa de Abreu, M.C. (2011). Effects of environmental pressures on company sustainability strategies: An interview study among Brazalian manufacturing firms. International Journal of Management, 28(3), 909-925. Sheth, J.N (2011). Impact of emerging markets on marketing: Rethinking existing perspectives and practices. Journal of Marketing, 75(4), 166-182. Slater, S.F. and Narver, J.C. (1998). Customer-led and market-oriented: Let’s not confuse the two. Strategic Management Journal, 19(10), 1001-1006. Wackernagel, M. and Rees, W. (1997). Perceptual and structural barriers to investing in natural capital: Economics from an ecological footprint perspective. Ecological Economics, 20(1), 3-24. Menu Planning With Fuzzy 0-1 Integer Programming Kenan Oğuzhan Oruç1, Ibrahim Güngör2, Sezgin Irmak2, Semih Şenol1 1Faculty of Economics and Administrative Sciences Süleyman Demirel University, Isparta, Turkey 2Alanya Faculty of Business, Akdeniz University, Antalya, Turkey E-mails: kenanoruc@sdu.edu.tr, igungor@akdeniz.edu.tr,sezgin@akdeniz.edu.tr semihh_senol@hotmail.com Abstract For the sustainability of development, effective usage of sources and the determination of their optimal usage levels are very important. Healthiness, as one of the main components of sustainable development, is under influences of many factors one of which is nutrition, and the number of people who benefit from public nutrition services are increasing every day. 6 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo The growth in the number of people necessitates that an effective menu planning must be done in order to keep the continuity of sustainable public nutrition systems. In this study, detailed plans of 20 days’ lunch menu lists are prepared for workers who are at the age of between 19 to 30 years old. Fuzzy 0-1 integer linear programming technique was used during the planning process with the consideration of data’s fuzziness. CarlssonKorhenon approach, which is offered for the situations when all parameters are fuzzy in the model configuration, is applied. Keywords: Menu Planning, Nutrition, Fuzzy, 0-1 Linear Programming. Jel Codes: C44, C61, Q01 1.INTRODUCTION Brundtland Report defines sustainable development as a development understanding which meets the needs of today’s generations without endangering the meeting ability of future generations (Çetin, 2006). Sustainable development involves environment, economy, sociodemographic and health elements (Çelik, 2006). Health, as one of the main components of sustainable development is under the influence of some factors such as nutrition, heredity, climate and environmental conditions. Among them, nutrition is the primal one (Baysal, 2009). Public food service system (PFSS) has become an important part of our lives as a result of recent changes such as technological developments, transition from agricultural society to industrial society and socio-economic and cultural changes of urban life. PFSS is defined as the system via which people, who are either at home or working outside of their home, can meet the food needs just as they wish to have it without going outside from their place. PFSS institutions are those kinds of establishments that can programme and manage nutritional needs and problems of specific groups from a single center. Places and institutions in which people usually exist publicly and eat together are hospitals, schools, universities, nursing homes, prisons, armies, hotels, offices, restaurants, institutions and factories (Atılan, 2008). In the past, it was thought that only 10 or 15 percent of the general population benefit from PFS. According to 2010 census data the rate of working people in Turkey reached up to % 50.5 (TÜİK, 2011). Therefore it can be inferred that the rate of people who benefit from PFS also increased (Atılan, 2008). The rapid growth in the world population increases the need for food and inadequacy of agricultural production rises food prices (Kaypak, 2011). If the rapid growth tendency in population, food production and consume of resources continue without any change, human being will reach development limits of the planet in the next century (Kaypak, 2011). In sustainable development the biggest target is to maximize the benefits and values of sources for society. It is necessary in terms of exhaustible sources to determine optimal usage levels of them (Çetin, 2006). With the increasing number of PFS people, an effective menu 7 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo planning should be made in PFSS in the context of providing the continuity of human health and efficient use of resources. Menu planning is a complicated process that many factors should be taken into consideration in planning such as cost, taste, variety, energy, need of nutrient etc. and mathematical models are used in the planning of the process (Şenol, 2011). It is possible to see many studies on menu planning in science literature some of which as follows: Anderson and Earle (1983), Colavita and D’orsi (1990), Soden and Fletcer (1992) Sklan and Dariel (1993) Kılınç (2007), Ediz and Yağdıran (2009), Şenol (2009), Mamat et al. (2011) and many other scholars. The data is quite important to perform an accurate mathematical modeling. However, it is not always possible to reach required exact/precise data for menu planning. Fuzzy modeling is performed on the bases of fuzzy set theory which is developed by Zadeh (1965) when a given date is inaccurate or fuzzy. In this study, sample lunch menu is planned to be served in three or four vessels as nonoptional menus for moderate activity job workers who are at the age of 19 to 30 years old. This plan is prepared for the companies that work 5 days a week and the schedule is thought monthly that means menus are for 20 days. Fuzzy 0-1 integer linear programming method is used during the planning process of the study and fuzziness were taken into account. In this model, 1280 decision variables and 752 constraints were used. Carlsson-Korhenon (1986) approach, which is offered for the situations when all parameters are fuzzy in the model configuration, is applied. GAMS 22.5 package program was used for all solutions. 2.Fuzzy Linear Programming Fuzzy linear programming models are constructed by adding the concept of fuzziness to linear programming models. These models are suggested for the solutions of problematic models that have fuzziness in their parameters and can be modeled by using linear functions. Especially it provides an opportunity to express the demands of decision maker flexibly (Bozdağ and Türe, 2007). There are many offered fuzzy linear programming models by scholars such as Zimmermann (1983), Werners (1987), Carlsson-Korhonen (1986). These models change according to their fuzziness in coefficients of the objective function, all parameters, objective function etc. or membership function of the fuzzy number. 0-1 integer linear programming model, which its’ all parameters are fuzzy and with a objective function that is based on minimization, can be expressed as follows: Objective Function: n ~ Zmin   cjx j j1 Constraints: 8 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo n ~ ~  a ij x j  b i i  1, 2,....,m x j  0 or 1 j  1, 2,....,n j1 According to fuzzy set theory each fuzzy number is a cluster. In the fuzzy sets each cluster members are included to set by taking a degree (µ) ranging from 0 to 1. If the cluster element takes a degree of 1, it is a full member of cluster, but if it takes a degree of 0, it cannot be a member of cluster (Abdel Kader and Dugdale, 2001:457). The function of membership can be defined in many ways depending on the situation of problem. The fuzzy number which has monotone increasing or decreasing membership function and upper and lower limit values are known can be defined as (Baykal, 2004):  X~ (x)     x  x L  x U  x L  ~x xU xL x Graphic 1.Monotone Increasing Membership Function  X~ (x) x L x U x  x U  x U  x L  ~x x Graphic 2.Monotone Decreasing Membership Function (x) values that are calculated according to (µ) refers to the degree of belonging to fuzzy set ( ) of (x). Carlsson-Korhonen (1986) suggest a linear model, which can be used in the cases when upper and lower limits of fuzzy numbers are known (namely , , ). The model can be applied to solve for different membership functions of the fuzzy number by valuing it from the applicable value (namely µ=1) into nonapplicable one (namely µ=0). For the application of the model, it is needed that membership functions of fuzzy numbers either should be increasing or decreasing. In the model, membership function of each fuzzy 9 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo number is composed thus it is clarified from fuzziness. According to preferred (µ) value/values of decision maker, the model is solved. 3.Menu Planning in PFSS Nutrition is the use of nutrients to protect health and provide maintenance of life. The science of nutrition deals not only with composition of nutrients (energy and nutrient quantities) but also age, gender, working conditions etc. (Baysal, 2009). In this study the nutrient amounts and quantities of required energy list in a lunch is given in table 1 as menu planning which is prepared for those who are at the age of between 19 to 30 years old and work in medium activity jobs. Table 1. Average Amounts of Energy and Some Nutrition List Except Bread in a Lunch Energy - Nutrition Elements Symbols That Are Used For Parameter In The Study Values Energy (kkal) E 750 Protein (g) P 22,7 Vitamin A (μg) A 750 Thiamin (mg) T 0,38 Vitamin C (mg) CV 34 Source: Ediz and Yağdıran, 2009, 73. Non optional menu systems, which do not give permission to choose food, are generally used in the PFSS that serve working staff. The number of the food menus in the container is limited from 3 to 4 (Ediz and Yağdıran, 2009, Beyhan and Ciğerim, 1995). A skeleton of menu is created while creating non optional menus. During this process, food groups are taken as ground and later on sample food are taken from each group. Here are the main food groups as follows (Ediz and Yağdıran, 2009, Beyhan and Ciğerim, 1995): The First Group Meals: Meat food as large and small pieces, meatballs, fish, meat and vegetable ones, dolma and sarma (Special Turkish meals), food legumes with meat. The Second Group Meals: Soups, rice dishes, pasta, pastries, olive oil dishes. The Third Group Meals: Fruits, salads, desserts and others. In this study, menu plannings are prepared for in total 64 dishes groups, 22 of which are set as the first group and 18 for second group and 24 for third group. Food names and codes given for food in the study, values of energy and nutrition of a portion of food, and costs is given in the table 2. The model is established on basis of the standards below. (Note 1)  Food Costs (Ci): The cost of each meal is determined as portion. However, the term “a portion” is relative itself when the weight of it and the amount of the content in a portion 10 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo     are considered. The amount of water, meat, onion, potato etc. in a given portion to a worker is not going to be equal with another. In addition to this, prices of materials which are used in the preparation of a meal can also be relative; moreover it is possible to meet rotten materials in the meal too. But, it is not precisely possible to take all these variables into account in the calculation of costs. Thanks to this fact, the costs of portions are fuzzied from right and left with the percentage of 5. The feasibility of lowering the cost of a meal gets lower as the cost of meal goes down. In other words, the more the price of food increases, the more the degree of membership increase. For this reason, it is assumed as that the fuzzied number has membership function that monotonically increases. The Number of Meals: In each menu, 3 or 4 kinds of food are served by choosing from first and second groups one for each and in addition to these one or two from third group. [1-2-3] Variety of the Menu: To be able to provide the menu diversity during planned time; each food should be chosen from first and third groups meals maximum 1 time and minimum 1, maximum 3 times from second group. [4-5-6] Each food served any day should be given again 5 days later at the earliest.[7] Energy and Nutrient Values: Due to the fact that the term of a portion is relative, the amount of nutrients and energy in a portion will be different. Therefore, the energy and nutritional values of a portion is fuzzied with 5 percentage from left and right. The applicability of increasement in the energy and values of nutrient of food decreases as the values of energy and nutrient increase. In other words, as a meal’s energy and nutrient value increase, so as the degree of membership decreases. For this reason, the fuzzied number is assumed to have a membership function that linearly decreasing. Energy and Nutrient Element Needs: The values in the table 1 are average values and calculated without bread. But, energy and nutrient element needs change for each staff in real life according to gender, age, physical characteristics, occupation and so on. Moreover, because of the fact that the amount of bread that is eaten by each worker is different, the nutrient element and energy amount that are taken from eaten bread will also be different. That is why the value of energy and nutrient element needs are fuzzied with 5 percentages from right and left and then used in the model as functional membership that linearly increases. [8-9-10-11] Here are some rules to be taken into consideration while creating skeleton of meal groups. These rules can be listed as follows (Ediz and Yağdıran, 2009):             Meaty vegetable meats should not be served next to the olive oil vegetable meals. [12] Dolmas (a Turkish food) should not be served next to rice. [13] Rice based meals should be preferred next to meaty legume meals. [14] With rice, pasta and pastries, dessert should be served. [15] Salad should not be served next to olive oil vegetable dishes. [16] Salad should not be served next to meaty vegetable dishes. [17] Mutter milk should not be served next to soups. [18] Dishes include potatoes should not be served together. [19] Dishes include yoghurt should not be served together. [20] Dishes include rise should not be served at the same time. [21] Dishes include carrots should not be served together. [22] Dishes include beans and squash should not be served together. [23] 11 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Establishment of the Model Decision Variables: 1 FGij   0 if i. food is served on j. day 1 SGij   0 if i. food is served on j. day 1 TGij   0 if i. food is served on j. day if i. food is not served on j. day if i. food is not served on j. day if i. food is not served on j. day i  1, 2,...,22 j  1, 2,....,20 i  1, 2,....,18 j  1, 2,....,20 i  1, 2,....,24 j  1, 2,....,20 Objective Function: 20 22 ~ 20 18 ~ 20 24 ~ Zmin    Ci * FGij    Ci * SGij   Ci * TGij j1i 1 j1i 1 j1i 1 Constrains: 22  FGij  1 j  1, 2,....,20 …...……….[1] i 1 18 1   SGij  2 j  1, 2,....,20 ……...…....[2] i 1 24  TGij  1 j  1, 2,....,20 …...………[3] i 1 20  FGij  1 i  1, 2,....,22 …...………[4] j1 20 1   SGij  3 i  1, 2,....,20 ……...…....[5] j1 20  TGij  1 i  1, 2,....,22 …...………[6] j1 n 4  TGij  1 n  1,2,....,16 j n 22 ~ i  1, 2,....,22 …...………[7] 18 ~ 24 ~ ~  Ei * FGij   Ei * SGij   Ei * TGij  7 5 0 j  1, 2,....,20 …...……….[8] 22 ~  Pi i 1 j  1, 2,....,20 …………….[9] i 1 i 1 i 1 18 ~ 24 ~ ~ * FGij   Pi * SGij   Pi * TGij  22,7 i 1 i 1 12 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo 22 ~  Ti i 1 18 ~ 24 ~ ~ * FGij   Ti * SGij   Ti * TGij  0, 3 8 i 1 i 1 22 ~ 18 ~ 24 ~ ~  CVi * FGij   CVi * SGij   CVi * TGij  3 4 i 1 i 1 i 1 22 6 i 13 i 1 20 13 i 19 i 12 22 6 18 i  21 i 1 i 14 18 4 i 12 i 1  FGij   SGij  1 j  1, 2,....,20 …………..[13]  FGij   SGij   SGij  1 j  1, 2,....,20 …………...[14]  SGij   TGij  1 8 i 1 i 5 22 8 i 13 i 5 j  1, 2,....,20 …………..[11] j  1, 2,....,20 ….……….[12]  FGij   SGij  1 6 j  1, 2,....,20 …….......…[10] j  1, 2,....,20 …………...[15]  SGij   TGij  1 j  1, 2,....,20 …………...[16]  FGij   TGij  1 j  1, 2,....,20 …………...[17] 11  SGij  TG22 j  1 j  1, 2,....,20 …………...[18] i 7 6  FGij  FG10 j  FG12 j  FG16 j  SG6 j  TG8 j  1 j  1, 2,....,20 …………..[19] FG15 j  FG20 j  SG5 j  SG8 j  TG22 j  TG23 j  1 j  1, 2,....,20 …………..[20] i 1 20 3 i 19 i 2 FG 4 j  FG 7 j  FG11j   FGij   SG ij  SG8 j  SG12 j  TG1j  1 9 FG3 j   FGij  SG6 j  1 j  1, 2,..,20 ………...…..[22] i 7 9 17 21 i 8 i 16 i  20 j  1, 2,..,20 …….[21]  FGij   FGij   FGij  SG4 j  1 j  1, 2,..,20 …………….[23] The membership function of energy need fuzzy data can be calculated as follows: E  787,5E~  712,5(1  E~ )  712,5  75E~ 13 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo The constrains, of which right side constants are fuzzy, are written below as in the form of membership function. All other fuzzy data and membership functions are given in the table 3. 22 ~ 18 ~ 24 ~  Ei * FGij   Ei * SGij   Ei * TGij  712,5  75E~ j  1, 2,....,20 ………..……[8] 22 ~  Pi i 1 18 ~ 24 ~ * FGij   Pi * SGij   Pi * TGij  21,57  2,27~P j  1, 2,....,20 …..………....[9] 22 ~  Ti i 1 18 ~ 24 ~ * FGij   Ti * SGij   Ti * TGij  0,36  0,04T~ j  1, 2,....,20 ..…………..[10] i 1 i 1 i 1 i 1 i 1 i 1 i 1 22 ~ 18 ~ 24 ~ ~  CVi * FGij   CVi * SGij   CVi * TGij  32,3  3,4C V i 1 i 1 i 1 j  1, 2,....,20 …………..[11] 4.CONCLUSIONS According to suggested model; menus that are obtained for the 3 different membership functions (0, 0,5 ve 1) are given at the table 4. Menus that are not possible to apply are shown in the column of µ=0 and certain applicable menus are shown at the µ=1. 20 days’ menu costs of each person for these membership degrees are found respectively as 27,34 TL, 28,34 TL and 32,38 TL. In other words, the costs of menus for each person can vary between 27,34 TL - 32,38 TL As it is clearly seen in this study, menu planning is such a complicated process that many different elements should be taken into consideration during the period. It is quite hard in a handmade menu planning to take all necessary conditions into consideration to obtain minimum costs. Because of this reason, making a menu planning via mathematical models not only helps to save time but also helps to eliminate possible mistakes. Additionally regarding the fuzziness of the data gains a flexibility for models. REFERENCES Abdel Kader, M.G. and Dugdale, D. (2001) Evaluating Investment in Advanced Manufacturing Technology: A Fuzzy Set Theory Approach, British Accounting Review, 33, 455-489. Anderson, A.M. and, Earle, M.D. (1983) Diet Planning in the Third World by Linear and Goal Programming, Journal of Reseach Society, 34, 9-16. Atılan, M. (2007) Adana’da Toplu Beslenme Yapılan Bazı Kurumların Menülerinin Değerlendirilmesi ve Tüketici Görüşlerinin Belirlenmesi, Master’s Thesis, Adana. Baykal, N. and BEYAN, T. (2004) Bulanık Mantık İlke ve Temelleri, Bıçaklar Publishing, No: 9, Ankara. Baysal, A. (2006) Beslenme, Hatipoğlu Publishing, 12. Ed., Ankara. 14 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Beyhan, Y., and Ciğerim, N. (1995) Toplu Beslenme Sistemlerinde Menü Yönetimi ve Denetimi, Kök Publishing, Ankara. Bozdağ, N. and Türe, H. (2007) Bulanık Doğrusal Programlama ve İMKB Üzerine Bir Uygulama, 8. Congress of Econometrics and Statistics, Malatya, Turkey. Colavita, C. and D’orsi, R. (1990) Linear Programming and Pediatric Dietetics, British Journal of Nutrition, 64, 307-317. Carlsson, C. Korhonen, P. (1986) A Parametric Approach to Fuzzy Linear Programming, Fuzzy Sets and Systems, 20, 17-30. Çetin, M. (2006) Teori ve Uygulamada Bölgesel Sürdürülebilir Kalkınma, Cumhuriyet University Journal of Economics and Administrative Sciences, 7 (1), 1-20. Çelik, Y. (2006) Sürdürülebilir Kalkınma Kavramı ve Sağlık, Hacettepe Journal of Health Administration, 9 (1), 19-37. Kaypak, Ş. (2011) Küreselleşme Sürecinde Sürdürülebilir Bir Kalkınma İçin Sürdürülebilir Bir Çevre, KMU Journal of Social and Economic Research, 13 (20), 19-33. Ediz, A. and Yağdıran, Y. (2009) Hedef Programlama Tekniği ile Menü Planlaması, Gazi Üniversitesi The Journal of Faculty of Economics and Administrative Sciences, 11 (1), 45-74. Mamat, M., Rokhayati, Y., Noor, M. M. and Mohd İ. (2011) Optimizing Human Diet Problem with Fuzzy Price Using Fuzzy Linear Programming Approach, Pakistan Journal of Nutrition, 10 (6), 594-598. Şenol, S. (2011) Menü Planlama Sorununa Karma Tamsayılı Programlama Modeli İle Çözüm Önerisi, Master’s Thesis, Isparta. Sklan, D. and Dariel, I. (1993) Diet Planning for Humans Using Mixed-Integer Linear Programming, British Journal of Nutrition, 70, 27-35. Soden, P.M. and Fletcer, L.R. (1992) Modifying Diets to Satisfy Nutritional Requirements Using Linear Programming, British Journal of Nutrition, 68, 565-572. Kılınç, E. (2007) Diyet Problemlerinin Optimizasyonu ve Bir Uygulama, Master’s Thesis, Isparta. TÜİK, (2011) Newsletter, http://www.tuik.gov.tr/PreHaberBultenleri.do?id=8570. Werners, B. (1987) An Interactive Fuzzy Programming System, Fuzzy Sets and Systems, 23, 131-147. Zadeh, L.A. (1965) Fuzzy Sets, Information and Control, 8, 338-353. Zimmermann, H.J. (1983) Fuzzy Mathematical Programming, Computers and Operations Research, 10 (4), 291-298 15 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Note 1: Numbers that are in the square brackets indicate constraint numbers in model. 16 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Table 2. The Information of the Meals Total Energy and Nutrient Values of a Portion Meals With Meat Vegetable Dishes The First Group Meals Meat Dishes Meal Kods The Name of Meals The Cost of Energy Protein Thiamin Vitamin The Meals (kcal) (gr) (mg) C (mg) (TL) Ei Pi Ti Ni FG1 Cold Cuts 339 19,6 0,1 12,4 1,227 FG2 Roast Meat 348 18,4 0,1 12,3 1,191 FG3 Boiled Veal 369,4 36,6 0,1 8,8 1,209 FG4 Kadınbudu Meatballs 417 16,2 0,2 15,2 0,985 FG5 Oven Meatballs 309 15,4 0,2 15,2 0,905 FG6 İzmir Meatballs 343 14,6 0,2 14,1 0,877 FG7 Rosted Lamb 416,6 43 0,2 0,3 1,708 FG8 Schnitzel Chicken 534,2 54,7 0,3 18,1 1,047 FG9 Grilled Chicken 337,6 47,8 0,2 18,4 0,978 FG10 Boiled Chicken 259 26,2 0,2 14,6 0,646 FG11 Chicken with soy sauce 315,1 39,9 0,1 0,1 0,795 FG12 Whitefish 489,6 52,3 0,3 33 0,916 FG13 Cabbage stew 190 10,3 0,1 65,8 0,662 FG14 Cauliflower 187 11,3 0,2 121,3 0,799 FG15 Spinach and rice with minced meat 276 15,6 0,2 77,8 0,853 FG16 Mixed vegetable pot 221 10,1 0,1 31,7 0,700 FG17 Green beans with meat 222 11,1 0,2 41,3 0,774 FG18 Stuffed Eggplant 270 9,6 0,1 22,8 0,845 FG19 Rice and minced meat stuffed bell peppers 226 11,2 0,1 84,2 0,908 FG20 Stuffed courgettes 247 11,1 0,1 21,7 0,862 FG21 Dry bean with meat 336 19,1 0,3 3,3 0,518 17 �With Olive Oil The Second Group Meals 3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo FG22 Chick peas with meat 350 17,4 0,3 2,3 0,516 SG1 Imambayıldı 194 2,1 0,1 18,3 0,293 SG2 Stuffed green peppers with olive oil 265 4,6 0,1 88,8 0,444 SG3 Stuffed grape leaves with olive oil 268 4,7 0,1 42,1 0,327 SG4 Green runner beans 177 3,5 0,1 38 0,267 SG5 Horse bean with olive oil 266 11,3 0,5 46,5 0,362 SG6 Kidney bean with olive oil 328 13,3 0,2 10 0,274 Table 2. The Information of the Meals (Cont.) Total Energy and Nutrient Values of a Portion Meals The Thir d Gro up Dess Meal erts s Pies Pilafs and Pastas Soaps Meal Kods The Cost of The Name of Meals Energy Protein Thiamin Vitamin The Meals (kcal) (gr) (mg) C (mg) (TL) Ei Pi Ti Ni SG7 Tomato 161 3,4 0,1 1,3 0,094 SG8 Yoghurt 115 3,3 0,1 0,3 0,092 SG9 Lentil 183 7,9 0,2 2,2 0,063 SG10 Noodle 115 1,8 0 0,3 0,066 SG11 Flour 184 2,8 0,1 0 0,043 SG12 Rice Pilaf 336 4,7 0,1 0 0,129 SG13 Bulgur Pilaf 291 6,5 0,2 10,5 0,068 SG14 Macaroni timbale 505 19,4 0,2 0,4 0,433 SG15 Macaroni with cheese 354 10,7 0,1 0 0,208 SG16 Rolled pastry 421 15 0,3 3,3 0,610 SG17 Water heurek 293,1 9,4 0,1 6,7 0,237 SG18 Spinach Pie 368,5 11,1 0,1 22,4 0,326 TG1 Rice Pudding 347 8,4 0,1 2,2 0,372 18 �Others Fruits Salads 3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo TG2 Syrup-soaked pastry 512,3 4,3 0 0,2 0,218 TG3 Sekerpare 482,6 5 0 0,2 0,215 TG4 Revani 367,6 4,8 0 0,2 0,208 TG5 Mixed Salad 123 1,3 0,1 28,8 0,278 TG6 Curly Salad 84 0,9 0,1 10,6 0,241 TG7 Shepherd Salad 113 1,8 0,1 52,2 0,301 TG8 Potato Salad 184,9 3,8 0,2 63,5 0,261 TG9 Apple 101 0,5 0 10 0,281 TG10 Apricot 72 0,9 0 11 0,270 TG11 Banana 153,3 1,8 0 13,8 0,529 TG12 Cherry 63 1,6 0 14 0,413 TG13 Grape 108 0,9 0,1 4 0,300 TG14 Melon 77 1,4 0,1 80 0,233 TG15 Watermelon 73 1,3 0,1 15 0,175 TG16 Orange 69 1,1 0,1 83 0,186 TG17 Mandarin 70 1 0,1 46 0,238 TG18 Pearch 83 1,1 0 39 0,247 TG19 Pears 113 0,5 0 10 0,248 TG20 Strawberry 57 1,1 0 100 0,180 TG21 Plum 59 0,7 0 9 0,188 TG22 Buttermilk 45 2,6 0 0 0,250 TG23 Yogurt 194 10,56 0,2 3 0,330 TG24 Pickle 10 0,6 0 0,7 0,480 Source: Şenol 2011, 74-125. 19 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Table 3. Fuzzy Data and Membership Functions M. Kods Energy (kcal) EL EU M.Ship Funct. Protein (gr) PL PU M.Ship Funct. Thiamin (mg) TL TU M.Ship Funct. Vitamin C (mg) CVL CVU The Cost of The Meals M.Ship Funct. CL CU M.Ship Funct. FG1 322,05 355,95 355,95-33,9µ 18,62 20,58 20,58-1,96µ 0,10 0,11 0,11-0,01µ 11,78 13,02 13,02-1,24µ 1,17 1,29 1,17+0,12µ FG2 330,60 365,40 365,4-34,8µ 17,48 19,32 19,32-1,84µ 0,10 0,11 0,11-0,01µ 11,69 12,92 12,915-1,23µ 1,13 1,25 1,13+0,12µ FG3 350,93 387,87 387,87-36,94µ 34,77 38,43 38,43-3,66µ 0,10 0,11 0,11-0,01µ 8,36 9,24 9,24-0,88µ 1,15 1,27 1,15+0,12µ FG4 396,15 437,85 437,85-41,7µ 15,39 17,01 17,01-1,62µ 0,19 0,21 0,21-0,02µ 14,44 15,96 15,96-1,52µ 0,94 1,03 0,94+0,1µ FG5 293,55 324,45 324,45-30,9µ 14,63 16,17 16,17-1,54µ 0,19 0,21 0,21-0,02µ 14,44 15,96 15,96-1,52µ 0,86 0,95 0,86+0,09µ FG6 325,85 360,15 360,15-34,3µ 13,87 15,33 15,33-1,46µ 0,19 0,21 0,21-0,02µ 13,40 14,81 14,81-1,41µ 0,83 0,92 0,83+0,09µ FG7 395,77 437,43 437,43-41,66µ 40,85 45,15 45,15-4,3µ 0,19 0,21 0,21-0,02µ 0,29 0,32 0,312-0,03µ 1,62 1,79 1,62+0,17µ FG8 507,49 560,91 560,91-53,42µ 51,97 57,44 57,44-5,47µ 0,29 0,32 0,32-0,03µ 17,20 19,01 19,06-1,81µ 0,99 1,1 0,99+0,1µ FG9 320,72 354,48 354,48-33,76µ 45,41 50,19 50,19-4,78µ 0,19 0,21 0,21-0,02µ 17,48 19,32 19,32-1,84µ 0,93 1,03 0,93+0,1µ FG10 246,05 271,95 271,95-25,9µ 24,89 27,51 27,51-2,62µ 0,19 0,21 0,21-0,02µ 13,87 15,33 15,33-1,46µ 0,61 0,68 0,61+0,06µ FG11 299,35 330,86 330,86-31,51µ 37,91 41,90 41,9-3,99µ 0,10 0,11 0,105-0,01µ 0,10 0,11 0,11-0,01µ 0,76 0,83 0,76+0,08µ FG12 465,12 514,08 514,08-48,96µ 49,69 54,92 54,92-5,23µ 0,29 0,32 0,32-0,03µ 31,35 34,65 34,65-3,3µ 0,87 0,96 0,87+0,09µ FG13 180,50 199,50 9,79 10,82 10,82-1,03µ 0,10 0,11 0,11-0,01µ 62,51 69,09 0,7 0,63+0,07µ FG14 177,65 196,35 196,35-18,7µ 10,74 11,87 11,87-1,13µ 0,19 0,21 0,21-0,02µ 115,24 127,37 127,37-12,13µ 0,76 0,84 0,76+0,08µ FG15 262,20 289,80 289,8-27,6µ 14,82 16,38 16,38-1,56µ 0,19 0,21 0,21-0,02µ 0,81+0,09µ 199,5-19µ 20 73,91 81,69 69,09-6,58µ 0,63 81,69-7,78µ 0,81 0,9 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo 9,60 10,61 10,61-1,01µ 0,10 0,11 0,11-0,01µ 30,12 33,29 33,29-3,17µ 0,67 0,74 0,67+0,07µ 233,1-22,2µ 10,55 11,66 11,66-1,11µ 0,19 0,21 0,21-0,02µ 39,24 43,37 43,37-4,13µ 0,74 0,81 0,74+0,08µ 9,12 10,08 10,08-0,96µ 0,10 0,11 0,11-0,01µ 21,66 23,94 23,94-2,28µ 0,8 0,89 0,8+0,08µ 214,70 237,30 237,3-22,6µ 10,64 11,76 11,76-1,12µ 0,10 0,11 0,11-0,01µ 79,99 88,41 88,41-8,42µ 0,86 0,95 0,86+0,09µ FG20 234,65 259,35 259,35-24,7µ 10,55 11,66 11,655-1,11µ 0,10 0,11 0,11-0,01µ 20,62 22,79 22,79-2,17µ 0,82 0,91 0,82+0,09µ FG21 319,20 352,80 352,8-33,6µ 18,15 20,06 20,06-1,91µ 0,29 0,32 0,32-0,03µ 3,14 3,47 3,47-0,33µ 0,49 0,54 0,49+0,05µ FG22 332,50 367,50 367,5-35µ 16,53 18,27 18,27-1,74µ 0,29 0,32 0,32-0,03µ 2,19 2,42 2,42-0,23µ 0,49 0,54 0,49+0,05µ SG1 184,30 203,70 203,7-19,4µ 2,00 2,21 2,205-0,21µ 0,10 0,11 0,11-0,01µ 17,39 19,22 19,22-1,83µ 0,28 0,31 0,28+0,03µ SG2 251,75 278,25 278,25-26,5µ 4,37 4,83 4,83-0,46µ 0,10 0,11 0,11-0,01µ 84,36 93,24 93,24-8,88µ 0,42 0,47 0,42+0,04µ SG3 254,60 281,40 281,4-26,8µ 4,47 4,94 4,94-0,47µ 0,10 0,11 0,11-0,01µ 40,00 44,21 44,21-4,21µ 0,31 0,34 0,31+0,03µ SG4 168,15 185,85 185,85-17,7µ 3,33 3,68 3,68-0,35µ 0,10 0,11 0,11-0,01µ 36,10 39,90 39,9-3,8µ 0,25 0,28 0,25+0,03µ SG5 252,70 279,30 279,3-26,6µ 10,74 11,87 11,87-1,13µ 0,48 0,53 0,53-0,05µ 44,18 48,83 48,83-4,65µ 0,34 0,38 0,34+0,04µ SG6 311,60 344,40 344,4-32,8µ 12,64 13,97 13,97-1,33µ 0,19 0,21 0,21-0,02µ 9,50 10,50 10,5-1µ 0,26 0,29 0,26+0,03µ SG7 152,95 169,05 169,05-16,1µ 3,23 3,57 3,57-0,34µ 0,10 0,11 0,11-0,01µ 1,24 1,37 1,37-0,13µ 0,09 0,1 0,09+0,01µ SG8 109,25 120,75 120,75-11,5µ 3,14 3,47 3,47-0,33µ 0,10 0,11 0,11-0,01µ 0,29 0,32 0,32-0,03µ 0,09 0,1 0,09+0,01µ SG9 173,85 192,15 192,15-18,3µ 7,51 8,30 8,3-0,79µ 0,19 0,21 0,21-0,02µ 2,09 2,31 2,31-0,22µ 0,06 0,07 0,06+0,01µ SG10 109,25 120,75 120,75-11,5µ 1,71 1,89 1,89-0,18µ 0 0 0 0,29 0,32 0,32-0,03µ 0,06 0,07 0,06+0,01µ FG16 209,95 232,05 FG17 210,90 233,10 FG18 256,50 283,50 FG19 232,05-22,1µ 283,5-27µ 21 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Table 3. Fuzzy Data and Membership Functions (Cont.) M. Kods Energy (kcal) EL EU M.Ship Funct. Protein (gr) PL PU M.Ship Funct. Thiamin (mg) TL TU Vitamin C (mg) M.Ship Funct. CVL CVU The Cost of The Meals M.Ship Funct. CL CU M.Ship Funct. SG11 174,80 193,20 193,2-18,4µ 2,66 2,94 2,94-0,28µ 0,10 0,11 0,11-0,01µ 0 0 0 0,04 0,05 0,04+0µ SG12 319,20 352,80 352,8-33,6µ 4,47 4,94 4,94-0,47µ 0,10 0,11 0,11-0,01µ 0 0 0 0,12 0,14 0,12+0,01µ SG13 276,45 305,55 305,55-29,1µ 6,18 6,83 6,83-0,65µ 0,19 0,21 0,21-0,02µ 9,98 11,03 11,03-1,05µ 0,06 0,07 0,06+0,01µ SG14 479,75 530,25 530,25-50,5µ 18,43 20,37 20,37-1,94µ 0,19 0,21 0,21-0,02µ 0,38 0,42 0,42-0,04µ 0,41 0,45 0,41+0,04µ SG15 336,30 371,70 371,7-35,4µ 10,17 11,24 11,24-1,07µ 0,10 0,11 0,11-0,01µ 0 0 SG16 399,95 442,05 442,05-42,1µ 14,25 15,75 15,75-1,5µ 0,29 0,32 0,32-0,03µ 3,14 SG17 278,45 307,76 307,76-29,31µ 9,87 9,87-0,94µ 0,10 0,11 0,11-0,01µ SG18 350,08 386,93 386,93-36,85µ 10,55 11,66 11,66-1,11µ 0,10 0,11 TG1 329,65 364,35 364,35-34,7µ 7,98 8,82 8,82-0,84µ 0,10 TG2 486,69 537,92 537,92-51,23µ 4,09 4,52 4,52-0,43µ TG3 458,47 506,73 506,73-48,26µ 4,75 5,25 TG4 349,22 385,98 385,98-36,76µ 4,56 TG5 116,85 129,15 129,15-12,3µ 88,20 107,35 118,65 TG6 TG7 79,80 0,2 0,22 0,2+0,02µ 3,47 3,47-0,33µ 0,58 0,64 0,58+0,06µ 6,37 7,04 7,04-0,67µ 0,23 0,25 0,23+0,02µ 0,11-0,01µ 21,28 23,52 23,52-2,24µ 0,31 0,34 0,31+0,03µ 0,11 0,11-0,01µ 2,09 2,31 2,31-0,22µ 0,35 0,39 0,35+0,04µ 0 0 0 0,19 0,21 0,21-0,02µ 0,21 0,23 0,21+0,02µ 5,25-0,5µ 0 0 0 0,19 0,21 0,21-0,02µ 0,2 0,23 0,2+0,02µ 5,04 5,04-0,48µ 0 0 0 0,19 0,21 0,21-0,02µ 0,2 0,22 0,2+0,02µ 1,24 1,37 1,365-0,13µ 0,10 0,11 0,11-0,01µ 27,36 30,24 30,24-2,88µ 0,26 0,29 0,26+0,03µ 88,2-8,4µ 0,86 0,95 0,95-0,09µ 0,10 0,11 0,11-0,01µ 10,07 11,13 11,13-1,06µ 0,23 0,25 0,23+0,02µ 118,65-11,3µ 1,71 1,89 1,89-0,18µ 0,10 0,11 0,11-0,01µ 49,59 54,81 54,81-5,22µ 0,29 0,32 0,29+0,03µ 8,93 22 0 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo TG8 3,61 3,99 3,99-0,38µ 0,19 0,21 0,21-0,02µ 60,33 66,68 66,68-6,35µ 0,25 0,27 0,25+0,03µ 106,05-10,1µ 0,48 0,53 0,53-0,05µ 0 0 0 9,5 10,5 10,5-1µ 0,27 0,3 0,27+0,03µ 75,6-7,2µ 0,86 0,95 0,95-0,09µ 0 0 0 10,45 11,55 11,55-1,1µ 0,26 0,28 0,26+0,03µ 145,64 160,97 160,97-15,33µ 1,71 1,89 1,89-0,18µ 0 0 0 13,11 14,49 175,66 194,15 194,15-18,49µ TG9 95,95 106,05 TG10 68,40 TG11 TG12 TG13 75,60 14,49-1,38µ 0,5 0,56 0,5+0,05µ 66,15 66,15-6,3µ 1,52 1,68 1,68-0,16µ 0 0 0 13,3 14,7 14,7-1,4µ 0,39 0,43 0,39+0,04µ 102,60 113,40 113,4-10,8µ 0,86 0,95 0,95-0,09µ 0,10 0,11 0,11-0,01µ 3,8 4,2 4,2-0,4µ 0,29 0,32 0,29+0,03µ 59,85 TG14 73,15 80,85 80,85-7,7µ 1,33 1,47 1,47-0,14µ 0,095 0,105 0,105-0,01µ 76 84 84-8µ 0,22 0,24 0,22+0,02µ TG15 69,35 76,65 76,65-7,3µ 1,24 1,37 1,37-0,13µ 0,095 0,105 0,105-0,01µ 14,25 15,75 15,75-1,5µ 0,17 0,18 0,17+0,02µ TG16 65,55 72,45 72,45-6,9µ 1,05 1,16 1,16-0,11µ 0,095 0,105 0,105-0,01µ 78,85 87,15 87,15-8,3µ 0,18 0,2 0,18+0,02µ TG17 66,50 73,50 73,5-7µ 0,95 1,05 1,05-0,1µ 0,095 0,105 0,105-0,01µ 43,7 48,3 48,3-4,6µ 0,23 0,25 0,23+0,02µ TG18 78,85 87,15 87,15-8,3µ 1,05 1,16 1,16-0,11µ 0 0 0-0µ 37,05 40,95 40,95-3,9µ 0,23 0,26 0,23+0,02µ 107,35 118,65 118,65-11,3µ 0,48 0,53 0,53-0,05µ 0 0 0-0µ 9,5 10,5 10,5-1µ 0,24 0,26 0,24+0,02µ 105-10µ 0,17 0,19 0,17+0,02µ TG19 TG20 54,15 59,85 59,85-5,7µ 1,05 1,16 1,16-0,11µ 0 0 0-0µ 95 105 TG21 56,05 61,95 61,95-5,9µ 0,67 0,74 0,74-0,07µ 0 0 0-0µ 8,55 9,45 TG22 42,75 47,25 47,25-4,5µ 2,47 2,73 2,73-0,26µ 0 0 0-0µ 0 203,7-19,4µ 10,03 11,09 11,09-1,06µ 0,19 0,21 0,21-0,02µ 0,63-0,06µ 0 0 0-0µ TG23 TG24 184,30 203,70 9,50 10,50 10,5-1µ 0,57 0,63 23 0,2 0,18+0,02µ 0 0 0,24 0,26 0,24+0,03µ 2,85 3,15 3,15-0,3µ 0,31 0,35 0,31+0,03µ 0,67 0,74 9,45-0,9µ 0,18 0,74-0,07µ 0,46 0,5 0,46+0,05µ �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Table 4. Menus According to Membership Degrees Day µ=0 µ=0.5 µ=1 Yoghurt Soap Lentil Soap Roast Meat Cauliflower Mixed vegetable pot Horse bean with olive oil Macaroni with cheese Bulgur Pilaf Sekerpare Apricot Plum Lentil Soap Flour Soap Yoghurt Soap Rosted Lamb Stuffed courgettes Stuffed Eggplant Shepherd Salad Macaroni with cheese Rolled pastry Melon Cherry Flour Soap Noodle Soap Rosted Lamb Boiled Chicken Cabbage stew Bulgur Pilaf Stuffed grape leaves with olive oil Rolled pastry Potato Salad Grape Apricot Roast Meat Schnitzel Chicken Lentil Soap Horse bean with olive oil Stuffed green peppers with olive oil Dry bean with meat Revani Grape Rice Pilaf 1 2 3 4 Strawberry 5 Soya Soslu Tavuk (Rice Pilaf G.) Yoghurt Soap Soya Soslu Tavuk (Rice Pilaf G.) Kidney bean with olive oil Stuffed Eggplant Kidney bean with olive oil Melon Spinach Pie Tomato Soap Mandarin Orange Grilled Chicken Kadınbudu Meatballs Rice Pilaf Stuffed grape leaves with olive oil Yoghurt Soap 6 Mixed vegetable pot 24 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Macaroni timbale Potato Salad Apple Flour Soap Melon Lentil Soap Flour Soap İzmir Meatballs Cabbage stew Cauliflower Green runner beans Rice Pilaf Water heurek Noodle Soap Plum Pears Yogurt Flour Soap Lentil Soap Yoghurt Soap Green runner beans İmambayıldı Boiled Veal Oven Meatballs Roast Meat Bulgur Pilaf Sekerpare Watermelon Mixed Salad Dry bean with meat Noodle Soap Lentil Soap Bulgur Pilaf Stuffed grape leaves with olive oil Cabbage stew Sekerpare Boiled Chicken Rolled pastry Yoğurt Plum Whitefish Kadınbudu Meatballs Tomato Soap Spinach Pie Maccaroni with cheese Spinach and rice with minced meat Buttermilk Mixed Salad Spinach Pie 7 8 9 10 Watermelon Table 4. Menus According to Membership Degrees (Cont.) İzmir Meatballs Boiled Veal Flour Soap Water heurek Horse bean with olive oil İmambayıldı Curly Salad Revani Grilled Chicken 11 Rice Pudding 12 25 Noodle Soap Soya Soslu Tavuk (Rice Pilaf G.) Noodle Soap Schnitzel Chicken Kidney bean with olive oil Cauliflower �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Potato Salad 13 Bulgur Pilaf Macaroni timbale Orange Grape Stuffed courgettes Lentil Soap Schnitzel Chicken Rolled pastry Spinach and rice with minced meat Stuffed green peppers with olive oil Mandarin Sekerpare Mandarin Kadınbudu Meatballs Flour Soap Cold cuts Bulgur Pilaf Cold cuts Horse bean with olive oil Mixed Salad Spinach Pie Revani 14 Curly Salad Lentil Soap Whitefish Tomato Soap Green beans with meat Rice Pilaf Chick peas with meat Syrup-soaked pastry Apple Rice Pilaf 15 Pearch 16 Spinach and rice with minced meat Tomato Soap Lentil Soap Macaroni timbale Stuffed grape leaves with olive oil Oven Meatballs Pearch İzmir Meatballs Water heurek Syrup-soaked pastry Shepherd Salad Cold cuts Green beans with meat Flour Soap İmambayıldı Macaroni timbale Mixed vegetable pot Yoğurt Buttermilk Rolled pastry 17 Apple 18 26 Flour Soap Noodle Soap Boiled Chicken Stuffed green peppers with olive oil Dry bean with meat Green runner beans �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Boiled Veal Bulgur Pilaf Bulgur Pilaf Orange Strawberry Pears Grilled Chicken Oven Meatballs Yoghurt Soap Bulgur Pilaf Green runner beans Green beans with meat Pears Rice Pudding Macaroni timbale 19 Apricot Tomato Soap Chick peas with meat Whitefish Stuffed Eggplant Rice Pilaf Maccaroni with cheese Maccaroni with cheese Pearch Curly Salad 20 Watermelon Awareness-rasing Sustainable Business and Corporate Social Responsibility Among Small and Medium-sized Enterprises Huseyin Onlem Ersoz1,Ramazan Kilic2 1Adnan Menderes University, Karacasu Memnune Inci Vocational School, Aydin, Turkey 2Adnan Menderes University, Faculty of Economics and Administrative Sciences, Aydin, Turkey E-mails: hoersoz@adu.edu.tr, rkilic@adu.edu.tr Abstract Sustainable business and corporate social responsibility (CSR) activities have been raising in Turkey since 2001 Economic Crisis. Corporate social responsibility (CSR), is an approach developed with the concept of sustainable development. CSR is a kind of self-regulation management and organization model. CSR means that a company's business model should be socially responsible and environmentally sustainable. It refers to responsible corporate action beyond legal requirements; CSR manifests itself throughout the value chain, in a company’s treatment of its employees and in its dealings with the relevant stakeholders. Especially, most large-sized companies in Turkey at least have played some roles on sustainable development with their projects, activities or reports. Most of them have relations with the world business environment. That’s why they could find chance and had to use sustainable strategic management methods to prevent their stakeholders. But many small and 27 � Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Extent The size or duration of the resource. 1095 Title A name given to the resource Menu Planning With Fuzzy 0-1 Integer Programming Author Author Kenan, Oğuzhan Oruç Ibrahim, Güngör Sezgin, Irmak Semih, Şenol Abstract A summary of the resource. For the sustainability of development, effective usage of sources and the determination of their optimal usage levels are very important. Healthiness, as one of the main components of sustainable development, is under influences of many factors one of which is nutrition, and the number of people who benefit from public nutrition services are increasing every day.The growth in the number of people necessitates that an effective menu planning must be done in order to keep the continuity of sustainable public nutrition systems. In this study, detailed plans of 20 days’ lunch menu lists are prepared for workers who are at the age of between 19 to 30 years old. Fuzzy 0-1 integer linear programming technique was used during the planning process with the consideration of data’s fuzziness. Carlsson- Korhenon approach, which is offered for the situations when all parameters are fuzzy in the model configuration, is applied. Keywords: Menu Planning, Nutrition, Fuzzy, 0-1 Linear Programming. Date A point or period of time associated with an event in the lifecycle of the resource 2012-05-31 Keywords Keywords. Conference or Workshop Item PeerReviewed H Social Sciences (General),HB Economic Theory,HF Commerce https://omeka.ibu.edu.ba/files/original/d69e7fef77014540d59a832dc2fc1424.pdf 8ba2147f93420fa7b9e2747d4569c4e8 PDF Text Text 3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Report on : Students expenditure and the economic recession Kerim Hadziabdic Inetnatinal Burch University Sarajevo, Bosnia and Herzegovina Abstract All subjects were selected from International Islamic University Malaysia (IIUM), data was collected using questionnaire which is attached to the research paper. There are two types of data which is local student’s data and foreign student’s data. The findings from research are representing that foreign students as well as local students are affected by current economic recession. 1.INTRODUCTION THE ECONOMIC recession which had taken place on 2008-2009 had a global implication all around the world. Like any other economic recessions before, it had been triggered by a widespread contraction succeeding an economic bubble. As for the case of 2008-2009, the bubble that blew before the event was the increasing number of subprime mortgages and lending of individuals due to low interests. It had mainly originated from the United States, and when it had a dire critical economic meltdown, no other country in the world could escape the repercussions. Contingency ripple took place and country as far as Malaysia too were well effected by the economic recession. An economic recession is a phenomena of which it effects almost any if not every aspect of individuals who rely on money and the common market. Thus students, like any other individuals are part of this economic equation and are subject to impact to any economic circumstances. Malaysia has over 900,000 students currently enrolled in public and private higher education institutions (Ministry of Higher Education, 2009). This number includes foreign students who had become part and element of the Malaysian financial market. Both students wether local or foreign are well included in economic activities, either by saving money in banks, selling or buying things, or spending on services provided by the higher institutions. The great number of university students thus cannot be easily overlooked in the implications of the economic recession. The economic crisis has had an impact on their family’s finances and many have felt an effect on their own financial lives. The crisis also ultimately affected students’ confidence, behavior, trust in financial institutions and overall well-being. 2.SELECTION OF PROBLEM The economic recession may had happened almost two years ago, but no one could deny the economic repercussions still lingers today. Students engaged in many economic activities on a daily basis. Many like foreign students that recite at IIUM deal with money transactions that involve external as well local curacies. The global economic crisis 20082010 influences these transactions in many ways. Whilst Malaysians claim they were not badly affected by global economic crisis which originates mainly in US and Europe none the less on a longer time scale it would have altered the economic behavior of locals. The issue 299 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo here is how economic recession indirectly have an impact on the students pockets beat it the local students or foreign students of IIUM. Both types of students leave in a quiet similar and controlled environment and even consume common life style. What should differ on these students is their source of savings or money gotten of their parents whom would be more directly affected by the economic recession. Thus, the study aims to look into aspects of expenditure on a much localised scenario. It would be interesting to note how this particular phenomenon would have direct or indirect implication on student’s expenditure of various backgrounds. 2.1.Objective of study This paper tries to examine the two main components which are the type of student of IIUM and the level of changes on their expenditure habits after the global economic recession. These students which are between ages 18 to 30 are at their beginning of financial maturity and independence. Many of which decide their own economic decisions. However they still rely on a given source of income weather it is from their parents, scholarships or any other forms of financial assistance. By identifying the students of these various backgrounds it is possible for us to analyse the indirect impact of current economic recession on the students, furthermore they are also the factor of foreign and local students of IIUM who may act differently on the level of economic implications they meet with. This study also hopes to identify whether the economic behavior of students on the account of their expenditure are influenced by where they are from (foreign or local) in the event of an economic recession. The findings of this research would help in many ways of decision making of related authorities such as the university, or even at a personal financial management on a similar economic circumstances. 2.2.Research Questions Are student’s spending habits effected by an economic recession? Which type of student is more responsive towards the change in an economic recession, the locals or foreign? 3.Literature Review Malaysia had its bumpy road in facing the uncertainties of modern global economy. Global economic crisis of recession, inflation, bubble burst, and oil crisis are the examples of problem faced by Malaysia and without exception, most countries in the world. The global economic recession of 2008-2009 like any others before had effected considerably on all aspects of life of the public. What had actually caught my attention is that how many had undermined the role of young adults , typically students had anything at all to do with such global economic crisis. Many argue that students; whom may still heavily rely on parents or government in funding them, are best unaffected by the greater economic phenomenon. Students simply don't care, since they do not work or earn for money and oblivious to the hardship of their parents. Of course, these are generalization and a well attained myth of ungrateful sons and daughters who would plunder the money of their parents if not the tax payers money. The truth is that students are at a great stage of transitional period to financial 300 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo and psychological maturity. They’ve begin to deal problems and issues identical to those faced by working adults and had become a more responsible individual part of a greater society. Issues of monetary crisis that may had happened on a greater international and state level definitely trickles down to the general public and to students with no exception. Malaysia has over 900,000 students currently enrolled to both private and public universities (a good 3% of the population of Malaysia as of 2010) and a number that much could not escape any of the country’s economic equation. The well known by-product of any economic recession is the increasing number of unemployment as well as prices of goods, followed by the mass withdrawal from ordinary expenditure. Replicating this at the very micro level, a student has as much to worry about it than any other regular working adult. The scarcity of jobs offered by the market and the constant struggle of fresh graduates with existing unemployed workforce had become a nightmare of students who yearns for a sense of approval from the society and parents. While enrollment was related directly to salaries and employment opportunities for college graduates, it was related inversely to wage and employment opportunities for non college graduates (Freeman 1975; Handa and Skolnik 1975; and Mattila 1982). Whilst many studies focuses on the financial troubles faced by students who intends to pursue their studies ( i.e unaffordable tuition fees ) and prospect of working after graduating, little attention had been laid on financial difficulties faced when they are still enroled to a particular university. A review of university choice studies examined the differences changes in student responses to five key components of university cost: tuition, room and board(hostels), travel, cost of foregone earnings, and financial aid ( Leslie and Brinkman 1987, pp 195-197 ). These variables are the focus of a students financial planning, and come what may an economic recession or financial abysmal that may had befallen them, this issues still holds a primary importance. However, on an account of an economic recession may well effect how students handle their money, on a fixed cost ( i.e. books, transport, food ) or for leisures. An assessment done by National Institute of Endowment for Financial education on how a recession impact cripples student’s finances, they've concluded that 93% of students have felt and effect on their own financial lives. The crisis also ultimately affected student’s confidence, behaviour, trust in financial institutions and overall well being. This data stems from the landmark study Arizona Pathways to Life Success in University Students (APLUS), funded by the National Endowment for Financial Education. At the height of the economic crisis (February 2009 to April 2009), researchers at the University of Arizona completed Wave 1.5 of a longitudinal study of how young adults develop financial attitudes and behaviors.* In addition to that, this study stresses on how the economic recession of 2008-2009 would have direct or indirect implication on student’s expenditure and it would have to be addressing to students enrolled to local Malaysian university. IIUM fits in this category and boasts to have almost 13,000 students studying there. By implicating the idea of students difficulties in their finances (which is more or less covered under their expenditure behaviour) I hope to define not just how a student’s pocket can relate to an economic 301 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo recession but also on what are the common patterns that drive a typical students expenditure on day to day basis. By principle, a student will face either shortage of financial funds from their parents (as parents too had to reshuffle their expenditure planning due to an economic recession ) or the scarcity of financial aids from the government ( loans, endowments PTPTNs). This will deliberately alter their lifestyle and expenditure (ie, going less outings, and reside on cheap hostel food). I have also noticed that under the administration of Prime Minister Najib Tun Razak, much emphasis had been focused on students financial aids and welfare. According to his speech in introducing the supplementary supply (2009) bill, he mentions that the Government is willing to provide various subsidies, incentives and assistance for fuel consumption, food security, scholarships and educational assistance as well as social welfare programs. The allocation for subsidies and other assistance in 2008 totaled RM 34.1 billion or 22% of total operating expenditure and RM6 billion is accounted for helping students (Najib, 2009; Supplementary Supply (2009) Bill ). Finally, the study sets to look at the differences of expenditure behaviour that exists between local and foreign students on similar economic condition. IIUM is well known to have a great number of foreign students which stays for the average period of 3 - 5 years to complete a course. Given the long tenure of these students in Malaysia, foreign students may endure similar economic activities just as the locals do. However, there are many more variables that may influence their behaviour of spending, for which this study is trying to analyse. In comparison to the local students, a foreign student is expected to be more responsive to an economic recession. Thus , I am determined to have a full study of behaviour of the students expenditure on a given economic climate, namely due to the economic recession of 2008-2009 and discuss how local and foreign students cope up with it. THEORETICAL FRAMEWORK - Change in currency 40,00 rates 10,0 Decrease of Economic0 expenditure Crisis 20, 00 Increase price of goods Parents 200 decrease 2 expenditure 50,0 on 20, 0 00 Increase in savings Foreign students Local students DI TXV 10 ,0 Decrease in Spending less time on outings 0 students income 0, 00 DI TİV Reluctance of using credit services (EXPENDITUR E) Area of interest This study’s theoretical framework is inspired in replicating the research done by Fig. 1.1 - A model of Students expenditure and the economic University of Arizona completing a longitudinal study of how young adults develop financial recession 302 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo attitudes and behaviors. However, due to limited sources and time frame given this study seeks to carry a vertical cross-section of a given number of students of IIUM in a single survey. It is, perhaps, easier to understand the nature and function of a theoretical framework if it is viewed as the answer to two basic questions: 1. What is the problem?  Why is this study’s approach a feasible solution? Starting with the sample, it should be noted that this study is conducted on a short semester period, and that the number of students enrolled for the semester is considerably much lower than of a normal long semester. Nonetheless, this study aims to produce a set of sample of students replicable to the population of students in IIUM. This study assumes that there shall be no expenditure differences between male and female students on a given economic circumstances, although these numbers should be well noted. In addition to that, variables such as student’s ethnicity, home of residence, and their GPAs are well accounted for analyzation. The data collection method preferred in the study is survey questionnaire, which respondents can complete in less then 10 minutes about their family financial environment, attitudes and behaviour on the recent economic crisis and etc. Figure 1.1 demonstrates how variables should relate to one another, forming the proposed research question; Would students spend less following an economic crisis, if so are foreign students more responsive in such circumstances? Taking the economic crisis of 2008-2009 as a starting point, it led to several foreseeable effect, namely decrease of expenditure in the public and the increase price of goods. As stated earlier, the sample is taken from within the student population of IIUM; the university is thus a controlled environment for which all students reside. All students are assumed to be financially dependent on their parents, at least on the term of student’s monthly additional income. Decrease in students income should indicate the following effect of parents decreasing the amount spent on their university enrolled child. I am therefore to generalize the spending habits of students narrowing them down to increase of savings, spending less time on outings, and or reluctance of using credit services all of which signifies student’s expenditure. Note 303 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo also there is an added variable to foreign students, for which involves in transnational financial transaction and should be responsive to the change of currency rates of their country to Malaysia. Data collection A questionnaire survey of 30 respondents had been carried out through a non probability sampling method. They had been approached at random, however, the research is conducted under quota sampling method. In order to match the real representation of the student population of IIUM, the study restricts the number of foreign students respondents to 7 out of 30 bringing the percentage to 23,3 % of the whole sample. This number reflect closely to the ratio of foreign student to local students in IIUM. Sample questionnaire is attached to Appendix. 304 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Data analysis Data analysis of respondents identity Figure 1.5 Student’s monthly income We can see that in figure 1.5, most students receive in an average monthly income of Rm300 to Rm600. However, for the top range of students monthly income of Rm700 and above is received by all foreign students. This importantly signifies that foreign students needs more money than local students. Figure 1.6 Occupation of Guardian It is important to also note the minimal background of students by knowing how their guardian is employed and financing them. 12 students out of 30 answered that their guardians are in private sector followed by 11 whom are self employed. This data signifies that average of students comes from a middle income families. Figure 1.7 Campus status As shown on figure 1.7, we can see that it correlates with figure 1.5, of students monthly income. The major 83% of students whom stays in-campus has the average income of 300-600rm. Many can rely on this minimal income because lifestyle is considerably cheaper by living in campus. Accommodation 305 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Gathering the data, we could see on figure 1.2, that the respondents consists of more female than male. As stated earlier, there shall be no distinctions between the expenditure habit of male or female. In figure 1.3, the ratio is maintained to reflect the original student population of IIUM. The significance of year of intake tell us that there are more senior students compared to juniors. The fact is senior students would have much more financial awareness and experiences in managing their monthly income. Figure 1.2 Gender 306 Figure 1.3 Nationality Figure 1.4 Year of intake �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Figure 1.8 Social outings However, it is interesting to note that despite most having only up to 600rm to spend on monthly basis, still there are many students (11 of them ) who enjoys a good social outings for 2 to 4 times a week. It just shows that , come what may, economic recession or not, socializing is still the main primary importance in a student’s life. Figure 1.9 Use of credit/debit card Most of the students does not use or own a credit or debit card. This number represents the number of local students whom rely mostly on cash based transactions whilst the rest (mostly foreign students) are dealing with credit/debit cards. Figure 2.0 Savings A lot of students find it unimportant to save if they have just enough cash for the monthly expenditure. However, there are students who would reserve not more than 25% of their monthly income for savings. If you could relate to figure 2.3 of students interest in part time employment, it goes to say that many are interested to have a minimal saving whilst increasing their monthly income. 307 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Figure 2.1 Students perception towards price of goods Figure 2.1 explains that the economic recession has a greater and prolonged effect. Up to 93% of students believe that prices of goods are getting much expensive than before. Figure 2.2 Financial Assistance According to the result of the survey, up to 60% of the sample still greatly rely on their parents for financial assistance and difficulties. They feel more comfortable by gaining monetary help from their parents because it has been their primary source of income anyway. This data is followed by an equal distribution of respondents who would rely on friends or simply wait till difficulties is overcome. Figure 2.3 Part time employment As said earlier, figure 2.3 in students perception of having a part time job whilst studying shows the correlation of student’s need for extra money to cope up with the rise of price of goods. More over, many students also believe that they are mature enough to offer services to the job market, a determination to be part of the bigger society. 80 % of the students also consider that part time job would not effect their studies immensely. 308 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo The questionnaire also provides an open ended question, enquiring on how students feel the economic recession of 2008-2009 had effect on them. This question tries to sum up the overall feeling and perception towards such economic scenario where prices goes up and how it influences their monthly expenditure. Up to 70% of respondents have the impression that the economic recession has little or no impact on their lifestyle or expenditure. This leaves us the rest 30 % (which represents 9 respondents ) all of whom said that they are uneasy with the increase price of goods and thought that they ought to get more money to handle difficult situations up. 6 out of this 9 respondents are foreign students and 3 are locals. This shows that foreign students are more conscious to the changes brought by an economic recession and is determine to deal with it. The survey continues by providing questions specifically asked on foreign students. These questions are designed to ask about activities done by foreign students in handling their finances. Foreign students response 309 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Students’s response : “ Prices are much expensive and more pocket money is needed to maintain such current lifestyle.” Effected by economic recession “ Things are getting pricier than usual, there is less to spend. “ Figure 2.4 Frequency in dealing with international banking transaction Almost all of foreign students deal with international banking transaction. 43% of who deals on a monthly basis. International students whom still rely on their parents who are back home get their money through international banking. International banking is one of the sector most vunerable to economic crisis. Following this question, many students said that at the recent currency exchange, they had less to spend. 310 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Discussion Consistent with the research question proposed, the questionnaire conducted had intended to get as much data to prove of disapprove the argument. Reviewing back to the question, are students spending habits effected by an economic recession, it is undeniable that students do admit changes. Although, infinite factors does influence in giving such result, I am certain that economic crisis plays a big and important role in effecting the financial lives of not just working adults but students as well. There is indeed a link between students financial management and the bigger economic picture, and we can well discard the myth that students are unaware of economic crises. From that the study gathered how students generally feel about the economic recession. Most of the local students receive in average of Rm300-Rm600 monthly and comes from a middle income family status. I believe them having to live on around Rm 400 a month would be modest at best. Yet, with prices of good increasing either through gradual inflation or economic crisis, they managed to maintain with that amount and many feel that they are least effected by the economic crisis, though they acknowledge their ability to spend less. On the other hand, the foreign students has been projected to have more for their monthly income, some up to more than Rm 1500. Comparing this to the local, it shows that the locals have greater confidence in their financial securities than that of the foreign students. Local students are surrounded by elements familiar to them, whilst foreign student having to adapt to a foreign culture, may think they need more to spend. Take for example, Ali ; a Palestinian student may not be easily accustomed to normal day to day food like rice and sambal belacan, a cheap source of food for local student. He may have to reside if not occasionally to an Arab food restaurant which is considerably more expensive. This is a classic example involving just food, though many other factors may lead as to why foreign students need more monthly income. ( communication, room, etc.) Ironically, where both foreign and local students differ in their monthly income, they still think social outing should not be missed. Most of the respondents continues to go for social outings despite the increase prices of goods. This agrees with such longitudinal study carried out by a research that confirms how university students spend time, 16 hours for which is dedicated to socializing (National Survey of Student Engagement, 2006; Nonis, Philhours & Hudson, 2006). 311 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Coming to the second proposed research question, the result shows that there are no clear sign that economic recession is a direct factor to their expenditure behaviour. The study can only conclude that due to foreign students avid involvement in international banking transaction compared to local student, they are more responsive and vulnerable to global economic consequences. Foreign students also replied that due to the recent currency exchange rate, many felt that they have less to spend. This somewhat is aligned to the hypotheses proposed by this study that ultimately, foreign students are relatively more responsive to global economic conditions than local students. Conclusion In conclusion we can say that student’s expenditure is affected by current economic recession. Foreign students are affected more because of currency rates, increase of prices of basic needs, and it always more expensive to study abroad than to study at home. There are several limitations in this study. The findings, among other things may not represent wider population of both types of students due to limited number of respondents. However, the number of respondents in this study still provides the insight indeed. As findings presented, foreign students have more expenditures compare to local student and their pockets are affected by economic recession. Bibliography Freeman, Richard B. 1971. The Market for College. Trained Manpower. Cambridge, Mass.: Harvard University Press. Handa, M.L , and Skolnik, M.L 1975 “Unemployment, Expected Returns, and the Demand for University Education in Ontario: Some Empirical Results.” Higher Education 4: 27, 43. Hilgert, M. A., Hogarth, J. M., & Beverly, S. G. (2003). Household Financial Management: The Connection Between Knowledge and Behavior. Federal Reserve Bulletin July: 309- 322. Leslie, Larry I., and Brinkman, Paul T. 1987. “Student Price Response in Higher Education: The Student Demand Studies.” Journal of Higher Education 58(2): 181, 204. Najib Tun Abdul Razak, 2009. “In Introducing the Supplementary Supply (2009) Bill 2009.” Ministry 312 of Finance. 12. �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Nonis, S. A., Philhours, M. J. & Hudson, G. I. (2006). Where Does the Time Go? A Diary Approach to Business and Marketing Students’ Time Use. Journal of Marketing Education, 28, 121-134. Mattila, J. Peter, 1982 “Determinants of Male School Enrollments: A time Series Analysis.” Review of Economics and Statistics. 64, 242, 51. Ministry of Higher Education, 2009.( http://www.mohe.gov.my/educationmsia/index.php? article=mohe ) Green Economy-Green Sustainability-Green Ethics Nilgün Dolmaci, Nurdan Kuşat Süleyman Demirel Üniversity, Isparta, Turkey E-mails: nilgundolmaci@sdu.edu.tr, nurdankusat@sdu.edu.tr Abstract Although the concept ‘environment’ is perceived as a space where people live, it narrates an ecosystem in the broad sense. Ecosystem is described as a raw material store which fulfills the physical and biological needs. However, considering that the resources are scarce and the needs of people are limitless, it is clearly seen that the environmental resources are scarce as well. Within this content, efficient use of environmental resources has a great importance for sustainable development. Green economy approach brings a new perspective for the sustainable development. Since the degeneration in economic, cultural and historical environment led to development problems, green economy is an important instrument achieving sustainability in environmental values. In this study, green economy and green sustainability is handled from the point of decreasing the damage that environment and ecosystem are exposed. When it comes to solve the paradox between economic development and environment, the study touches on the green ethics perception which can be defined as getting and adopting the information, attitude and behavior that will preserve the living space and living quality of human beings both individually and globally. Keywords: Green Economy, Sustainable Development, Green Sustainability, Green Ethics 1. INTRODUCTION The words ‘green’ and ‘sustainability’ are usually used together. While the word ‘green’ represents the environment, ‘sustainability’ refers to convection of current resources to the next generation without any loss. Sustainable development, which is one of the most popular 313 � Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Extent The size or duration of the resource. 1338 Title A name given to the resource Report on : Students expenditure and the economic recession Author Author Kerim , Hadziabdic Abstract A summary of the resource. All subjects were selected from International Islamic University Malaysia (IIUM), data was collected using questionnaire which is attached to the research paper. There are two types of data which is local student’s data and foreign student’s data. The findings from research are representing that foreign students as well as local students are affected by current economic recession. Date A point or period of time associated with an event in the lifecycle of the resource 2012-05-31 Keywords Keywords. Conference or Workshop Item PeerReviewed H Social Sciences (General),HB Economic Theory,HG Finance,HJ Public Finance https://omeka.ibu.edu.ba/files/original/420badc84095e83d23957322ebbf4162.pdf f0ddc75bfa35c7a190eeb4c650e4eccf PDF Text Text Zhu, Jiang, FeiXiong, DongzhenPiao, Yun Liu, Ying Zhang (2011). “Statistically Modeling the Effectiveness of Disaster Information in Social Media”, 2011 IEEE Global Humanitarian Technology Conference, s. 431-436. Does predefined erp implementation methodology work for public companies in transitioning country? Classification of EEG signals for epileptic seizure prediction using ANN JasminKevric, AbdulhamitSubasi International Burch University, Faculty of Engineering and Information Technologies, FrancuskeRevolucije bb, Ilidža, Sarajevo, 71210, Bosnia and Herzegovina. E-mails:jkevric@ibu.edu.ba, asubasi@ibu.edu.ba Abstract In this paper, we developed a model for classification of EEG signals. The aim of the study is to determine whether this model can be used for epileptic seizure prediction if “pre-ictal” stages were successfully detected. We analyzed long-term Freiburg EEG data. Each of 21 patients contains datasets called “ictal” (seizure) and “inter-ictal” (seizure-free). We extracted 4096-samples (or 16 seconds) long segments from both datasets of each patient. These segments were decomposed into time-frequency representations using Discrete Wavelet Transform (DWT). The statistical features from the DWT sub-bands of EEG segments were calculated and fed as inputs to Multilayer Perceptron (MLP) and Radial Basis Function (RBF) network classifiers using 10-fold cross validation. We also applied multiscale PCA (MSPCA) de-noising method to determine if it can further enhance the classifiers’ performance. MLP-based approach outperformed RBF classifier with or without MSPCA, which significantly improved the classification accuracy of both classifiers. The proposed MLP-approach with MSPCAachieved a classification accuracy of 95.09%. We showed that a high classification accuracy of EEG signals can be accomplished in cases when additional “pre-ictal” class is introduced. Therefore, the proposed approach may become an efficient tool to predict epileptic seizures from EEG recordings. Keywords: Electroencephalogram (EEG); Epileptic seizure; Discrete Wavelet Transform (DWT); Multilayer Perceptron (MLP); Radial Basis Function (RBF) network; Multiscale PCA (MSPCA); Machine learning. 491 �1.INTRODUCTION Noninvasive electrodes on the scalp can record the brain's electrical activity called as electroencephalogram (EEG), produced by billions of neurons firing within the nervous system. The EEG signal is characterized by a nonstationarity in the waveforms and semistationary time-dependent states, and detection of these characteristics is a difficult task (Bigan, 1998). Over 50 million people in the world are affected by the epilepsy, the second mostcommon neurological disorder after stroke (D’Alessandro et al., 2003). Abnormal movements and seizures, resulting from the brain cells' excessive electrical discharge, are the signs of epilepsy. One of the most important causes of stress, morbidness and anxiety in epileptic patients is the inability of predicting seizure onset (Murray, 1993; Buck et al., 1997). Thereliable predictability of seizure onset would dramatically improve the safety and quality of life of these patients who cannot be treated successfully by common therapeutic options (Schachter, 1994). For example, patients would be able to prevent dangerous situations when being warned of upcoming seizure. Various automated intervention systems and measures could be implemented like applying electrical brain stimulations or delivering short-acting anticonvulsant drugs by using implanted devices (Stein et al., 2000;Elger, 2001). Additionally, the investigation of the pathophysiological mechanisms causing seizures could be improved by the accurate detection of states preceding seizures. Mormann et al., (2007) stated that seizure prediction is the long and winding road in their review article. D’Alessandro et al., (2003)used intelligent genetic search technique to classify preseizure and non-preseizure classes from four patients by a probabilistic neural network, reporting a sensitivity of 62.5% with 90.5% specificity. Costa et al., (2008)compared 6 types of neural network architectures which used 14 features extracted from EEG of two patients to classify brain states into four classes: inter-ictal, pre-ictal, ictal and pos-ictal. The accuracies of up to 99% were achieved. Mirowski et al., (2009)achieved 71% sensitivity and 0 false positives using convolutional networks combined with wavelet coherence. Chisci et al., (2010) used Autoregressive (AR) models to classify pre-ictal and inter-ictal classes from nine patients, reporting 100% sensitivities and average false positive rates of 0.174/h (on the interictal dataset). This paper is organized as follows. Section 2 describes the EEG data, signal processing and feature extraction methods, and the artificial neural networks with a brief description of each one. In section 3, the performance of the proposed system is presented and discussed. Finally, section 4 presents concluding remarks and perspectives for future work. 492 �2.Materials and methods 2.1 Subjects and data recording We analyzed long-term EEG data recorded during invasive pre-surgical epilepsy monitoring at the Epilepsy Center of the University Hospital of Freiburg, Germany. The Neurofile NT digital video EEG system with 128 channels, 256 Hz sampling rate, and a 16 bit analogue-todigital converter was used to acquire the EEG data. Each of 21 patients, suffering from medically intractable focal epilepsy, contains datasets called “ictal” and “inter-ictal”. The “ictal“ dataset consists of files containing epileptic seizures, each having a seizure-free "preictal" period of at least 50 minutes. The “inter-ictal“ dataset consist of approximately one day of seizure-free EEG recordings for each patient. Each patient had between two and five seizures, with an average of 4.2 seizures per patient or a total number of 87 seizures(Maiwald et al., 2004).The onset and end times of each seizure were determined by visual examination of skilled epileptologists. 2.2 Multiscale Principal Component Analysis Multiscale Principal Component Analysis (MSPCA) combines the wavelet analysis with PCA. The MSPCA method incorporates the decomposition of each variable on a selected family of wavelets during which the wavelet coefficients are thresholded. After that, the PCA model is separately built for the coefficients at each scale. In order to yield one model for all scales together, the models at important scales, which show process disturbances or abnormal operation, are merged in an effective scale-recursive way(Bakshi 1998; Ganesan, Das, & Venkataraman, 2004).Because of its multiscale type, it is suitable to use MSPCA for modeling of data consisting of contributions from events which behavior changes over time and frequency. MSPCA is powerful tool for monitoring autocorrelated measurements without time-series modeling or matrix augmentation due to approximate decorrelation of wavelet coefficients. The MSPCA method not only selects and monitors the significant signal features but also conforms to the nature of the signal (Bakshi 1998). 2.3 Discrete Wavelet Transform Signals like EEG may contain transitory or non-stationary characteristics. That is why Fourier Transform, which can be applied to the stationary signals, is not an ideal method to be directly applied to signals like EEG. Therefore, time-frequency methods like Wavelet Transform should be used. The analysis based on Discrete Wavelet Transform is best explained in terms of filter banks. Multi-resolution decomposition of a signal is the procedure of using a group of filters to separate that signal into various spectral components. Every stage of this procedure consists of two digital filters and two down-samplers by 2. The first filter is the discrete mother wavelet, being high-pass in nature. The second filter is its mirror version, being low pass in 493 �nature. Outputs of the first high-pass and low-pass filters, once being down-sampled, provide the detail D1 and the approximation A1, respectively (Adeli, Zhou, & Dadmehr, 2003;Marchant, 2003; Semmlow, 2004). In DWT analysis it is very important to choose the appropriate number of decomposition levels and appropriate wavelet selection. The components of the dominant frequency of the signal are the main base for choosing the number of decomposition levels. Distribution of energy of the EEG signal in frequency and time is shown by a compact representation of the extracted wavelet coefficients. Using statistics over the wavelet coefficients sets helped in decreasing the dimensionality of the extracted feature vectors (Kandaswamy et al., 2004).Subasi (2007) and Subasi&Gursoy (2010) achieved high accuracies in classifying EEG signals using statistical feature vectors extracted from wavelet coefficients. 2.4 Multilayer Perceptron Multilayer feedforward networks is composed of a set of source nodes which serve as sensory units that form the input layer, one or more hidden layers and an output layer. Hidden layers and an output layer consist of computational nodes. The input signal is transmitted through the network in a forward direction, layer by layer. This type of neural networks, which represents a generalization of the single-layer perceptron, is generally known as multilayer perceptron (MLP). When trained in a supervised manner using highly popular and computationally efficienterror back-propagation algorithm, multilayer perceptrons can successfully solve complex and different problems, but certainly do not provide an optimal solution for all solvable problems. Essentially, error back-propagation learning consists of a forward pass and a backward pass. In the forward pass, the effect of an input vector, when being applied to the sensory nodes, propagates through the network. At the end, a set of outputs, as the real response of the network, is formed. The synaptic weights are all fixed during this stage. However, these synaptic weights are being tuned according to errorcorrection rule during the backward pass. Namely, an error signal is produced as the real response of the network is subtracted from a desired (target) response. This error signal is then propagated backward through the network during which the synaptic weights are tuned so that the difference between the real and the desired response of the network decreases.One or more layers of hidden neurons enhance network’s learning of difficult problems by extracting more significant features from the input vectors(Haykin 1999). 2.5 Radial Basis Function Network The design of a neural network can also be perceived as a curve-fitting (approximation) problem in a high-dimensional space,where learning is viewed as finding a surface which 494 �represents a best fit to the training data in a multidimensional space. This multidimensional surface is then used to interpolate the test data. The method of radial-basis functions is motivated by such a viewpoint. The early work on radial-basis functions is reviewed in Powell (1985).A radial-basis function (RBF) network basically consists of three layers having completely different tasks. The input layer connects the network to the environment via source nodes that serve as sensory units. A nonlinear transformation from the input space to the hidden space of high dimensionality is applied in the second layer as the only hidden layer in the network. The output layer, producing the response of the network to the input vector, is linear. The effect of applying nonlinear transformation prior to a linear transformation is explained by Cover (1965). As stated by him, there is a higher change of a pattern recognition problem to be linearly separable in a high-dimensional space. Therefore, the dimension of the hidden space in an RBF network is often made high. Moreover, the higher the dimension of the hidden space, the more accurate the approximation of smooth mapping is(Mhaskar, 1996; Niyogi and Girosi, 1996). 3. Experimental results and discussion 3.1 Experiment Classification of EEG signals consists of data acquisition and preparation, signal processing, feature extraction and classification. We propose a method based on MSPCA for denoising, DWT for feature extraction and ANNs for classification.We extracted 4096-samples-long segments from both datasets of each patient. Approximately two segments per hour were extracted from “inter-ictal” dataset, producing 1050 inter-ictal segments. We also extracted two types of segments from “ictal” dataset: ictal and pre-ictal. We used minimum number of 4096-samples-long segments to cover all 87 seizure activities, producing 652 ictal segments. We extracted five segments within a seizure-free "pre-ictal" period of 50-60 minutes, producing 435 pre-ictal segments. Only one out of six channels was used for extraction of EEG segments, although results from the different authors presented a poor performance of univariate measures (Mormann et al., 2005). We selected the number of decomposition levels for DWT to be 5 since EEG signals contain no useful frequency components above 30 Hz, and because of 256Hz sampling rate of Neurofile NT used to acquire the EEG data. Daubechies 4 (DB4) wavelet filter was used to reconstruct the detail and approximation records.All 2137 EEG segments, which belong to three different classes, were divided into sub-band frequencies A5 (0-4 Hz), D5 (4-8 Hz), D4 (8-16 Hz), D3 (16-32 Hz), D2 (32-64 Hz) and D1 (64-128 Hz). Sub-band frequencies A5 and D3-D5 almost perfectly correspond to δ (0-4 Hz), θ (4-8 Hz), α (8-12 Hz) and β (12-26 Hz) frequencies of EEG signals (Bylsma et al., 1994). A set of fifteen statistical features was then extracted from the wavelet coefficients representing these sub-band frequencies and fed as inputs to classifiers. A Multiscale PCA (MSPCA) de-noising method was also applied to determine if it can further enhance the 495 �classifiers’ performance. We implemented a classification system based on MLP and RBF network using wavelet statistical features as inputs and 10-fold cross validation method, to guarantee validity of the results. 3.2 Results We performed two types of experiment: with and without MSPCA de-noising method applied.In Table 1, we have seen that MSPCA drastically improved the classification accuracy of both classifiers, while MLP network achieved higher total classification accuracy than RBF network. The accuracies for each class are also presented in Table 1. Accuracy Accuracy Accuracy Total (Pre-ictal) (Inter-ictal) (Ictal) Accuracy MLP +DWT 2.76 % 89.43 % 60.58 % 62.99 % RBFN +DWT 7.13 % 90.57 % 54.45 % 62.56 % MLP + MSPCA+DWT 87.82 % 97.43 % 96.17 % 95.09 % RBFN + MSPCA+DWT 71.49 % 97.14 % 94.02 % 90.97 % Classifier Table 1. Accuracies of MLP and RBF network classifiers with and without MSPCA. MSPCA significantly improved the classification accuracy for ictal and pre-ictal samples, while accuracy performance for inter-ictal class was only slightly improved. Classifiers are totally useless for seizure prediction if MSPCA is not applied. 3.3 Discussion The experiment results show that MSPCA is an effective denoising method for improving the classification performance. Without MSPCA, our method classified many pre-ictal/ictal data samples as being inter-ictal.Aminghafari, Cheze, & Poggi, (2006) showed that de-noised signals by MSPCA magnify the spikes more clearly. Therefore, MSPCA enhanced our classifier's performance for about 50%. Our approach outperformed the one explained in D’Alessandro et al., (2003). Theyalso used data of only four patients to developfour different classifiers for each patient. Although Costa et al., (2008)introduced one more class (pos-ictal) and achieved accuracies of 99%, using data of only two patients from Freiburg database is insufficient to successfully train and develop a model. Mirowski et al., (2009) predicted all seizures without false positives for 15 patients, without mentioning how classifier performed on data belonging to six remaining patients 496 �from Freiburg database. Thus, sensitivity of 71% is reported, which is lower than classification accuracies for pre-ictal class of both of our classifiers. Chisci et al., (2010)used nine patients for which additional electro-corticographic recordings (grid–strip electrodes) were available and achieved 100% sensitivity with low false positive rates. However, they developed patient-specific system by training nine classifiers, where each classifier used train and test data of only one patient. Our proposed system is more general because only one classifier is developed for all patients and it is not bound to specific group of epileptic patients. 4. CONCLUSION We showed that a high classification accuracy of EEG signals can be accomplished in cases when additional “pre-ictal” class is introduced. Many research papers showed that DWT coefficients well represent the EEG signals and ensure a good differentiation between classes. However, we managed to achieve high accuracies only when MSPCA de-noising method was applied to Freiburg dataset. The accuracy may be further improved by applying dimension reduction or feature selection methods like ICA or LDA on the feature vectors. Measures that characterize the relations between two or more channels can be used to further enhance the performance. Using only inter-ictal and pre-ictal samples to train the classifier could be investigated since our aim is not seizure detection. Freiburg dataset can serve as a challenge for trying other feature extraction methods rather than DWT. The proposed approach may become an efficient tool to predict epileptic seizures from EEG recordings. REFERENCES Adeli, H., Zhou, Z., & Dadmehr, N. (2003). Analysis of EEG records in an epileptic patient using wavelet transform. Journal of Neuroscience Methods 123, 69-87. Aminghafari, M., Cheze, N., & Poggi, J.-M. (2006). Multivariate denoising using wavelets and principal component analysis. Computational Statistics & Data Analysis, 50, 2381-2398. Bakshi, B. R. (1998). Multiscale PCA with Application to Multivariate Statistical Process Monitoring. AlChE, 44(7), 1596-1610. Bigan, C. (1998). A recursive time-frequency processing method for neural networks recognition of EEG seizures. In E. C. Ifeachor, A. Sperduti, & A. Starita (Eds.), Neural Networks and Expert Systems in Medicine and Healthcare. Singapore: World Scientific. Buck, D., Baker, G. A., Jacoby, A., Smith, D. F., & Chadwick, D. W. (1997). Patients' experiences of injury as a result of epilepsy. Epilepsia, 38, 439-44. Bylsma, F., Peyser, C., Folstein, S., Ross, C., & Brandt, J. (1994). EEG power spectra in Huntington’s disease: clinical and neuropsychological correlates. Neuropsychologia 32(2), 137-150. 497 �Chisci, L., Mavino, A., Perferi, G., Sciandrone, M., Anile, C., Colicchio, G., et al. (2010). Real-Time Epileptic Seizure Prediction Using AR Models and Support Vector Machines. IEEE Transactions on Biomedical Engineering, 57(5). Costa, R. P., Oliveira, P., Rodrigues, G., Leitao, B., & Dourado, A. (2008). Epileptic Seizure Classification Using Neural Networks with 14 Features. In I. Lovrek, R. J. Howlett, & L. C. Jain (Eds.), KES 2008, Part II, LNAI 5178 (pp. 281-288). Springer-Verlag Berlin Heidelberg. Cover, T. M. (1965). Geometrical and Statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Transactions on Electronic Computers, 326334. D'Alessandro, M., Esteller, R., Vachtsevanos, G., Hinson, A., Echauz, J., & Litt, B. (2003). Epileptic seizure prediction using hybrid feature selection over multiple intracranial EEG electrode contacts: a report of four patients. IEEE Transactions on Biomedical Engineering 50 (5), 603-615. Elger, C. E. (2001). Future trends in epileptology. Current Opinion in Neurology, 14, 185186. Ganesan, R., Das, T. K., & Venkataraman, V. (2004). Wavelet-based multiscale statistical process monitoring: A literature review. IIE Transactions, 36(9), 787-806. Haykin, S. (1999). Neural Networks: A Comprehensive Foundation (Second ed.). Prentice Hall. Kandaswamy, A., Kumar, C., Ramanathan, R., Jayaraman, S., & Malmurugan, N. (2004). Neural classification of lung sounds using wavelet coefficients. Computers in Biology and Medicine, 34(6), 523-537. Maiwald, T., Winterhalder, M., Aschenbrenner-Scheibe, R., Voss, H. U., Schulze-Bonhage, A., & Timmer, J. (2004). Comparison of three nonlinear seizure prediction methods by means of the seizure prediction characteristic. Physica D 194, 357-368. Marchant, B. P. (2003). Time–frequency analysis for biosystem engineering. Biosystems Engineering, 85(3), 261-281. Mhaskar, H. N. (1996). Neural networks for optimal approximation of smooth and analytic functions. Neural Computation, 8, 164-177. Mirowski, P., Madhavan, D., LeCun, Y., & Kuzniecky, R. (2009). Classification of patterns of EEG synchronization for seizure prediction. Clinical Neurophysiology, 120(11), 19271940. Mormann, F., Andrzejak, R. G., Elger, C. E., & Lehnertz, K. (2007). Seizure prediction: the long and winding road. Brain, 130, 314-333. Murray, J. (1993). Coping with the uncertainty of uncontrolled epilepsy. 2, 167-178. 498 �Niyogi, P., & Girosi, F. (1996). On the relationship between generalization error, hypothesis complexity, and sample complexity for Radial Basis Functions. Neural Computation, 8, 819842. Powell, M. J. (1985). Radial basis functions for multivariable interpolation: a review. IMA Conference on Algorithms for the Approximation of Functions and Data. Shrivenham: RMCS. Schachter, S. C. (1994). The brainstorms companion: epilepsy in our view. New York: Raven Press. Semmlow, J. L. (2004). Biosignal and biomedical image processing: MATLAB-based applications. New York: Marcel Dekker, Inc. Stein, A. G., Eder, H. G., Blum, D. E., Drachev, A., & Fischer, R. S. (2000). An automated drug delivery system for focal epilepsy. Epilepsy Res, 39, 103-114. Subasi, A. (2007). EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Systems with Applications 32, 1084-1093. Subasi, A., & Gürsoy, M. I. (2010). Comparison of PCA, ICA and LDA in EEG signal classification using DWT and SVM. Expert Systems with Applications 37, 8659-8666. Classification of Fetal State from the Cardiotocogram Recordings using ANN and Simple Logistic Hakan Sahin, Abdulhamit Subasi International Burch University, Faculty of Engineering and Information Technologies, 71000, Sarajevo, Bosnia and Herzegovina E-mail:hakanshah@hotmail.com , asubasi@ibu.edu.ba Abstract In this study, we present a comparison of machine learning technics using antepartum cardiotocographs performed by SisPorto 2.0 in predicting newborn outcome. CTG is widely used in pregnancy as a technique of measuring fetal well-being, mainly in pregnancies with increased risk of complications. It is a non-invasive way for checking the fetal conditions in the antepartum period. CTG is a continuous electronic record of the baby’s heart rate acquired via an ultrasound transducer placed on the mother’s abdomen. The information 499 � Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Extent The size or duration of the resource. 1208 Title A name given to the resource Classification of EEG signals for epileptic seizure prediction using ANN Author Author Kevric, Jasmin Subasi, Abdulhamit Abstract A summary of the resource. In this paper, we developed a model for classification of EEG signals. The aim of the study is to determine whether this model can be used for epileptic seizure prediction if “pre-ictal” stages were successfully detected. We analyzed long-term Freiburg EEG data. Each of 21 patients contains datasets called “ictal” (seizure) and “inter-ictal” (seizure-free). We extracted 4096-samples (or 16 seconds) long segments from both datasets of each patient. These segments were decomposed into time-frequency representations using Discrete Wavelet Transform (DWT). The statistical features from the DWT sub-bands of EEG segments were calculated and fed as inputs to Multilayer Perceptron (MLP) and Radial Basis Function (RBF) network classifiers using 10-fold cross validation. We also applied multiscale PCA (MSPCA) de-noising method to determine if it can further enhance the classifiers’ performance. MLP-based approach outperformed RBF classifier with or without MSPCA, which significantly improved the classification accuracy of both classifiers. The proposed MLP-approach with MSPCAachieved a classification accuracy of 95.09%. We showed that a high classification accuracy of EEG signals can be accomplished in cases when additional “pre-ictal” class is introduced. Therefore, the proposed approach may become an efficient tool to predict epileptic seizures from EEG recordings. Keywords: Electroencephalogram (EEG); Epileptic seizure; Discrete Wavelet Transform (DWT); Multilayer Perceptron (MLP); Radial Basis Function (RBF) network; Multiscale PCA (MSPCA); Machine learning. Date A point or period of time associated with an event in the lifecycle of the resource 2012-05-31 Keywords Keywords. Conference or Workshop Item PeerReviewed H Social Sciences (General),T Technology (General) https://omeka.ibu.edu.ba/files/original/31ab889c8926473947fcc11c8a4f3bb4.pdf 36a205a53358663839d102576a94ffd2 PDF Text Text 3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Kılıç, S., Kendirli, H. Ç. (2005). Endüstriyel pazarlarda ilişkisel pazarlamanın yeni ekonomideki yeri ve önemi. Üçüncü Sektör Kooperatifçilik Dergisi, 148 (2). ss: 20-36 Oktay, E., Balkanlı, A. ve Salepçioğlu, A.(2004) Bilgi Toplumunda Yeni Ekonomi ve Dönüşüm Stratejileri, http://iibf.ogu.edu.tr/kongre/bildiriler/04-02.pdf. Öztürk, L.(2005), Türkiye’de Dijital Eşitsizlik: TÜBİTAK –BİLTEN Anketleri Üzerine Bir Değerlendirme, Erciyes Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, Sayı :24, S.11-131. Pau, F. L.(2002) The Communications and İnformation Economy: issues, tarrifs and economics research areas. Journal of Economic Dynamics& Control, 26:1651-1675. Sabuncuoğlu, A., Vural, B. A.(2008). Bilgi ve İletişim Teknolojileri ve Ütopyan Bakış Açısı. Selçuk Üniversitesi İletişim Dergisi, Cilt:5, Sayı: 3, 5-19, Temmuz. Şahin, L., Çetin, B. I., Yıldırım, K. (2010). Bilişim Teknolojilerindeki Gelişmelerin İşletmelerin Strateji ve Maliyetleri Üzerine Etkileri. www.iudergi.com/tr/index.php/sosyalsiyaset/article/view/98. Toffler A.(1970), Future Shock, A Bantom Book, United States America. Yeloğlu, H. O.(2004). Bilgi Ekonomisi ve Değişkenleri: Türkiye ve OECD Karşılaştırmaları, 3. Ulusal Bilgi, Ekonomi ve Yönetim Kariyeri, Eskişehir. www. tuik. gov.tr. (18.08.2010), Haber Bülten, Sayı : 148 http://www.tuik.gov.tr/VeriBilgi.do?tb_id=25&ust_id=8: 26.04.2012 http://www.wrc.org.uk/includes/documents/cm_docs/2011/s/1_statistics_about_women_in_th e_uk_2009_25_5_10_latest_nn_sr1.doc.:26.04.2012. unpan1.un.org/intradoc/groups/public/documents/.../unpan023829.pdf. 26.04.2012. Walter W. Powell and Kaisa Snellman (2004) “The knowledge economy”, Annu. Rev. Sociol. 30:199–220 John Houghton and Peter Sheehan (2000). “A Primer on the Knowledge Economy” Centre for Strategic Economic Studies, Victoria University Economic Costs And Benefits Of The Eu Enlargement: The Impact On The Eu And Seec’s Kurtagić Haris, Nuroglu Elif International University of Sarajevo, Sarajevo, B&H E-mails: kurtagic.h@hotmail.com,enuroglu@ius.edu.ba Abstract The South-eastern enlargement of the European Union will be the sixth enlargement since establishing the European Community in 1957. The research uses the Gravity model, and measures the factors that have an influence on trade. The Gravity model involves coefficients 186 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo that explain the pattern of trade with GDP, geographical distance, population, and several dummy variables. Trade that is explained by Gravity model includes two regions, EU-15 (inclusive Bulgaria and Romania) and SEEC’s. The reason why Bulgaria and Romania are included, even if they are part of the SEEC’s, is to acquire as accurate pattern of trade as possible. Comparing the data from 2010, the gravity model describes trade flows between 23 countries. Thus, the purpose of this study is to analyze trade flows between two regions. Taking into consideration the costs of enlargement, this research examines the effects of the trade, its significance on the development of SEEC’s after enlargement, well-being of countries that are not part of the EU, as well as it offers a solution for the South-east European countries. Therefore, the solution that this research proposes is a model based on creation of the Balkan Union. Keywords: EU-Enlargement, Gravity model, South-eastern Europe, European union, Trade flows. 1.INTRODUCTION The South-eastern enlargement of the European Union (EU) – the sixth since 1973 – is a huge test for the EU, as well as for the applicant countries. The European Union consists of 27 members. Besides incumbent members, there are candidates, as well as potential candidates. Inclusive candidates: Croatia, Macedonia (FYR), Montenegro, Iceland, Serbia and Turkey, the potential candidates will comprise the next enlargement of 9 countries. The potential candidates are Albania, Bosnia and Herzegovina, and Kosovo. This enlargement will increase the EU area by 25%, the number of population by 19%, and absolute GDP by 5%. Although, the exact time pattern of accession is not clear yet, the European Commission plans to start with a group of 3 states that consist of Croatia, Montenegro and Iceland. Turkey is not sure yet, whether to access the Union or not, because the country has strong economy, and many analytics think that joining the Union would hurt Turkish economy. On the other hand, the applicant countries of South-eastern Europe are relatively poor countries with a GDP per capita below the EU average. Hence, the average GDP per capita of nine countries is $10,490 that would be 3 times lower compared to EU-27 or 4 times lower compared to EU-15. Similar to the third EU enlargement, the next enlargement would be a new challenge for the EU countries, as the integration of poor with rich countries increases heterogeneity. The South-east European countries will enter the EU on the basis of the Treaty of Accession. Once they access the EU, the members are part of a union and a single market. One union of 27 countries with over 501 million consumers, which have access to a single market, is of huge importance for customers. Construction of a single market of the European Union has brought the new impact, and improved the emergence of a common EU policy such as competition policy. The EU constantly works on improvement of common policies, especially, on a common market. Those policies have gained great importance that increases over time. The policies are important, since they strengthen mutual trade, improve the quality of products and services, then they expand a single market, and the most important thing is that they reduce trade barriers and increase positive effects of the common market. As a result, the EU policies preserve the good functioning of market, and the European Commission prevents or corrects 187 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo the non-competitive behaviour of companies. When it comes to South-east European countries (SEEC’s), the fact is that its consumers enjoy a freedom of choice that is diversified by almost the same prices, lower quality and a fewer innovations comparing to the EU standards. The competition is present between SEEC’s, but they are not competitive to the EU single market. Those companies cannot achieve scale of economies or competitive advantages as the EU countries do. Thus, this research shows economic costs and benefits that the EU enlargement brings to SEEC’s. It presents gains from trade for both, the European Union and South-east European Countries. This paper looks at the economic costs and benefits of the enlargement of the EU, as well as the impact that the enlargement would bring to the Southeast European countries. 2.MAASTRICHT TREATY, EUROPEAN UNION, SINGLE MARKET, AND SINGLE CURRENCY – EURO In order to improve trade, six countries (Belgium, France, Germany, Italy, Luxembourg and the Netherlands) have adopted first four regulations for a common market in agriculture, finance and regulation of governing competition. On 1 January 1973, Denmark, Ireland and the United Kingdom joined the EU. Greece became the 10th member of the EU in 1981 and Spain and Portugal join Union five years later. The situation was stable until the Berlin Wall fell in 1989, so the European Economic Community (EEC) member states were negotiating over a new treaty at Maastricht in December 1991. However, it included intergovernmental cooperation in foreign policy and internal security that resulted in the Maastricht Treaty, which created European Union on 1 November 1993. The collapse of communism throughout Central and Eastern Europe has connected Europeans. As a result of that, in 1993 the Single Market was completed with freedom of movement of goods, services, people and money. Three new members came in 1995, Austria, Finland and Sweden. The euro, Europe’s single currency replaced the old currencies on 1 January 2002, when 12 EU Member States adopted it as their official currency, creating the euro zone. The euro zone makes life easier for business, consumers and travellers. On 1 May 2004, 10 countries got memberships in the EU: the Czech Republic, Cyprus, Hungary, Malta, Poland, Slovakia, Estonia, Latvia, Lithuania and Slovenia, while Bulgaria and Romania on 1 January 2007. Today, the EU has 27 member states. Enlargement to 27 was one of the most important steps in the history of European integration. 12 new countries in the EU, not only have expanded geographical size and population, but they have created an end for splitting the continent into two since 1945. As the idea of the EU says, democratic freedom was settled in 12 new member states. The creation of the single market gave European Union countries a strong incentive to liberalize previously protected monopolistic markets. Within the Union, Member States have removed all tariffs on trade, while having unified tariffs on imports from outside the EU. It means that no matter which country is the importer, the tariff paid on products is the same, and once customs procedures are completed, goods can be shipped throughout the EU without additional duties. In accordance to rules and regulations of the European Union, member of the Union should be guaranteed: 188 Price stability, Stability of currency, Limitations of public debt, 60% of GDP, Economic balance with limited deficit of national budget, Investment stability, in sense of level of long-run interest rates, and �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo - 3% of GDP. The European Union is formed on the principles of liberty, democracy, respect for human rights and fundamental freedoms, and the rule of law, principles that are common to the Member States. The EU has a motto, ‘United in diversity’, and May 9 is commemorated each year as Europe Day. By singing the Treaty of Rome in 1957, the EU members promised to abolish barriers to trade freely across the European continent. In Tupy (2003), the benefits of free movement of goods are seen as major benefits of consumers. Generally, the European Union has integrated the market, and established common rules. Those rules are implemented through technical standards of consumer protection, environmental standards, competition policy, and fairness in the workplace (Tupy, 2003, p. 6). Thus, for the SEEC’s the common market could be a place for integration of every aspect of the state. Once the boarders are opened to flow of goods and capital, people would look at historic events less, and they will go for personal interest. 3.EUROPEAN UNION TODAY Today, European Union exists as a union that aims to increase economic and political circumstances. The logic behind that is to deliver a peace, stability, and prosperity, to help improve living standards, promote a single currency that will build a single market, where people, goods, services and capital could move within the European Union. It is not a government or state or international organization, but a novel entity which respects human rights. Many countries, a huge single market, and single currency provide many benefits, but for whom? As a single market, the EU is a major world trading power. The single market aims at putting down barriers and simplifying rules to enable everyone in the EU to take advantage of the opportunities given to them by having access to 27 countries and 501 million people. Looking from economics perspective, small countries from Europe cannot achieve growth and prosperity without the EU. Therefore, in order to compete on the world stage and achieve economies of scale, the European countries need a broader base, and the European single market provides it. The single market is one of the European Union’s greatest achievements. Restrictions on trade and free competition between member countries have gradually been eliminated; therefore, the whole system helps standards of living to rise. Within the EU, all border controls on goods have been eliminated, together with customs controls on people, but the police still conduct random checks as part of the fight against crime and drugs. When it comes to tax barriers, then tax barriers have been reduced by partially aligning national Value Added Tax rates, which must be agreed by the EU member states. There is also the EU’s competition policy that tries to ensure that within the European single market competition is not only free but also fair. Therefore, in the EU single market there is no cartel, or unfair monopoly. The European Union was created to succeed in political objectives, through achieving economic cooperation. In modern terms, people call it as an “area of freedom, security and justice”, where every citizen has an equal justice and protection by the law. The European Commission represents the EU in international negotiations at the World Trade Organizations (WTO). Right now, the EU would like to put more emphasis on quality of food, precautionary principle (“better safe than sorry”) and animal welfare. 189 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo The EU has regional policy stating that European Funds should be used to improve development in regions that are lagging behind, to increase standard of living in areas that are in decline, to help young people and the long-term unemployed find work. One important and interesting thing is that European Funds are also allocating funds to farming and to lessfavoured rural areas. In order to fund its policies, the European Union has an annual budget that in 2010 amounted to more than € 140 billion. The budget is financed from the EU’s own resources that cannot exceed 1.23 % of the total gross national income of all the member states (Fontaine, 2010, p. 35). The resources are mainly collected from: - Customs duties on products imported into the EU, A percentage of the value added tax (VAT) levied on goods and services throughout the EU, and Contributions from the member states, reflecting the wealth of each country. The European Union has more influence on the world stage when it speaks with a single voice in international affairs such as trade negotiations. 4.European Enlargement On January 1, 2007, the EU recorded the fifth enlargement. Bulgaria and Romania became new members of the EU. Before that, on May 1, 2004 the EU enlarged from 15 to 25 member countries. In the period from 1990 to 1999, the EU invested more than $85 billion to support the new Member States during the accession process (Delegation of the European Union to the United States of America, 2011, p. 21). Every new enlargement of the European Union is seen as a historic step toward long-term objectives of the union. Thus, any country that respects liberty, democracy, human rights and fundamental freedoms, and the rule of law is qualified to apply for EU membership. Applying for the EU membership is the start of a long and rigorous process. Once a country submits an application to the Council of the EU, it activates a sequence of EU procedures that may, or may not, result in the country being invited to become a member. After applying for membership, the process starts with accomplishing the Copenhagen Criteria. There are not many criteria, but, in essence, every country has to work on it, since the criteria are detailed. Fontaine (2010) mentions these criteria as follows: - Institutions that provides high democracy, the rule of law, human rights and respect for and protection of minorities. Strong market economy and the ability to cope with threats and pressure within the Union. Ability to take on the obligations of membership, accomplishing the aims of the Union (Fontaine, 2010, p. 16). Once the Council unanimously agrees to begin accession negotiations, discussions may be formally opened. The negotiation has got 35 separate policy areas that are called “chapters”, and each candidate country proceeds separately from one stage of the process to next. Each stage must satisfy all conditions, and then the candidate country could move on. Thank to this process, the prospect of accession acts as a powerful incentive for reform, providing simultaneously benefits to the EU and to its acceding members. 190 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo A candidate country is one whose EU membership application has been accepted by all relevant EU institutions, allowing it to begin accession negotiations. Once negotiations are concluded to the desired level for both sides, a comprehensive Draft Accession Treaty is submitted for approval by the Council of the EU, the European Commission, and the European Parliament. After the treaty is approved, it is signed by the candidate country and the representatives of all EU Member States. Afterward, all Member States and the candidate get the treaty for ratification. Once the ratification process is done, the treaty enters into force on its scheduled date, and the candidate country becomes an EU Member State. 5.Enlargement of the South-eastern Europe The next enlargement in the EU is related to Western Balkan region. The structure and procedures to be applied on the Western Balkan region are the same as it was for the former candidates. A first significant step in this process is the establishment of European partnership with Albania, Bosnia and Herzegovina, Kosovo, Montenegro, and Serbia. It would be a very important factor that assists the Western Balkan states in preparing for membership within rational framework and in developing action plans with timetables of reforms. “The main instrument that created by the European Union for Balkan integration is the Stabilization and Association Process (SAP), launched in 2000, that was established as a long process in order to establish development of the Western Balkans both in terms of political effort and financial and human resources” (Montanari, 2005, p. 59). The aim of the SAP is to create conditions that alike to the EU. Thus, the candidates work on preparation for future EU standards. When it comes to SEEC’s enlargement, conflicts can arise between European Union members as a result from its redistribution effects. EU members observe SEEC’s as a geographical area for expanding single market that can import more goods and services from the current EU members. Once the union is enlarged, there is a new distribution of income that can create lower income for some of the EU 27. That is not the only threat for EU 27, high unemployment is another one. Taking this fact into consideration, when more immigration happens, the neighbouring countries of the SEEC’s might be affected more. First targets are Slovenia and Hungary as very close countries to candidate of the EU. Previous five enlargements are observed if they were Pareto efficient for all member states and the candidate states, and evidence suggests that enlargements were not Pareto efficient in every enlargement round (Schneider T. P., 2007, p. 570). Thus, as Schneider (2007) states, the next enlargement is going to be very complicated from aspects of EU redistribution and from the free movement of labour. Thus, “the EU Eastern enlargement will adversely affect labourintensive and low-tech sectors in the EU member countries but will stimulate growth of skillintensive service industries and some capital-intensive and high-tech industries in Western Europe” (Schneider T. P., 2007, p. 572). Table 1 Basic Socioeconomic Indicators for South-eastern Europe (2010) Population (millions) 191 Per capita GDP (current US$) Unemployment 2009 (percent of labour force) �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Albania Bosnia and Herzegovina Bulgaria Croatia Kosovo Macedonia Montenegro Romania Serbia 3.20 3.76 7.54 4.42 1.82 2.06 0.63 21.44 7.29 3,678 4,409 6,325 13,754 3,059 4,460 6,510 7,538 5,269 6.8 9.1 45.4 32.2 19.1 6.9 16.6 Sources: World Bank, World Development Indicator. Monstat, Department of statistics of labour market, life conditions, social services and household consumption. As a result, the candidate countries would have an incentive to export workers, rather than to attract them. Table 1 shows the unemployment rate, population and GDP per capita for SEEC’s from 2010. From the table it is obvious that for relatively slow countries the unemployment rate is high. Kosovo has unemployment of 45.4%, but the real unemployment rate is around 25%, since the country has a problem with a gray economics. The other countries have acceptable rates, but still high that is a threat for labour migration and labour inflows in the EU. Two biggest countries of the SEEC’s became the EU members, and remaining 7 will bring 23 million new customers to the single market. If we take into account GDP’s of SEEC’s than it is obvious that states have relatively low ones, comparing to the EU15 (4 time lower), and EU-27 (3 time lower). Some of the candidate countries are economically weak, with high unemployment rate and low wages, and they will be ready to adapt to the system of free movement of labour. However, the Union could apply the potential limitations on the free movement of workers of new member. There was a case when the United Kingdom joined the Union in 1973. The state had to accept the limitations on the free movement of its workers within Belgium, France, Germany, and Luxembourg (Schneider T. P., 2007, p. 574). This clause could be used when accepting SEEC’s to the Union, where Austria, Slovenia, and Hungary might ask for limitations of free movements of these three countries’ workers in order to sign accession treaty. Within the EU, the gains from the enlargement could be redistributed from the either relative winners of enlargement (members of the Union or the candidates) to the relative losers of enlargement that can also be state from these two groups. When it comes to trade flows in 2010, than from Table 2 we see that Croatia is a main exporter to the EU-15 (BG+RO). The total import for the EU-15 (BG+RO) is 32.42% of total of the SEEC’s. On the other hand, Croatia is the leader in imports, as well. The total export from the EU (BG+RO) to Croatia is 39.32 of total of the SEEC’s exports. From that fact, it is not surprising that the country is the first to access the union. After enlargement the imports will be higher for each country, the members of the EU will export to SEEC’s and try to import less. Croatia and Serbia to some extent manage their resources properly, but the rest have to increase an export that is almost 40% of overall exports to SEEC’s. 192 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Table 2 EU-15 + BG and RO trade with South-eastern Europe by Country (2010) EU-15+BG and RO Imports Percentage of Imports from Million US$ SEEC’s Croatia Serbia Macedonia Bosnia and Herzegovina Albania Montenegro EU-15+BG and RO Exports Percentage of Exports to Million US$ SEEC’s 4,149 3,867 1,738 32.42 30.22 13.59 9,069 6,414 2,081 39.32 27.81 9.03 1,732 1,157 152 13.53 9.04 1.19 2,557 2,442 500 11.09 10.59 2.17 6.Strategy The strategy of the EU for creating sustainable growth and jobs encourages innovation within businesses and investment in people that can design a knowledge-based society. Not only that, but the idea is to attract more people into employment, and keep them in work longer as life expectancy rises. Besides, the adaptability of workers and enterprises, provide better education and skills, globalization and mobility would increase the well-being of the society. By 2020, the EU aims to have accomplished the following targets: - 75 % of the population aged 20-64 should be employed, 3 % of the EU’s GDP should be invested in R&D, The “20/20/20” targets in terms of reduction of greenhouse gas emissions, renewable energy production, and energy efficiency should be met. The share of school dropouts should be under 10 % and at least 40 % of the population between the ages of 30 and 34 should have a degree of diploma. 20 million fewer people should be living below the poverty line (Delegation of the European Union to the United States of America, 2011, p. 38). In order to accomplish the objectives, the EU adopted a proposal to re-focus R&D and innovation policy on major challenges; enhance the quality and attractiveness of Europe’s higher education system; deliver sustainable economic and social benefits from a Digital Single Market; enable the EU’s industrial base to become more competitive, promote entrepreneurship, and develop new skills for workers; and ensure economic, social and territorial cohesion by helping the poor and socially excluded. 193 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo 7.COSTS AND BENEFITS OF THE SOUTH-EASTERN ENLARGEMENT 7.1.The Overall Economic Impact of the Enlargement Taking into account the economic costs of enlargement, as well as the distribution of gain among incumbent Member States, we should consider the broader benefits and costs for the Union’s economy that would take place after the enlargement. The countries of the last EU enlargement were highly welcomed in the EU alliance because they belong to those of the developed countries in the EU, and, hence, did not only cost nothing, but contributed to the EU budget with significant amount. In a case of the Southeastern enlargement, the EU incumbents are firstly concerned about the costs, rather than the possible benefits. Typically, this enlargement would enable consumers and companies to arrange their businesses more efficiently, so that there would be higher output and income. Taking into consideration the costs of enlargement the question is how the distribution of gains is shared among the EU members. Those countries that have strong trade relations with the SEEC’s will benefit. The European enlargement process is by no means a win-win project, but relatively unpredictable condition that creates both winners and losers. Due to the huge wage-gap between East and West sides of Europe, there might be a migration wave from East to the West, as a result of full involvement of East side of Europe in the single market. On the other side, there are almost certain gains for some new members. Thus, the incumbent members prolong the acceptance of those candidates. One obvious case is Turkey. The country has applied for the European Union, and almost 17 years later in 2004, the EU finally decided to open accession negotiations. This news was a real shock for almost each member of the Union. That was in the period when Turkey was becoming stronger in its economy. The country was showing signals of real and healthy economy. Thus, many EU states appeared unwilling to accept Turkey to the European club. Immediately, some of them emphasized that the applicant would have to accept few exceptions from the common policies. Germany asked for permanent restrictions on the free movement of labour while France and other members of the EU called for refusing an allocation of agriculture subsidies to Turkish farmers (Schneider C. J., 2007, p. 85). Thus, from the case of Turkey, it is obvious that EU members are only looking for distribution of gains. Current members will question enlargement if a new state is to decrease the gains. In this context, economic integration with the European Union is a challenging issue. Official unemployment rates are very high, while “unofficial” estimates of unemployment that include the large gray economy could be lower for 20%. Thus, it is obvious that SEEC’s will benefit from the enlargement. They can export workers to neighbouring countries, even they can increase net trade, but the costs will be imposed on the current members of the EU. Table 3 EU-15 Countries’ Imports + BG and RO from South-eastern Europe (2010) Country 194 Million US$ Percentage of the EU total imports from the SEEC’s �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Italy Germany Austria Greece Romania Spain France Netherlands United Kingdom Bulgaria Belgium Sweden Denmark Portugal Finland Ireland Luxembourg Total 4,426 3,047 1,680 744 488 411 396 378 344 338 267 106 60 41 27 23 11 34.61 23.83 13.14 5.82 3.82 3.21 3.10 2.96 2.69 2.64 2.09 0.83 0.47 0.32 0.21 0.18 0.09 12,787 Source: IMF, Direction of Trade Statistics. When it comes to patterns of trade in the past few years, the EU trade with the South-eastern Europe has been in surplus; expressed in U.S. dollars. The largest trading partner of the SEEC’s is Italy, which absorbs 43 percent of EU imports from the region and accounts for 33 percent of the exports (Montanari, 2005, p. 7). This suggests that geographical distance plays a considerable role in determining trade patterns. Table 3 shows that in 2010 Italy accounts for 34.61% of total imports from SEEC’s to EU-15 (BG+RO). That is the main reason why SEEC’s trade mostly with their neighbouring partners. Behind Italy are Germany, Austria, Greece and Romania. It shows that distance between capitals plays considerable role in international trade. Countries that are away from the SEEC’s take account of around 10% of total imports. 7.2.The Costs of Enlargement When it comes to costs of the enlargement, then the most significant ones are: the costs for public finances, the costs of labour market disruption, and the costs of wage competition (Grabbe, 2001, p. 33). On the other hand, we have to take into account costs of the expansion of membership as well. It means that the EU bureaucratic machinery is likely to grow to be unmanageable. There is the added cost of preparing translations of all EU documents in language of member state. However, the cost of preparing is in second place, behind the reaching decisions. Reaching decisions on a unanimous or qualified-majority basis is likely to become more difficult (Richard E. Baldwin, 1997, p. 172). South-eastern countries have problems in reaching decisions in their parliaments, and accession to the EU would make things worse, since the new members would, very often, go for personal feelings, rather than for well-being of the society. Voting prolong, and not reaching decision on time, would increase the costs of bringing people to the parliament, and most important is the time spent while new regulations could have already taken place. 195 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Nowadays, the situation of Croatia and its accession to the European Union is related to neighbouring countries. Many analysts of macroeconomic issues warn saying that if Croatia enters the EU; many mechanisms will change, especially for Bosnia and Herzegovina and its exports and imports, because the EU asks for high criteria of product quality. As a country with cheaper labour, Bosnia is going to be a place where Croatia produces licensed products that are expensive to be produced in the EU. There are Croatian media, who speak about negative consequences of accession to the EU. If Croatia joins the Union, it would become small, political and economical unessential province inside the European giant countries (R.I., 2012). The European Union is not a single country, but Union of different people and countries (big and small), where member states represent their own interests on European level better and more efficiently, then in a case they would, in today’s globalized world, without the Union. Table 4 EU-15 Countries’ Exports + BG and RO to South-eastern Europe (2010) Country Italy Germany Austria Bulgaria Greece Netherlands Romania France United Kingdom Belgium Spain Denmark Sweden Ireland Finland Portugal Luxembourg Total Million US$ Percentage of EU total exports from the SEEC’s 6,116 5,428 2,775 1389 1,344 1,332 1,120 904 785 568 548 254 191 149 109 32 15 26.52 23.54 12.03 6.02 5.83 5.78 4.86 3.92 3.40 2.46 2.38 1.10 0.83 0.65 0.47 0.14 0.07 23,059 Source: IMF, Direction of Trade Statistics. Table 4 shows almost the same results as Table 3. Italy, with 26% is a leader in exports from SEEC’s, where Germany, Austria, Bulgaria and Greece come after. One half of the countries from the table have a total of 15% of overall exports that explains again importance of distance between capitals. 7.3. The Economic Benefits of Enlargement After enlargement, benefits will accumulate, not only to the member states of the Union, but also to us, individual citizens. One of the principles on which the EU is based is that it will 196 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo improve the welfare of its member states and their citizens. This process does not have appeal to the government of the member state which have initiated the accession process; it is also expected to have an influence on the citizens and their readiness to assist integration process. The main economic benefits after enlargement are the classical ones generated by integration processes. Thus, those benefits can be generated from expansion of the Single Market, strengthening of the Union’s position in global markets and catching up with EU living standards. When SEEC’s join the Union they will be passing from a free trade area for manufactured products to a single market. The major benefit is free movement of workers, which is a highly sensitive issue. Enlargement will be good for the European economy. Enlargement will add over million consumers to the single market that will create many new jobs in both the applicant countries and the incumbent member states. Looking from perspective of European companies, they are looking forward to seeing more states on the EU single market that would possibly reduce the risk of doing business in the other half of the continent. In the long-run, the applicant countries will need help from the EU in order to increase private investment that will meet the EU environment and transport standards. The main economic benefit of EU membership is a potential improvement in the investment climate of the Southeast European countries. When the SEEC’s join the EU, participation in the single market should involve the end of contingent protection (anti-dumping and safeguards). In1999, the total number of antidumping investigations opened was 86 (Nello, 2002, p. 296). It is obvious that some countries will be better-off and some worse-off. Bosnia can increase the sale of its domestic tobacco company`s products if it proves that the Croatian tobacco company is dumping in Bosnia (this case was speculated in media in 2010). The EU imposes environmental regulations that take into account environmental quality protection, production processes, and products (Tupy, 2003, p. 9). One of the significant benefits will be quality in the countries of South-eastern Europe. It will affect local producers, and decrease their profits, but more importantly, the customers will be better-off. Besides, citizens will enjoy higher air quality, water protection, pollution controls, and all other things that create negative externality. For instance, people from Zenica (Bosnia and Herzegovina) could have higher air quality, when Environmental Regulation Agency introduces pollution control to Metal company. 8.GRAVITY MODEL 8.1. Gravity Model of EU-15 (including Bulgaria and Romania) and SEEC’s The aim of this research is to analyze trade patterns between the EU and SEEC’s. Thus, the research devotes much time analyzing trade between the European Union and South-eastern Europe, and it uses one of the most popular models in International Economics – Gravity model. Therefore, it is attractive to evaluate whether there could be a potential for trade growth between the two groups. The answer to this question can be obtained by estimating a gravity model of trade. Such model as gravity is very often applied to research trade patterns between countries. The model that is used in this research is very similar to one that Montanari (2005) uses while explanating trade patterns. Gravity model describes bilateral trade patterns in accordance with socioeconomic and geographical characteristics of the 197 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo countries measured (Montanari, 2005, p. 60). The gravity model is used to measure bilateral trade flows between EU-15 and SEEC’s. The countries included in the analysis are divided into two groups. First group consists of the EU-15, while the other includes countries from the South-eastern Europe. Even if, Bulgaria and Romania are the members of the SEEC’s, the research uses them as the EU members, to get more precise results. EU-15 does not include Bulgaria and Romania, but EU-27 does, so we obtain a real potential of the SEEC’s only if we include two countries into the first group. Greece was a part of the EU-15, and, therefore, it counts in the first group of the model. This research uses the data as from 2010. Thus, there are separate indicators for Montenegro and Serbia. Due to lack of data, the analysis excludes Kosovo. The equation of the Gravity model is as follows: (1) : is the export flows from EU-15 countries (including Bulgaria and Romania) to SEEC’s. We take data for exports (2010) that are measured in current US Dollars, Millions. : represents the GDP of the exporting country expressed in current US Dollars, Millions. : is the GDP of the importer country expressed in current US Dollars, Millions. : is the distance between capitals of exporter and importer in kilometres. : is the dummy which has 1 if exporter and importer share a common border and 0 otherwise. : is the dummy variable which is 1 if exporter and importer countries use a common currency and 0 otherwise. : is a dummy variable which takes 1 for a specific importer and exporter country and is used to capture the effects of any political, historical or cultural event between two countries. : describes error term. Appendix A mentions data sources used in the model and explain the functions of the gravity model. The model says that trade increases if countries have the same border, or they use the same currency. In a case of signing a bilateral agreement, like CEFTA, the trade barriers decrease and countries trade more. 8.2.The Basic Gravity Model Table 5, which consists of 4 models, explains patterns of trade between the EU countries and SEEC’s. In the first model, GDP of exporter country seems to have a positive effect on trade flows and its coefficient of 1.06 shows that when GDP of exporter increases by 1%, its exports increases by 1.06%. Similar result is given for the importer, where the coefficient of 0.78 shows the increase in trade flows when GDP of importer increases by 1%. There is negative coefficient for the distance saying that when countries are further they trade less. In order to decrease the cost of transportation, those countries trade mostly with neighbouring 198 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo countries, and the model says that 1% increase in distance will decrease trade by 2.39%. Besides that, the model uses dummies such as common borders and currency. While common borders have a positive impact on trade with coefficient of 0.53, the common currency has a negative effect with -0.62. This negative dummy might be a result of not using Euro. Montenegro is the only one, who is a member of the Euro zone. The value of R2, 86%, shows that this model explains 86% of the variation in trade flows between the EU and SEEC’s. Table 5 Gravity Model: Regression Results Variable Model 1 lngdpexp 1.06 (0.07) 0.78 (0.11) -2.39 (0.18) 0.53 (0.35) -0.62 (0.30) lngdpimp lndist BOR CUR bilateral Model 2 Model 3 1.06 (0.07) 0.78 (0.11) -2.34 (0.20) 0.58 (0.36) -0.64 (0.30) 0.00 (0.00) expfix 1.06 (0.07) 0.89 (0.09) -2.52 (0.17) 1.06 (0.07) 0.76 (0.11) -2.33 (0.20) 0.58 (0.36) -0.74 (0.33) -0.01 (0.02) 0.04 (0.05) -11.54 3.36 0.86 0.81 252.13 273.05 101 cons -11.30 (3.28) -11.72 (3.35) 0.00 (0.02) -0.01 (0.05) -13.08 (3.07) R2 RMSE AIC BIC N 0.86 0.81 249.31 265.00 101 0.86 0.81 250.81 269.11 101 0.85 0.84 255.99 271.68 101 impfix Model 4 8.3.Bilateral Effects Model The equation used to estimate column 2 is: (2) where is a dummy variable which takes 1 for a specific importer and exporter country and is used to capture the effects of any political, historical or cultural event between two countries on their trade flows, and all other variables are the same as Equation 1. Second model has the same coefficient of GDP as the first one. Thus, if there is any increase in GDP of exporter and importer countries, the trade would also boost. In the second model, there is a slightly lower coefficient of distance, because the bilateral variable is included. The 199 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo coefficient of common borders says that trade increases if two countries share the same border. 8.4.Main Effects Model (Exporter and Importer Fixed Effects) The equation used to estimate Model 3 is: (3) where equals 1 whenever a country is exporting and 0 otherwise and equals 1 whenever a country is importing and 0 otherwise. These dummies might differ depending on the countries’ tendency to export and import. This model does not include dummies and bilateral effects variables, but it has exporter and importer fixed effects. In general, the model does not give a better picture; R2 is lower than the first and second models, and even, Akaike`s information criteria (AIC) are worse. 8.5.Main Effects Model with Dummies When we enlarge Model 3 with common border and common currency variables, the model becomes the following: (4) The model has a higher R2 with 86%. The distance has a negative coefficient as usual. The model says that neighbouring countries trade more with each other than other countries. The results show that trade increases with economic size, measured by GDP in our model while it decreases with distance between them. This kind of model could be very useful for analyzing international trade; it is seen in the straightforwardness of explanation of trade patterns that can be used to test the impact of new policy measures. 8.6.Results Measuring overall economic impact of EU-enlargement is almost impossible task given that there are problems of global economy, uncertainties, which could change the whole process. The main benefits of enlargement for incumbent countries are not economic, but rather they are related to stability and security. The economic benefits to the EU-27 will not be significant in the short-run, neither the costs. However, in the long-run the whole European economy will gain significantly from enlargement. Table 5 presents four different models, where each of them consists of various variables. Each model uses GDP of exporting and importing countries. By becoming a part of the single market, there would be increase in the outputs and the growth of imports and exports that lead to an increase in GDP. As a result, according to the results of our models, if SEEC’s access the union, GDP’s will become higher, trade will increase, and at the end export flows become larger. Therefore, households would benefit from the European enlargement and from the 200 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo removal of tariffs. Removal of tariffs would lead to reduction in import prices, and will affect the allocation of household income. The removal of trade barriers would have a clear impact on price setting. As a result, the scenario would be a reduction in prices. Conversely, the increased demand should be taken into account, together with the removal of trade tariffs, which at the end will provide a positive output. The European Union has to manage enlargement appropriately if it wants to gather all the potential benefits. Flourishing management depends on developing political strategy that is behaving in interest of enlargement as a way of gains for the public and for interest groups. Comparing previous enlargements and next ones, there are significant differences. For instance, Greece, Portugal and Spain became members of the EU before the single market and monetary union programs were implemented. Thus, they became members when the EU was a much less integrated and smaller market. Today, the EU economy is experienced and it has a faster growth trade than it was in time of accession period of Portugal and Spain. The main benefits of enlargement for the SEEC’s are not only economic, but they are more oriented to provide stability and security. The major risks are concerns of large migration flows, wage competition, and the costs to the EU’s budget. Results show that there is a room for trade to increase, especially, in neighbouring countries. SEEC’s would have to invest in new technology in order to be competitive for the EU single market. 9.POLICY SUGGESTIONS Yugoslavia was located in the South-eastern Europe, in the heart of the Balkan Peninsula. The heart connected two continents, Europe with Asia, and was the gate to the Black Sea. The country had resources and good geographical position to grow and become super power of Europe. Unfortunately, the country had not been unable to run resources properly. The economy of Yugoslavia was oriented toward agriculture, so the whole national prosperity depended on the development of agriculture. The character of Yugoslavia is seen in the fact that out of 24,849,425 hectares of the whole territory, 11,500,000 hectares, or 46 per cent, account for agriculture (Roucek, 1933, p. 414). The country produced hemp, cotton, hops, opium, tobacco, etc. All these, and many other products, were high quality. The important thing is that no single part of Yugoslavia produced all these products. The country consisted of 7 parts that are today independent countries (Bosnia and Herzegovina, Croatia, Kosovo, Macedonia, Montenegro, Slovenia, and Serbia). Hence, each part of Yugoslavia was famous for the production of a particular good. Exports went mainly to France, Germany, and Switzerland. The interesting thing is that each province of Yugoslavia has its own special kinds of fruits. Different fruits and variety of foods and beverages are produced in a way that is traditional for every region of the country. Besides that, there were plenty of mineral resources such as coal, iron, copper, etc. Yugoslavia could, by its richness of iron, take a place as one of the leading countries in Europe. The main importers of Yugoslavian products were Italy (28.31%), Austria (17.68%), Germany (11.66%), Hungary (7.18 per cent), Greece (6.05%), etc. (Roucek, 1933, p. 420). The main products of exports were wood, cement, cereals, and ores. The economic situation of Yugoslavia gave a real situation of the country, the country`s potential, and prosperity. Today, the former parts of Yugoslavia need a stabilization process 201 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo that would bring them to previous conditions. It is obvious that countries need capital and investment in infrastructure. Taking all of these facts into consideration, a solution for the SEEC’s, among the one of the European Union, might be the creation of the Balkan Union. The union would consist of 7 countries (Albania, Bosnia and Herzegovina, Croatia, Kosovo, Macedonia, Montenegro, and Serbia). In that case, the members of the Union would be oriented towards a kind of closed economy. They would trade more between each other, and try to decrease imports from countries out of the Balkan Union. The construction of the Balkan Union would look like the European Union. There would be institutions to regulate the union, but mainly, ones for culture, education, and trade. If each member specializes in production of particular goods and services, and achieves comparative advantage with economies of scale, then that product would be easy to sell, or to exchange for something else, of course from one of the Union members. The Union would need to have a supervisor. Currently, the only state that has incentives and interest to regulate these countries is Turkey. If all countries agreed, Turkey would be the supervisor and manager of the Union. Even more, as a state with high FDI in South-eastern Europe, Turkey will be responsible for infrastructure, growth and development of the Union. Developing countries, as “members” of the Balkan Union, must diversify their productive structure and strengthen domestic demand. People should buy more domestic products, and stop buying the similar foreign products, thinking foreign is better. Still, there are many foreign products that are better, but at least beverages and foods have to be bought from domestic producers. Today, the countries not only depend on agriculture and manufacturing, but on tourism. Thus, economic policy for each country should be taking advantages of potential, which they have. There are countries such as Bosnia and Herzegovina, and Montenegro that should be oriented towards tourism, specifically winter and summer tourism. Recently, Minister of Turkey, Rifat Sait has called for establishing of Balkan Parliament, where, besides Turkey, there would be Albania, Bosnia and Herzegovina, Bulgaria, Greece, Kosovo, Macedonia, Montenegro, Romania, and Serbia (Bojadžić, 2011). With headquarters in Izmir, the initiative of Turkey and its leading party, AK party, would welcome academics, NGO, politicians, journalists, writers, representatives of private sector, etc. who could invest money in development of Balkans. The idea might seem unachievable, where some countries could reject supervision of Turkey, but it is the only country that does not look at historical problems that occurred on the Balkans, and it wants to establish regular connections with each state from the Balkans. 10.CONCLUSION This study discusses economic costs and benefits of South-eastern enlargement of the European Union. The idea of the united Europe is not a recent idea. Thus, the research starts with brief overview of the idea of the European Union and its objectives. The purpose was to maintain a peaceful and prosperous life throughout Europe. At the beginning of the fifties in the last century, the EU has signed many treaties and brought new policies that would ensure a zone for free capital movement. Besides that, the Union had five enlargements and introduced a common currency. Creation of a single market, which today consists of 501 million of consumers, is to provide a better life for every citizen. 202 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Next enlargement is of the South-east European countries. After accession of the SEEC’s, the European Union will have more than 30 members, with diverse cultures, histories and languages. Can such a diverse union of nations create a common political “union”? The EU was a trial to unify Europe, but it is obvious that it is difficult, since it is impossible to connect Germany or Sweden with, let say, Mediterranean, and there is no surprise for the failure of Greece and Italy. Can citizens of the EU establish a sense of being European while deeply belonging to their country? In essence, they can, if incumbent members follow the example of the first European Community. The moral legitimacy of the European Community is based on compromise, while consolidating the peace between former enemies. It stayed within the principle that all members, large and small, had equivalent rights and respected minorities. Therefore, the next enlargement should bring equivalence to small countries, and more important, stable market. EU member states account for almost 1/3 of the entire global economy, so in that sense the common market is the preferable mean to the global market. The research uses the Gravity model to test the trade relations between EU-15 (inclusive Bulgaria and Romania) and SEEC’s. After adding dummy variables such as common borders and currency to the model, the results show that there is a space for growth of trade between them. Trade is positively affected by GDP of exporters and importers. Larger GDP means higher production and increased ability for trade. However, distance has negative effect, and in this model it decreases trade if country is far away from partner. On the other hand, common borders positively affect trade, so by diminishing trade barriers, quotas, and taxes countries could stimulate trade to grow. Since Montenegro is the only one who uses Euro, the currency seems to affect trade negatively, and this shows that if any of SEEC’s adopts Euro it would stimulate trade. Current situation of the EU shows that high unemployment is present in many EU countries, so the EU has to be focused on achieving growth and creating jobs. In order to make its economies more dynamic and increase social cohesion, Europe must invest more in research and innovation, education and training. Thus, President of the European Commission presented a strategy for next 10 years, which is called the Europe 2020 strategy. 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APPENDIX DATA AND VARIABLES USED FOR THE ESTIMATION OF THE GRAVITY MODEL The reporting countries that are used for the analysis are the members of the EU-15 (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, and United Kingdom). Since the Belgium and Romania are not parts of the EU-15, they are not included in the South-east European countries, but they are attached to the first group, the EU-15. There are six partner countries: Albania, Bosnia and Herzegovina, Croatia, Macedonia, Montenegro, and Serbia. The data GDP and population were taken from the World Bank’s World Development Indicators. First two figures are measured in current US Dollars, Millions. Bilateral trade flows, imports and exports, were taken from the IMF’s Direction of Trade Statistics, year 2010. Thus, the reference year for estimating potential trade between the EU-15 + (BG and RO) and the South-east Europe is 2010. Distances between capital cities of the countries were taken from www.viamichelin.co.uk. The most used routes for transportation of goods by trucks and by ship (in case of Italy and its partner countries) are taken for analysis. Dummy variable BOR takes a value of 1 if country of EU-15 and its partner share a common border, 0 otherwise. Dummy variable CURR takes a value of 1 if country of EU-15 and its partner uses a common currency, 0 otherwise. 204 � Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Extent The size or duration of the resource. 1325 Title A name given to the resource Economic Costs And Benefits Of The Eu Enlargement: The Impact On The Eu And Seec’s Author Author Kurtagić , Haris Abstract A summary of the resource. The South-eastern enlargement of the European Union will be the sixth enlargement since establishing the European Community in 1957. The research uses the Gravity model, and measures the factors that have an influence on trade. The Gravity model involves coefficients that explain the pattern of trade with GDP, geographical distance, population, and several dummy variables. Trade that is explained by Gravity model includes two regions, EU-15 (inclusive Bulgaria and Romania) and SEEC’s. The reason why Bulgaria and Romania are included, even if they are part of the SEEC’s, is to acquire as accurate pattern of trade as possible. Comparing the data from 2010, the gravity model describes trade flows between 23 countries. Thus, the purpose of this study is to analyze trade flows between two regions. Taking into consideration the costs of enlargement, this research examines the effects of the trade, its significance on the development of SEEC’s after enlargement, well-being of countries that are not part of the EU, as well as it offers a solution for the South-east European countries. Therefore, the solution that this research proposes is a model based on creation of the Balkan Union. Keywords: EU-Enlargement, Gravity model, South-eastern Europe, European union, Trade flows. Date A point or period of time associated with an event in the lifecycle of the resource 2012-05-31 Keywords Keywords. Conference or Workshop Item PeerReviewed H Social Sciences (General),HB Economic Theory,HG Finance,HJ Public Finance