283 10 3494 https://omeka.ibu.edu.ba/files/original/6ffb473f8036bbc34bd9f2d98ebce125.docx c9994af7d67e336b06da159dd9062e92 https://omeka.ibu.edu.ba/files/original/72531860bc9322be563b0f59cc11e812.pdf 224941cdb284f3c51f12fc780e9c9886 PDF Text Text The Factor of Beliefs About Language Learning in Bosnia and Herzegovina. Isak Ozturk International Burch University/ Sarajevo, Bosnia and Herzegovina Key words:Language learning beloefs; language learning strategies; motivation, attitude;personality ABSTRACT The aim of this paper is to study the factor of the Beliefs About Language Learning Inventory (BALLI) and to investigate a sample of 200 Bosnian remote high school EFL learners’ language learning beliefs, their learning strategies, and the relationship between learners’ beliefs and their use of strategies and compare them with the EFL learners in the downtown city schools. This study will also examine the influence of learning variables such as aptitude, attitudes, motivation, anxiety, personality on learner beliefs and strategies. Data will be collected by using questionnaire; the Beliefs about Language Learning Inventory (BALLI) by Elaine Horwitz, the University of Texas at Austin. The findings will help teachers to have some pedagogical implications to increase students’ level of motivation in an English language classroom. For example teachers can set goals for students in learning English, provide required materials regarding language learning, and inspire students to learn. These findings may also help students to improve their level of English and encourage them to study harder. Horwitz (1988) suggests that better understanding of students' beliefs of language learning may allow language teachers to better understand students' expectations and satisfactions with their language class. Once students are able to face their beliefs, they may understand their weakness and try to solve the problem. � 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. 1799 Title A name given to the resource The Factor of Beliefs About Language Learning in Bosnia and Herzegovina. Author Author OZTURK, Isak Abstract A summary of the resource. Key words:Language learning beloefs; language learning strategies; motivation, attitude;personality ABSTRACT The aim of this paper is to study the factor of the Beliefs About Language Learning Inventory (BALLI) and to investigate a sample of 200 Bosnian remote high school EFL learners’ language learning beliefs, their learning strategies, and the relationship between learners’ beliefs and their use of strategies and compare them with the EFL learners in the downtown city schools. This study will also examine the influence of learning variables such as aptitude, attitudes, motivation, anxiety, personality on learner beliefs and strategies. Data will be collected by using questionnaire; the Beliefs about Language Learning Inventory (BALLI) by Elaine Horwitz, the University of Texas at Austin. The findings will help teachers to have some pedagogical implications to increase students’ level of motivation in an English language classroom. For example teachers can set goals for students in learning English, provide required materials regarding language learning, and inspire students to learn. These findings may also help students to improve their level of English and encourage them to study harder. Horwitz (1988) suggests that better understanding of students' beliefs of language learning may allow language teachers to better understand students' expectations and satisfactions with their language class. Once students are able to face their beliefs, they may understand their weakness and try to solve the problem. Publisher An entity responsible for making the resource available IBU Publishing Date A point or period of time associated with an event in the lifecycle of the resource 2013-05-03 Keywords Keywords. Article PeerReviewed https://omeka.ibu.edu.ba/files/original/5416f38c974462981311f535403f5529.pdf 09cd9cf7cbc55809bffe2e9767231296 PDF Text Text SCHEIN, Edgar H, “Organizational Culture”, American Psychologist, Vol. 45, No. 2, pp. 109-119, 1990. SIMSEK, M. Şerif, H. Serdar Öge, Human Resources Management with Strategic and International Perspectives, Gazi Publishing, Ankara, 2007. SPOKANE, Arnold R., Eric J. Luchetta and Matthew H. Richwine, “Holland’s Theory of Personalities in Work Environments”, Career Choices and Development, Jossey-Bass, San Francisco, pp. 373-427, 2002. SOUTGATE, Nicole, An Exploration of Career Salience, Career Commitment, and Job Involvement, Master Thessis, Masters in Industrial Psychology at University of the Witwatersrand, Supervisor: Dr. Andrew Thatcher, 2005. TUZ, Melek Vergiliel, “Kariyer Planlamasında Yeni Yaklaşımlar”, U.Ü. Journal of Science and Literature Faculty, Vol. 4, No. 4, pp. 169-176, 2003. TYLOR, Edward B., Primitive Culture: Researches Into the Development of Mythology, Philosophy, Religion, Art, and Custom, Volume I, John Murray Albemarle Street, London, 1871 YOUNG, Richard A, Ladislav Valach and Audrey Collin, “A Contextualist Explanation of Career”, Career Choices and Development, Jossey-Bass, San Francisco, pp. 206-255, 2002. The Factors Determined To The Improvement In The Least Developed And Developing Countries: Testing A Model Gözde Ergin, Adil Oğuzhan Trakya University, Department of Econometrics Abstract Finding the different ways of the improvement as a multidimensional process causes different improvement ways in all countries in the world. The economic improvement that cause a structural changing is very important in all economies all over the world and it is necessary for the least developed countries at the same time. These countries have solved the phenomena of poverty, unemployment, low life standards and unimproved. The differentiation in the socio-cultural structures of the least developed and developing countries effect the improvement in a positive way. In the study, the socio-economic factors of improvement and a classification according to the gross national product levels per person in the least developed and developing countries have been done by taking the definition accepted by World Bank into consideration. There are fifteen countries in the classification of the least developed and developing countries. The data of thirty-three factors in the comparison of these countries have been obtained from the data source of World Bank, OECD, EUROSTAT and UN (2000 – 2009). 610 �The Statistical and Casual Models in the kinds of econometric models have been estimated with ‘’Panel Model Method’’. For choosing the suitable model, the test for choosing model ‘’Hausman’’ has been used. As a result, the factors determined to the improvement of the countries in a different improvement levels have been discussed and the comments related to them have been made. 1.INTRODUCTION The concept of economic development could be defined as the process of increasing of material wellbeing, abolishing the poverty, the input in production and the usage of these outputs as a result and besides, as an activity of the protections of the level of the socioeconomical standards of the society in order to use them more actively and with different methods for the production process. The problems of individuals and the world increase gradually because of the increase of the world population and the globalization. In todays world, where the incomes of the individuals raise, the distribution of income gets worse and the poverty increases, the importance of the development problematic dramatically increased. In a world which gets smaller with the expansion of communication tools due to globalization, the level of information acquisition of both countries and individuals has risen. Therefore, solutions are sought through development policies for the alternating lifestyles, economies and the differentiation in countries’ and individuals’ socio-cultural structures. Development, having multiple aspects, has various angles and these bring about different development periods in each country. For that reason, development is defined in different ways by various people and thinkers. Economic Development is very important in every economies and it nearly becomes compulsory for the low-level of development countries. Because, these countries can only find a solution for poverty, low-living standarts and backwardness entity with economic development. But on the other hand when examining for advanced tecnology, development shows necessity for maintaining current growth rate in advanced tecnologies (Jain & Ohri, 2007:2) Development carries meaning of recovering economy and in any case of negativities for underdeveloped countries. As known,it should be tried to be developed and handled with all defects in this issue and origins of these defects for achive successing in a weak issue. The biggest step of developing should be provided the process of developing with handle by looking cause and effects of deficincies in economy. Development not only developt in terms of economy, but also known as social and politically changes and positive contributions of these changes. 611 �2.Development Theories After from 1950s, a lot of development theories suggested to the world.(boyacıoğlu, 200:2728). The major ones of these theories are known as The Development Theory of Rostow, The Balanced Development Theory and Unbalanced Development Theories. According to Rostow’s development theory in 1960, the development countries are the countries surpassed the stage of traditional society,transition,rising,maturity, and the mass consumption. In the countries which is in the stage of traditional society occur an intense agricultural sector and the functions of the limited production and modern scientific-technical practices. Education and infrastructure investment in the transition stage society have a dramatically increasing and bring about new initiatives. .In rising stage the composed profit returns to investment and technology is started to use successfully at all sectors. Anymore, the societies in the maturity stage use their sources in the areas having modern technology. While their production and exportation are increasing, parallelly the requiremenst for new import goods are increasing, as well. As for in the stage of mass consumption, the per capita income arise and the society starts concentrating on consumption rather than production.Impetus between these stages is the expediting economic growth as returning internal and external austerity to enough amount investment. (Dolun ve Atik, 2006: 8). Balanced development aims a condition of equilibrium in the economy.The economic events occured in the underdeveloped society rely on the complementarity link.In terms of thought, complementarity is the important factor of the balanced development and it is not an instrument to realize the balance situation but it is an directive item.The balanced development model rests on the mutual dependence.As a first, it is the mutual dependence in production.On one hand, every economic group have to find income and look for the market for its outcome .As a second, it is that every income growth create an enhancement in demand. The balanced development with balance, food products with clothing, agricultural feedstock with industrial products, public enterprises with other investments and such as production for the export and domestic demand are asked to arised for many other ecomomic situations . According to Rosentein-Roden respected the pioneer of balanced development in order to increase income and demand are needed benefical and healthy investments.Concerted investments are going to increase income and demand.Thus, investment in parts is not enough both increasing demand and income.Overall, coordinated investments in Rosentein-Roden model supplies with the external savings. With the aim of comparing economic development and economic growth, an organized schedule is given below. With the aim of comparing economic development and economic growth, an organized schedule is given below. 612 �Content Economic Development Economic Growth Economic Development refers to either mutations savings and national (mutations betwen institutional and tecnologic) frame or progressive mutations of economic structure. Economic Growth is an increase in capacity of an ecenomy to produce goods and services like investment, savings and revenues. Economic Development refers to benefit from unused resources in underdevelop countries. Use Economic Growth is related with development of low-used sources from developed contries to use in optimum way. Development, equilibrium rate is connected with the raising of high steady state. .Growth is connected with general steady and graduaded raise at the rate of investment and outcome. Economic Development implies the problems of underdevelopment countries. Economic Growth implies the problem of developed countries. Action Creates both qualitative and quantitative mutations in economy. Creates only quantitative mutations economy. Scope Connected with all mutations in economy. Connected with small motations in economy. Boost (büyüme) Definition 3. Panel Data Analysis When T numbered observations of N numbered econometric units are dealed together establish panel data model. Assets belonging to any year establish the cross section of the panel; the assets the economic units take by years establish the time sector. In other words, across every econometric unit there is a time series. Panel data analysis model is the model where economic relations are presumed using time sector cross. (Powel, 2010: 1). 4. A Model Test Regarding Development Factors Affecting Development at Underdeveloped and Developing Countries Taking into consideration the development factors affecting the development of developing and underdeveloped countries with the condition of benefiting from panel data, the Socio-Economic variables of countries taking place at the panel model are defined as below. X 1 : Research- Development Cost GDP % X 2 : GDP Per Capita(Year ) X 3 : Rural Population’s % Among Total Population X 4 : The Rate of Urban Poplation in the Overall Population X 5 : Death Rate ( 1000 Person) 613 X 6 : Tax Revenue GDP % �X 7 : İnfant Death Rate (1000 İnfant) X 8 : Agricultural Rate % in GDP X 9 : Service Sector % in GDP X 10 : Industrial Sector % in GDP X 11 : Import of Good and Service % in GDP % X 27 :FDI %in Net Capital Inflow X 12 : Export of Good and Service % in GDP % X 13 : GDP Rate X 14 : Real Inflation Rate X 15 : Unemployment Rate X 17 : The Number of Scientfic Article X 19 : Expectancy of Life (Year) X 18 : Electrical Consumption Per Capita X 20 : Inflation Rate X 22 : Cultiroted Land (Hek.) X 23 : The Rate of Employed In Industrial Sector X 24 : The Rate of Employed In Service Sector X 25 : Dependency Rate X 28 : Comminication Revenue X 29 : Energy Import % in GDP X 30 : The Rate of Big Urban in Over Population X 31 : Women at the Parliament X 32 : The Rate of Population ( Year) X 33 : GDP per Capita($) 4.1. Approximation Results According to Panel Model of Underdeveloped Countries Under this chapter underdeveloped countries are Uzbekistan; Kyrgyzstan; Ethiopia; Kenya, Nepal, Bangladesh and Afghanistan. These countries are considered as underdeveloped ones according World Bank’s definitions. For these countries different approximation models of social and economic sector will be tested. Table 1: Approximation Results of Underdeveloped Countries Social Sector According to Panel Model Model I Constant Effective Model Variables C X19? X25? X30? X31? Random Effective Model Coefficients Coefficients -6.672591 (0.2953) 3.505694 (0.0047) -1.995913 (0.0007) 1.940735 (0.0044) 0.103846 (0.0518) -1.786856 (0.7641) 2.428435 (0.0139) -1.605449 (0.0037) 1.279782 (0.0045) 0.132759 (0.0077) Random Effects (Cross 0.136976 Fixed Effects (Cross) _UZB--C 0.099859 614 Model II Model III Model IV Model V Pooled Least (LSDV)Model Fit Panel Data Model using GLS, removing Autocorrelation and homoscedasticity Robust Score Coefficients Coefficients Coefficients 3.505694 (0.0047) -1.995913 (0.0007) 1.940735 (0.0044) 0.103846 (0.0518) 8.353807 (0.000) .5925234 (0.000) -1.352025 (0.000) .1943402 (0.000) .05734 (0.000) -1.516349 (0.897) 2.367225 (0.166) -1.583195 (0.014) 1.244267 (0.195) .1343365 (0.015) -6.572731 - - - �_KIR--C _ETOP--C _KEN--C _NEPAL--C _BANG--C _AFG--C -1.402104 0.571702 0.641290 0.470263 -0.696803 0.315794 -0.869378 0.178319 0.633556 0.270047 -0.483477 0.133956 -8.074695 -6.100889 -6.031301 -6.202328 -7.369394 -6.356797 - - R 2 0.847275 0.506778 0.847275 - - R 2 0.821389 0.476426 0.821389 - - Se 0.215075 0.220746 0.215075 - - ∑ e2i 2.729168 3.167381 2.729168 - - 14.23175 - 14.23175 62.08698 - 32.73143 16.69666 32.731 - - 0.000000 0.000000 0.000000 - - -0.092336 - -0.092336 - - 0.260999 - 0.260999 - - 0.048013 - 0.048013 - - 1.167119 - - Log likelihood F-statistic Prob(Fstatistic) Akaike info criterion Schwarz criterion HannanQuinn criter. DurbinWatson stat Wald-ist. LM corr(u_i,Xb) F u_i=0 sigma_u sigma_e Rho 1.167119 1.009085 - 66.79 - 399.78 - -0.8864 25.72 .76597963 .21507476 108.83 0 (assumed) .48702719 .21507476 - - - .92692184 .83680818 - - - Table2: Panel Model of Approximation Results Economic Development Sector of Underdeveloped Countries’ Model I Constant Effective Model Variables C 615 Coefficients -21.94974 (0.0544) Model II Model III Model IV Fit Panel Data Model using GLS,removing Autocorrelation and homoscedasticity Model V Random Effective Model Pooled Least (LSDV)Model Coefficients Coefficients Coefficients Coefficients 8.061894 (0.0137) - 10.48043 (0.000) 8.83934 (0.000) Robust Score �X22? 1.808341 (0.0157) Fixed Effects (Cross) _UZB--C 0.570444 _KIR—C 2.574761 _ETOP—C -2.399361 _KEN--C 0.266447 _NEPAL— 1.172460 C _BANG--C -0.874519 _AFG--C -1.310231 -0.144044 (0.4889) Random Effects (Cross 0.416194 0.075481 -0.598519 0.407666 1.808341 -.3189611 (0.000) -.1946205 (0.025) -21.37930 -19.37498 -24.34911 -21.68330 - - -0.181637 -20.77728 - - 0.137800 -0.256984 -22.82426 -23.25998 - - R 2 0.684503 0.006429 0.684503 - - R 2 0.648883 -0.008183 0.648883 - - 0.301551 0.316311 0.301551 - - 5.637852 6.803588 5.637852 - - -11.16097 - -11.16097 50.3867 - 19.21651 0.439978 19.21651 - - 0.000000 0.509375 0.00000 - - 0.547456 - 0.547456 - - 0.804427 - 0.804427 - - 0.649528 - 0.649528 - - 0.663113 0.459701 0.663113 - - - 0.44 82.60 - 256.57 - 5.04 - -0.9727 0 (assumed) - - - 16.40 1.6653277 .30155042 .96825253 .39296918 .30155042 .62938698 - - - Se ∑ 2 ei Log likelihood F-statistic Prob(Fstatistic) Akaike info criterion Schwarz criterion HannanQuinn criter. DurbinWatson stat Wald-ist. LM corr(u_i, Xb) F u_i=0 sigma_u sigma_e Rho 4.2. A Model Test According to the Economic Development of Underdeveloped Countries When the economic factors dimension of improvement models of underdeveloped countries is seen as a panel model, Constant Effective Model has been estimated as a first model. In the estimation of this model all economic variables has been added to the model as explanatory variables. 616 �The Hausman Test was applied to understand which model is more coherent at the above approximated Fixed Effect Cross Model and Random Effects. The results are below. Table 3: Hausman Determination Model Test Results Correlated Random Effects - Hausman Test Pool: Untitled Test cross-section random effects Chi-Sq. Statistic Chi-Sq. d.f. Test Summary Cross-section random 7.819711 Prob. 1 0.0052 Because the test result is p<0.05 the hypothesis is denied and FEM is preferred. In addition the  i of the countries statistic meaning test is approximated at LSDV model III. 4.3. Panel Model Approximation Results of Developing Countries Under this chapter as developing countries; Azerbaijan, Argentina, Brazil, Bulgaria, China, Mexico, Turkey and Kazakhstan are taken. These countries are considered as developing ones according World Bank’s definitions. For these countries different approximation models of social and economic sector will be tested. Table 4: Panel Model of Approximation Results Economic Development Sector of Developing Countries Model I Model II Constant Effective Model Variables C X8? X18? Coefficients 1.205352 (0.5830) -1.417998 (0.0000) 1.281596 (0.0000) Fixed Effects (Cross) _AZER— -0.481379 C _ARJ--C 0.479222 _BRE--C 0.072678 _BULG— -0.609218 C _CHN--C 0.386770 617 Model III Model IV Model V Random Effective Model Pooled Least (LSDV)Model Fit Panel Data Model using GLS, removing Autocorrelation and homoscedasticity Robust Score Coefficients Coefficients Coefficients Coefficients -1.417998 (0.0000) 1.281596 (0.0000) 8.461126 (0.000) -1.315091 (0.000) .3027834 (0.010) 2.974038 (0.332) -1.443142 (0.000) 1.059926 (0.004) -0.460849 0.723973 - - 0.464491 0.013988 1.684574 1.278031 - - -0.427305 0.596134 - - 0.265768 1.592122 - - 3.536970 (0.0721) -1.449383 (0.0000) 0.988897 (0.0001) Random Effects (Cross - �_MEK--C _TC--C _KAZ--C 0.019974 1.092678 -0.960724 -0.059541 0.996506 -0.793057 1.225326 2.298030 0.244628 - - R 2 0.868931 0.679488 0.883863 - - R 2 0.260507 0.671163 0.868931 - - 4.750457 0.268295 0.260507 - - -0.563648 5.542612 4.750457 - - 59.19276 - -0.563648 9.971518 0.000000 81.62015 59.19276 - - - 0.000000 0.000000 - - 0.264091 - 0.264091 - - 0.561845 - 0.561845 - - 0.383469 - 0.383469 - - 1.472890 1.202092 1.472890 - - - 163.24 129.86 - 174.48 - 76.11 - -0.5420 0 (assumed) - - - 29.98 .66598321 .26050657 .86729755 .4772026 .26050657 .77040971 - - - Se ∑ 2 ei Log likelihood F-statistic Prob(Fstatistic) Akaike info criterion Schwarz criterion HannanQuinn criter. DurbinWatson stat Wald-ist. LM corr(u_i, Xb) F u_i=0 sigma_u sigma_e Rho Table 5. Hausman Determination Model Test Results Correlated Random Effects - Hausman Test Pool: Untitled Test cross-section random effects Test Summary Cross-section random Chi-Sq. Statistic Chi-Sq. d.f. 6.672753 2 Prob. 0.0356 Because the test result is p<0.05 the hypothesis is denied and FEM is preferred. In addition the  i of the countries statistic meaning test is approximated at LSDV model III. 618 �Table 6: Panel Model of Approximation Results Social Sector of Developing Countries Model I Model II Model III Pooled Least Sabit Etkili Model Tesadüfi Etkili Model (LSDV)Model Katsayılar Katsayılar Katsayılar 11.25996 30.32038 Değişkenler Model IV Model V Fit Panel Data Model using GLS, removing Autocorrelation and homoscedasticity Robust Score Katsayılar Katsayılar 7.424574 31.62741 (0.000) (0.000) C X25? X30? (0.0407) (0.0000) -7.450049 -6.270002 -7.450049 .0323962 -6.833261 (0.0000) (0.0000) (0.0000) (0.868) (0.000) 9.174063 0.856127 9.174063 .2843438 1.173124 (0.0000) (0.0001) (0.0000) (0.000) (0.009) Fixed Effects (Cross) Random Effects (Cross _AZER--C -9.249312 -1.556559 2.010649 - - _ARJ--C -5.165218 0.628508 6.094744 - - _BRE--C 3.671319 0.593179 14.93128 - - _BULG--C -2.847708 -0.949942 8.412254 - - _CHN--C 14.45671 -0.236747 25.71667 - - _MEK--C -1.194810 1.310704 10.06515 - - _TC--C -0.779853 0.497350 10.48011 - - _KAZ--C 1.108873 -0.286492 12.36884 - - R2 0.886835 0.527576 0.886835 - - R2 0.872286 0.515305 0.872286 - - Se 0.257151 0.326793 0.257151 - - 619 �e 4.628865 8.223100 4.628865 - Log-Lik. 0.473513 - 0.473513 -100.2685 42.994 60.95 - - 2 i F -Statistic - Prob(F-statistic) 0.000000 0.000000 0.000000 - - Akaike info criterion 0.238162 - 0.238162 - - Schwarz criterion 0.535916 - 0.535916 - - Hannan-Quinn criter. 0.357540 - 0.357540 - - Durbin-Watson stat 1.493082 0.852996 1.493082 - - Wald-ist. - 85.99 - 311.09 72.20 LM - 58.48 - - - -0.9951 0 (assumed) - - - F u_i=0 71.25 - - - - sigma_u 7.0313029 .43614757 - - - sigma_e .25715142 .25715142 - - - rho .99866425 .74204622 - - - corr(u_i, Xb) 5.CONCLUSION The development of economies is possible trough achieving a better position of the accepted criteria and indicators of development. Societies and countries can be categorized among developed countries when they manage to realize the necessary conditions of development. The variation among development factors and socio-economic levels of countries has led to the establishment of categories of developed, under developed and developing countries. Development is a well-rounded process, thus because of its well rounded face the difference of development processes in each country is dissimilar. Economic development brings also structural change which is very important for every economy but in countries where the development level is rather low, is almost compulsory. Because these countries can bring solution to their poverty, unemployment, low level of living standard and underdevelopment trough economic development. The diversification of socio-cultural structure of underdeveloped countries affects positively the development. In these countries culture has limited effect upon economic actions and brings a slow development process. 620 �In developed countries development is a necessity to prolong existent growth rate. In these countries it is aimed to upgrade the living standards of people trough economic development. In developing countries the first target of development which is the skewness of the economy and inequality brings also poor level of living. In these countries the sociocultural development criteria are in low levels and the existence of a traditional cultural approach hinders development. According to the evaluations of social criteria of underdeveloped countries in this essay, life expectations, the rise number of women at the parliament and the increase of life percentages in metropole together with the decrease of dependence rate, affects positively the development. These factors have shown that they are an important step towards development level of the underdeveloped countries. It is arrived to conclusion that in undeveloped countries the decrease of rural population and exports has positive effects upon development. When we look to the suggestive variations of the social criteria model of developing countries, we see that while the increase of life percentages in metropole increases development, the increase of dependence rate has negative effects upon development. According to economic criteria, the increase of the agricultural sector at GDP affects negatively the development. The increase of per capita electric consumption is an important indicator of development for the developing countries. Therefore the increases in prosperity and positive economic activities are only possible trough economic development. In conclusion via development policies is possible to create more modern societies. REFERENCE Balgati, H. Badi, ‘’Forecasting With Panel Data’’, Journal of Forecasting, Wiley İnterScience, January, 2008, s.155-156 Balgati, H. 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Robert, ‘’A Primer of Panel Data Analysis’’, Social Sciences, Statistics & Mapping, Connet İnformation Tecnology at New York, September, 2003, s.1-2 Yavilioğlu Cengiz, ‘’Geri Kalmışlık Olgusu ve Ekonomistik Kalkınma Teorileri (Eleştirisel Bir Yaklaşım)’’, C.Ü. İktisadi ve İdari Bilimler Dergisi, Cilt:3, Sayı:2, Sivas, 2002, s.7-60 http://www.diffen.com/difference/Economic_Development_vs_Economic_Growth http://econ.worldbank.org/WBSITE/EXTERNAL/DATASTATISTICS/0,,contentMDK:2042 0458~isCURL:Y~pagePK:64133150~piPK:64133175~theSitePK:239419,00.html (01.10.2010) http://data.worldbank.org/indicator/SP.POP.GROW (27.09.2010) http://data.worldbank.org/indicator/SL.AGR.EMPL.FE.ZS(27.09.2010) 622 � 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. 1219 Title A name given to the resource The Factors Determined To The Improvement In The Least Developed And Developing Countries: Testing A Model Author Author Gözde , Ergin Abstract A summary of the resource. Finding the different ways of the improvement as a multidimensional process causes different improvement ways in all countries in the world. The economic improvement that cause a structural changing is very important in all economies all over the world and it is necessary for the least developed countries at the same time. These countries have solved the phenomena of poverty, unemployment, low life standards and unimproved. The differentiation in the socio-cultural structures of the least developed and developing countries effect the improvement in a positive way. In the study, the socio-economic factors of improvement and a classification according to the gross national product levels per person in the least developed and developing countries have been done by taking the definition accepted by World Bank into consideration. There are fifteen countries in the classification of the least developed and developing countries. The data of thirty-three factors in the comparison of these countries have been obtained from the data source of World Bank, OECD, EUROSTAT and UN (2000 – 2009). The Statistical and Casual Models in the kinds of econometric models have been estimated with ‘’Panel Model Method’’. For choosing the suitable model, the test for choosing model ‘’Hausman’’ has been used. As a result, the factors determined to the improvement of the countries in a different improvement levels have been discussed and the comments related to them have been made. 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/c3547f779adfcd7908143b7cd04299d8.pdf 673948985cfb974b53f467b82db3b817 PDF Text Text Journal of Economic and Social Studies The Factors Which Caused the Decline in the Amount of the Newly One Family Houses Sold in US Ali Cüneyt ÇETİN Suleyman Demirel University, Faculty of Economics and Administrative Sciences, Isparta, Turkey, cuneytcetin@sdu.edu.tr Jing LI KOLE School of Business State University of New York at Oswego Oswego, New York, 13126 USA, likole@oswego.edu ABSTRACT The new privately owned one-family house sold (C25) is recognized as great indicator for economy. The monthly data indicates that 250.000 houses were sold in February 2011. Compared to 2006 when 1,061,000 were sold, we understand that the total number of houses sold decreased by 76% in 2011. The purpose of this paper is to analyze factors that determine the decline of number of C25 in US. The empirical results indicate when the interest rate increases 1%, the number of new privately owned one-family houses sold decreases by 20 thousand.When the unemployment rate increases 1%, the number of new privately owned one-family houses sold decreases 81 thousand, holding all other variables constant. The results show a positive relationship may exist if rising home prices increase the quantity demanded for housing. Income and house sold have positive relationship but it’s not significant. For the population variable, the coefficient is a negative number. The result of monthly dummy test indicates that none of the months has significant effects. We could be able to conclude that current mortgage rate is significant at 1% level; mortgage rate at lag one time period is significant at 5% level; both real personal incomes at lag one time period and unemployment rate at lag two time period are significant at 10% level. JEL Codes: O18, R21 Volume 3 Number 1 Spring 2013 KEYWORDS United States of America,housingsales, mortgage, regression ARTICLE HISTORY Submitted: 29 Jun 2012 Resubmitted: 22 November 2012 Accepted: 25 December2012 185 �Ali Cüneyt ÇETİN / Jing LI KOLE Introduction Sales of new and existing privately owned single-family homes1 represent the number of housing units sold. New homes are newly constructed houses that are sold by the developer to the first owner. Existing homes are houses that are at least one year old. The number of new and existing homes available for sale indicates the inventory of unsold houses that are on the market. Economic output is increased far more by the purchase of a new house than of an existing house because of the materials and construction work required in building a new house, although renovation work is sometimes done when an existing house is purchased. While existing-home sales have a much smaller direct impact on the economy than new-home sales, existing and new-home sales are in fact closely linked because existing-home owners often can afford to buy a new home only by selling their current home. Thus, the market for existing homes strongly influences sales of new homes. In addition, both new and existing home sales generate purchases of furniture, appliances, and other house furnishings, which is a secondary stimulus to the economy. Home sales are sensitive to changes in economic conditions related to employment, personal income and saving, interest rates, housing starts, housing affordability index, and mortgage delinquency and foreclosure. Although housing is a necessity of living, home sales are highly cyclical because households are most likely to purchase a home during prosperous times when they can best afford it, but they tend to defer a home purchase during depressed times when they can least afford it (Chea, 2010). The new privately owned one-family house sold2 (C25) is recognized as great indicator for economy. The Housing Sales Survey is conducted by the Bureau of the Census under contract with the U.S Department of Housing and Urban Development. Sales of single-family homes were 250,000, according to the new monthly data3 in February 2011. Compared to five years ago, 1,061,000 in 2006 were decreased by 76%. 1 Single-family homes are unattached houses and townhouses, including individually owned and operated housing units as well as single-family townhouse condominiums. Currently, some 66 percent of all U.S. housing consists of single or one-family homes (Listokin, D.,& Burchell, R.W. Housing (shelter), Microsoft® Student 2009 [DVD], Redmond, WA: Microsoft Corporation). 2 It’s commonly known as C25. 3 Measures of new-home sales and of new homes available for sale are prepared monthly by the Bureau of the Census in the U.S. Department of Commerce and the U.S. Department of Housing and Urban Development. 186 Journal of Economic and Social Studies �The Factors Which Caused the Decline in the Amount of the Newly One Family Houses Sold in US What are the causes to the dramatic decline of number of C25? The purpose of this paper is to analyze factors that determine the decline of number of C25 in US. Literature Review An extensive body of literature exists concerning housing demand and home sales with most works confined to specific subtopics within the housing market. In recent years, researchers have devoted much of their effort to identify factors that determine the housing market mechanism (Sander and Testa, 2009; Lyytikäinen, 2009; Fratantoni and Schuh, 2003; Taylor, 2007; Bradley, Gabriel, and Wohar, 1995; Vargas-Silva, 2008). Many factors have been cited (Ewing and Wang, 2005; Baffoe-Bonnie, 1998; Huang, 1973; Thom, 1985) as sources of housing market dynamics; among these, housing price (Rapach and Strauss, 2009) and housing starts (Lyytikäinen, 2009; Ewing and Wang, 2005; Puri and Lierop, 1988; Huang, 1973) play a very important role. This literature review relates to the variables in statistical models and their explanatory power in the case of home sales and housing demand. Rising home prices would tend to result in a decrease in the quantity demanded for housing. However, as Campbell and Cocco (2007) found, a positive relationship may exist if rising home prices increase the perceived wealth of house holds, or lead to relaxed borrowing constraints. Their work also suggested that a reverse causality could result, with relaxed borrowing constraints increasing housing demand and therefore prices. Goodwin (1986) noted that inflation –distorted home prices may actually increase demand by acting as inflation hedges, with homeowners using increased home equity to compensate for rising prices in other areas. Unemployment, by lowering a person’s income, would tend to dampen the demand for new housing. Literature concerning the effects of unemployment on housing have largely ignored this simple assumption and instead focused on the effect homeownership has on unemployment. Oswald (1996) found that a 10 percent increase in homeownership increased unemployment by 2 percent. A study using Spanish data by Garcia and Hernandez (2004) that included extensive demographic variables concerning age, income and marital status found that the previous literature was not relevant for the Spanish market, where high homeownership rates were negatively correlated to unemployment. Volume 3 Number 1 Spring 2013 187 �Ali Cüneyt ÇETİN / Jing LI KOLE Inflation can produce a number of effects on the housing market. By increasing the price of housing, inflation can be assumed to reduce the demand for housing in inflationary times. Yet if used as an inflation hedge, housing demand may actually increase with inflation (Goodwin, 1986). The tax deductible nature of nominal rates of mortgage interest can actually lower the real cost of capital and therefore stimulates demand and homeownership (Rosen and Rosen, 1980), especially given the fact that capital gains are not taxable for first-time home sales. Kearl’s (1979) often cited work stated that inflation’s effect on housing costs serves to lower housing demand, while Feldstein and Summers (1978) observed that inflation decreases housing’s attractiveness as an investment. Hendershott (1980) confirmed the negative relationship between inflation and housing demand, and found that carrying costs were much more important in determining this demand than capital gains. According to Follain (1982), a 1 percent increase in the anticipated inflation rate reduced homeownership by more than three percentage points for all households with a larger effect occurring for non-elderly married couples. Complicit in this finding was the result that higher interest rates necessarily constrain borrowing. Homeownership usually necessitates borrowing, making the interest rate a key factor in the demand for housing. Aspergis (2003) stated that interest rates were the most important factor influencing housing demand, outweighing both inflation and unemployment as an explanatory variable which reinforced a conclusion suggested by Goodwin (1986), among others. Feldstein and Summers (1978) noted that the tax deductibility of mortgage interest plays a role in increasing the real interest rate, with cost depreciation lowering it. Their work also confirmed the Fisher effect link between inflation and nominal interest rates, with the two variables working together to either increase or decrease housing demand (Kagochi and Mace, 2009,p. 134-135). Data and Research Methodology The purpose of this paper is to analyze factors that determine the decline of number of the newly one-family houses sold in US. For this reason, our dependent variable is the new privately owned one-family house sold. People have a tendency to buy a house when the mortgage rate is low. Historically, the new home sales usually have a lagged reaction to changing mortgage rates. 188 Journal of Economic and Social Studies �The Factors Which Caused the Decline in the Amount of the Newly One Family Houses Sold in US Therefore, our first independent variable is long–term mortgage rate. People have a tendency to buy a house when the mortgage rate is low.Our prediction to the sign of the slope should be negative. We think people’s income should be another cause to C25. Following the same idea, the unemployment rate will also capture people’s expectation about their future income. If people lose their job, logically, they will not risk borrowing a 30 years mortgage. Another rational thought would be a C25 increase when population increases. So, population in United States is our fourth independent variable. A principle of microeconomics assumes that, holding all other factors equal, as the price of a product or service goes up, demand for that product or service declines. Conversely, if the price declines, demand goes up. Finally, we take the House Price Index for the United States as our last independent variable. Thus, our independent variables include 30 years mortgage rate, real personal income (seasonal adjusted), unemployment rate, population, and house price index. After determining our independent variables, we tried to search proper data to answer our question. The sample period is a time series of monthly data beginning February 1, 1980 and ending February 1, 2011. It contains 31 years and a total of 373 data sets. Data are collected from the Federal Reserve Bank of St. Louis economic research database. The reason why we have chosen Federal Reserve Bank of St. Louis economic research database as our resource is twofold. First, most of the data sets come with a nice graph which is a good source for visualization. Second, all the data sets have a downloading option in excel. This option made our data input session smooth. However, there are still some problems we have encountered during the data gathering process. Variables such as mortgage rate, income, and unemployment rate are collected monthly. But the house price index is collected quarterly; the population is collected annually. In order to have the same statistical measurement, we duplicated the last two variables in a respective monthly time series. Before we started to perform any test, we made some prediction about our variables’ slope sign and the significance of the variables. We predicted that the slopes of real personal income and a population should be positive. It makes sense when Volume 3 Number 1 Spring 2013 189 �Ali Cüneyt ÇETİN / Jing LI KOLE incomes increase people have more money to consume. Similarly, population increase should lead to more people needing houses. We also predicted that the slopes of mortgage rate, unemployment rate, and price index should be negative. As mortgage rates increase, people tend to borrow less to purchase houses. When a high unemployment rate occurs, people are more likely to have lower income expectation. The house price index is the average house price for a given period. Normally, we expect that a price increase leads to a demand decrease. That is the reason why the last three slopes are negative. Empirical Analysis We used Gretl4 as a tool to perform our entire statistics tests. The first test that we run was the Ordinary Least Squares (OLS). We generate a multiple regression model which include our dependent variable, Housesold and our independent variables, HPIndex_(1), Mortgage_(2), Population_(3), Real personal income_(4), and Unemployment_(5). The result of Ordinary Least Squares model is shown in Table 1. According to the Table 1, excluding the constant, mortgage rate and unemployment rate are significant at 1% significance level (p-value). Since the p-value of HPIndex, Population and RPIncome variables are above 0.10, these variables have no significant effect on house sold. The Gretl result also shows that the R² is 0.452. The interpretation of R2 is the proportion of the variable explained by the regression model. In this case, we can use our five independent variables to explain 45% of the reason why the new house sold. 4 Gretl is an open-sourcestatistical package, mainly for econometrics. The name is an acronym for GnuRegression, Econometrics and Time-series Library. Though it can’t be considered as a generalpurpose statistical software (its main functions are time series analysis, regression analysis and various econometric tests), it is very useful thanks also to its perfect integration with R. and with two other statistical packages used in seasonal adjustments: Tramo-Seatss and X-12-Arima, http:// gretl.sourceforge.net, 16.11.2012. 190 Journal of Economic and Social Studies �The Factors Which Caused the Decline in the Amount of the Newly One Family Houses Sold in US Table 1. Ordinary least squares, using observations 1-373, Dependent variable: Housesold coefficient std. error t-ratio p-value Const 2500.60 806.541 3.100 0.0021 HPIndex 0.698577 0.592120 1.180 0.2388 Mortgage -20.3564 7.08586 -2.873 0.0043 Population -5.68910 4.53039 -1.256 0.2100 RPIncome 0.0380852 Unemployment -81.4594 0.0735233 0.5180 0.6048 6.11404 -13.32 2.63e-033 Mean dependent var 721.3190 S.D. dependent var 238.4758 Sum squared resid 11603099 S.E. of regression 177.8091 R-squared 0.451543 Adjusted R-squared 0.444071 F(5, 367) 60.43004 P-value(F) 7.86e-46 Log-likelihood -2458.645 Akaike criterion 4929.289 Schwarz criterion 4952.819 Hannan-Quinn 4938.633 Rho 0.959309 Durbin-Watson 0.087015 Housesold=2,500.6+0.699HPindex-20.356Mortgage-5.689Population+0.038RPincome81.459Unemployment There are some surprises due to the sign of the slopes. Initially, we predicted the coefficient of population should be positive since more people need more houses. Nevertheless, the coefficient of the population in the OLS model is about -5. And our prediction for house price index coefficient is negative, but here it is positive 0.699. We need to continue a further investigation of this model or our data sets. Before we make any conclusion, we should interpret the OLS model first. The coefficient for the 30-year Mortgage (2) rate is negative 20.356. The p-value for the 2 is 0.0043. It shows that the 2 is significant at 1% significance level. The coefficient for the unemployment (5) is negative 81.459. The p-value for 5 is smaller than 0.001. We can say that with 99% confidence level that the unemployment variable is significant. The p-value is 0.2388 for 1. It means that this variable is not significant at even the 10% significance level. The coefficient for real personal income is 0.038 and the p-value is 0.605. In order to test the monthly effects, we include 11 month dummy variables in our new model. Since our data is time series, we notice that our Durbin-Watson Volume 3 Number 1 Spring 2013 191 �Ali Cüneyt ÇETİN / Jing LI KOLE statistic is equal to 0.084. We also performed a Durbin-Watson test to check the autocorrelation error in the model. Table 2 shows the OLS, using observations for 1980:02 2011:02. Table 2. Ordinary least squares, using observations 1980:02 2011:02 Dependent variable: Housesold (T = 373) coefficient std. error t-ratio p-value const 2657.05 868.735 3.059 0.0024 HPIndex 0.659071 0.607389 1.085 0.2786 Mortgage -21.1217 7.30748 -2.890 0.0041 Population -6.51837 4.92045 -1.325 0.1861 RPIncome 0.0499770 0.0790419 0.6323 0.5276 Unemployment -81.2761 6.21222 -13.08 3.30e-032 dm1 -26.2139 45.8000 -0.5724 0.5674 dm2 -25.4406 45.7532 -0.5560 0.5785 dm3 0.327942 47.4696 0.006908 0.9945 dm4 -4.43171 47.3711 -0.09355 0.9255 dm5 -3.23851 46.5742 -0.06953 0.9446 dm6 -0.805373 46.6899 -0.01725 0.9862 dm7 2.85291 46.3886 0.06150 0.9510 dm8 -8.65891 46.2479 -0.1872 0.8516 dm9 -6.80871 46.2805 -0.1471 0.8831 dm10 -6.62623 46.1451 -0.1436 0.8859 dm11 -8.69124 45.9089 -0.1893 0.8500 Mean dependent var 721.3190 S.D. dependent var 238.4758 Sum squared resid 11572504 S.E. of regression 180.2971 R-squared 0.452989 Adjusted R-squared 0.428405 F(16, 356) 18.42562 P-value(F) 1.24e-37 Log-likelihood -2458.152 Akaike criterion 4950.305 Schwarz criterion 5016.971 Hannan-Quinn 4976.777 rho 0.960583 Durbin-Watson 0.084113 Durbin-Watson statistic 0.0870146 p-value 0 According to the Durbin-Watson test, p-value is equal to zero shows that the model has autocorrelation problem. We should correct the model with a proper statistical method. Since the Durbin-Watson statistic equal to 0.087, it shows a positive first order autocorrelation. 192 Journal of Economic and Social Studies �The Factors Which Caused the Decline in the Amount of the Newly One Family Houses Sold in US The following result is the Prais-Winsten correction model, here we took lag-2 time period. Comparing to our lag-1 period result, the lag-2 period has a DW result closer to 2. This is the reason why we took lag-2 time period. Table 3 shows the Prais-Winsten correction model. Table 3. Prais-Winsten, using observations 1980: 04 - 2011: 02 Dependent variable: Housesold (T = 371) Coefficient std. error t-ratio p-value const -205.993 239.504 -0.8601 0.3903 HPIndex -0.137471 1.03831 -0.1324 0.8947 HPIndex_1 0.122039 1.34883 0.09048 0.9280 HPIndex_2 -0.257960 1.01868 -0.2532 0.8002 Mortgage -29.3433 9.40833 -3.119 0.0020 Mortgage _1 -3.93193 22.0249 -0.1785 0.8584 Mortgage _2 32.0894 15.5183 2.068 0.0394 Population -1.35884 3.21593 -0.4225 0.6729 Population_1 6.66001 4.68142 1.423 0.1557 Population_2 -4.20546 3.35179 -1.255 0.2104 RPIncome 0.0493773 0.0445178 1.109 0.2681 RPIncome_1 -0.111914 0.0587686 -1.904 0.0577 RPIncome_2 0.0560686 0.0457059 1.227 0.2207 Unemployment 1.68362 15.3669 0.1096 0.9128 Unemployment _1 29.2876 23.5791 1.242 0.2150 Unemployment _2 -28.4143 15.1497 -1.876 0.0615 Statistics based on the rho-differenced data Mean dependent var 722.4717 S.D. dependent var 238.5867 Sum squared resid 734764.0 S.E. of regression 45.62329 R-squared 0.965114 Adjusted R-squared 0.963434 F(17, 353) 827.8281 P-value(F) 2.0e-272 Rho -0.038935 Durbin-Watson 2.076334 After the Prais-Winsten correction (Table 3), we noticed that the Durbin-Watson statistic is 2.076. It means that the autocorrelation error is very low. In this new model, current mortgage rate is significant at 1% level; mortgage rate at lag -1time period is significant at 5% level; both real personal incomes at lag-1 time period and unemployment rate at lag-2 time period are significant at 10% level. Volume 3 Number 1 Spring 2013 193 �Ali Cüneyt ÇETİN / Jing LI KOLE The new R Square, 96%, is much higher than the OLS model. It also has a lower tratio. These indications might reveal a multicollinearity relationship existing among the independent variables. When a multicollinearity problem exists in this model, it is possible that each of the individual coefficients may be individually insignificant, but the joint effect may have a significant impact on the dependent variable. Since some independent variables in this model are not significant, we decided to perform a Wald-test to test the joint effect of these factors: Price index, real personal income, unemployment rate, and population. H0: 1=2=3=5=7= 8=9=10=12=13=14=0 H1: at least one of the  is not zero The Wald-test result is below: Wald-test formula: F = [(ESS R - ESS U ) / m]/ {ESS / [N − (k + 1)]} Test statistic: F (12, 353) = 1.94718, with p-value = 0.0282136 Where the following notation applies: ESS R , error sum of squares of Model R ESS U , error sum of squares of Model U ESS, error sum of squares Model R is called the restricted model Model U is called the unrestricted model m=number of restrictions N= number of observations k= number of regressors in unrestricted regression Since the p-value of the Wald-test is 0.028, we do have enough evidence to reject the null hypothesis at 5% significance level. In another word, the joint effects of the non-significant variable are great than zero. Given the result of Wald test, we should continue an investigation the multicollinearity among the independent variables. Therefore, we carried on a series of Auxiliary Regressions. By using Auxiliary regressions, we can compute variance inflation factor(VIF) which is a measure of the effect of multicollinearity on the variance parameter estimates. The auxiliary regression and VIF result is presented in Table 4. 194 Journal of Economic and Social Studies �The Factors Which Caused the Decline in the Amount of the Newly One Family Houses Sold in US Table 4. The Auxiliary regression and VIF result In-Variables HPIndex Mortgage PoPula RPI Unemp VIF 433.35 150.01 188.52 238.63 105.828 HPIndex_1 Mortgage_1 Popula_1 RPI_1 Unemp_1 In-Variables VIF In-Variables VIF 2084.58 392.72 2093.87 1798.31 213.71 HPIndex_2 Mortgage_2 Popula_2 RPI_2 Unemp_2 1378.97 136.64 1236.1 1208.83 103.95 High VIFs suggest the presence of a multicollinearity problem. When VIF>30 usually indicates a sever multicollinearity. The VIF results for all the variables are great than 30. It means that all the variables are highly correlated. It also means that we have a small sample size. Conclusion Housing sales play a significant role as leading indicator of the economy, and thereforeunderstanding the market dynamics cannot be overemphasized, especially in light of the recenthousing market turmoil and its effect on the economy as a whole. Since, the factors in thehousing market will likely continue to play an important role in the business and economy (Gupta& Das, 2010; Bernanke and Gertler, 1995), understanding the market mechanism, specifically thelead-lag relationship between factors can offer policy makers a notion about the direction of theoverall market trajectory in advance, and thus, provides a better control for designing appropriatepolicies for housing market stabilization (Choudhury, 2010, p.45). As a result of such importance of the housing market on the economy,the purpose of this paper is to analyze factors that determine the decline of number of C25 in US. The study found that the coefficient for the 30-year Mortgage (2) rate is negative 20.356. It indicates when the interest rate increases 1%, the number of new privately owned one-family houses sold decreases by 20 thousand, holding all other variables constant. This is not a surprise result for this regression analysis. The mortgage rate plays a critical role in house market. The 30-year mortgage rate decreases more than 50% from 13% in the1980s to 5%-7% in the 2000s. At the same time, the number of houses sold increases about 50% from 541,000 in the 1980s to 1,000,000 in 2006, before the 2007 recession. Volume 3 Number 1 Spring 2013 195 �Ali Cüneyt ÇETİN / Jing LI KOLE The coefficient for the unemployment (5) is negative 81.459. It indicates when the unemployment rate increases 1%, the number of new privately owned onefamily houses sold decreases 81 thousand, holding all other variables constant. This result proves our prediction in the sign of the slope. New houses sold and labor markets tend to go together. When the unemployment rate is low, people have a positive expectation for their future income. These expectations will strengthen the house market. Similarly, when a large number of people lose their jobs, the house market will move slowly. It’s also true that these two factors are strong indicators for the economy. Currently, we have a slow house market and a low employment rate. One of the unexpected results is the positive sign of the coefficient 1for the house price index. As we explained previously, we thought when price goes up the demand should go down. But it doesn’t fit in this case. One possible explanation is that this is all a function of rising demand and the rising prices for houses simply reflects the rising demand and the inadequate supply of new construction for homes. The second possibility is that rising prices actually cause an increase in demand. This is because the purchase of a house has two components: the usefulness of the house as a place to live, and the anticipated future income to be obtained from selling the house later at a higher price. Rising home prices increase buyers’ expectation of future profits from selling their houses, so they are willing to pay more for a house. The coefficient 4 for the real personal income variable is 0.038 and the p-value is 0.605. This result indicates that income and house sold have positive relationship but it’s not significant. This may due to the unemployment rate variable which captures most income effects. In another way, it shows that real personal income and unemployment have a high correlation.For the population variable, the p-value is 0.21, so it has no significance effect on house sold. In order to test the monthly effects, we include 11 month dummy variables in our new model. The result of monthly dummy test indicates that none of the months has significant effects. However, from March to July the slopes of the months have positive or lower negativeeffects. It means that these few months have more houses sold than other months. Consequently, it’s impossible to determine all the causes to the number of new house sold since many factors are interrelated. However, through our series of statistical tests, wecould be able to conclude that current mortgage rate is significant at 1% level; mortgage rate at lag one time period is significant at 5% level; both real 196 Journal of Economic and Social Studies �The Factors Which Caused the Decline in the Amount of the Newly One Family Houses Sold in US personal incomes at lag one time period and unemployment rate at lag two time period are significant at 10% level. References Aspergis, N. (2003). Housing prices and macroeconomic factors: prospects within the European Monetary Union, International Real Estate Review, Vol. 6 No. 1, pp. 63-74. Baffoe-Bonnie, J. (1998). The dynamic impact of macroeconomic aggregates on housing prices and stock of houses: a national and regional analysis, Journal of Real Estate Finance and Economics, 17(2), 179–197. Bernanke, B. S.,& Gertler, M. (1995). Inside the black box: The credit channel of monetary policy transmission,The Journal of Economic Perspectives, 9, 27–48. Bradley, M. G., Gabriel, S. A.,& Wohar, M. E. (1995). The thrift crisis, mortgage-credit intermediation, and Housing Activity. Journal of Money, Credit, and Banking, 27(2), 476-497. Campbell, J. Y.,& Cocco, J. (2007). How do house prices affect consumption? Evidence from micro data, Journal of Monetary Economics, Vol. 54, pp. 591-621. Chea,S. (2010).Home Sales as an Economic Indicator,http://voices.yahoo.com/home-sales-as-economic-indicator-5362321.html?cat=3 Choudhury, A. (2010). Factors Associated in Housing Market Dynamics: An Exploratory Longitudinal Analysis, Academy of Accounting and Financial Studies Journal, Volume 14, Number 4, pp. 43-54. Ewing, B.T. & Wang, Y. (2005). Single Housing Starts and Macroeconomic Activity: An Application of Generalized Impulse Response Analysis,Applied Economics Letters, 12(3), pp. 187-190. Feldstein, M.,& Summers, L. (1978). Inflation, tax rules, and the long-term interest rate, Brookings Papers on Economic Activity, Vol. 1, pp. 61-109. Follain, J.R. (1982). Does inflation affect real behavior: the case of housing, Southern Economic Journal, Vol. 48, No. 3, pp. 570-82. Fratantoni, M., & Schuh, S. (2003). Monetary Policy, Housing, and Heterogeneous Regional Markets, Journal of Money, Credit and Banking, 35(4), pp. 557-589. Garcia, J. A.B.,& Hernandez, R.J.E. (2004). User cost changes, unemployment and homeownership: evidence from Spain, Urban Studies, Vol. 41, No. 3, pp. 563-78. Goodwin, T.H. (1986). Inflation, risk, taxes, and the demand for owner-occupied housing, Review of Economics and Statistics, Vol. 68, No. 2, pp. 197-206. Gupta, R.,& Das, S. (2010). Predicting Downturns in the US Housing Market: A Bayesian Approach, Journal of Real Estate Finance and Economics, Vol. 41, No. 3, pp. 294-319. Hendershott, P.H. (1980). Real user costs and the demand for single family housing, Brookings Papers on Economic Activity, Vol. 2, pp. 401-52. Huang, D.S. (1973). Short-Run Instability in Single-Family Housing Starts, Journal of the American Statistical Association, 68(344), pp. 788- 792. Volume 3 Number 1 Spring 2013 197 �Kagochi J. M., & Mace, L. M. (2009). The determinants of demand for single family housing in Alabama urbanized areas, International Journal of Housing Markets and Analysis,Vol. 2, No. 2, pp. 132-144. Kearl, J.R. (1979). Inflation, mortgage, and housing, The Journal of Political Economy, Vol. 87 No. 5, pp. 1115-38. Listokin, D.,& Burchell, R.W. Housing (shelter), Microsoft® Student 2009 [DVD], Redmond, WA: Microsoft Corporation. Lyytikäinen, T. (2009). Three-rate property taxation and housing construction, Journal of Urban Economics, 65(3), pp. 305-313. Oswald, A. (1996). A conjecture on the explanation for high unemployment in the industrialized nations: part I, Warwick Economics Research Paper 475, University of Warwick, Coventry, pp. 197206. Puri, A.K.,& Lierop, J.V. (1988). Forecasting Housing Starts, International Journal of Forecasting, 4, pp. 125-134. Rapach, D. E., & Strauss, J. K. (2009). Differences in housing price forecastability across US states, International Journal of Forecasting, 25(2), pp. 351-372. Rosen, H. S., & Rosen, K. T. (1980). Federal taxes and homeownership: evidence from time series, Journal of Political Economy, Vol. 88 No. 11, pp. 59-75. Sander, W., & Testa, W. A. (2009). Education and Household Location in Chicago,Growth and Change, 40(1), pp. 116–139. Taylor, J. B. (2007). Housing and Monetary Policy), NBER Working Paper No. W13682. Available at SSRN: http://ssrn.com/abstract=1077808. Thom, R. (1985). The Relationship between Housing Starts and Mortgage Availability, The Review of Economics and Statistics, 67(4), pp. 693-696. Vargas-Silva, C. (2008). Monetary Policy and the U.S. Housing Market: A VAR Analysis Imposing Sign Restrictions,Journal of Macroeconomics, 30(3), pp. 977-990. 198 Journal of Economic and Social Studies � 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. 2381 Title A name given to the resource The Factors Which Caused the Decline in the Amount of the Newly One Family Houses Sold in US Author Author ÇETİN, Ali Cüneyt KOLE, Jing Li Abstract A summary of the resource. The new privately owned one-family house sold (C25) is recognized as great indicator for economy. The monthly data indicates that 250.000 houses were sold in February 2011. Compared to 2006 when 1,061,000 were sold, we understand that the total number of houses sold decreased by 76% in 2011. The purpose of this paper is to analyze factors that determine the decline of number of C25 in US. The empirical results indicate when the interest rate increases 1%, the number of new privately owned one-family houses sold decreases by 20 thousand.When the unemployment rate increases 1%, the number of new privately owned one-family houses sold decreases 81 thousand, holding all other variables constant. The results show a positive relationship may exist if rising home prices increase the quantity demanded for housing. Income and house sold have positive relationship but it’s not significant. For the population variable, the coefficient is a negative number. The result of monthly dummy test indicates that none of the months has significant effects. We could be able to conclude that current mortgage rate is significant at 1% level; mortgage rate at lag one time period is significant at 5% level; both real personal incomes at lag one time period and unemployment rate at lag two time period are significant at 10% level. Publisher An entity responsible for making the resource available International Burch University Date A point or period of time associated with an event in the lifecycle of the resource 2013-03-10 Keywords Keywords. Article PeerReviewed H Social Sciences (General) https://omeka.ibu.edu.ba/files/original/81805e36f5664fc6117e6ab037652d76.pdf 3db36f5cd7e1d7380439025b8747eefe PDF Text Text 3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Maskell, B. and Kato, N. (2007) “Value Stream Costing: The Lean Solution to Standard Costing Complexity and Waste” in E. D. Stenzel (eds.) Lean Accounting: Best Practices for Sustainable Integration, Hoboken, New Jersey: John Wiley & Sons, Inc. Maskell, B. H. and Kennedy, F. A. (2007) Why Do We Need Lean Accounting and How Does It Work?, Journal of Corporate Accounting & Finance (Wiley), 18(3),59-73. McNair, C.J. (2007) On Target: Customer-Driven Lean Management. in E. D. Stenzel (eds.) Lean Accounting: Best Practices for Sustainable Integration, Hoboken, New Jersey: John Wiley & Sons, Inc. Shah, R. and Ward, P.T. (2003) Lean Manufacturing: Context, Practice Bundles, and Performance, Journal of Operations Management, 21, 129-149. Sharman, P.A. (2003) The Case for Management Accounting, Strategic Finance, 85(4), 43-47. Van Der Merwe, A. and Thomson, J. (2007) The Lowdown on Lean Accounting. Strategic Finance, 88(8), 26-33. White, L. (2009) Resource Consumption Accounting: Manager-Focused Management Accounting, The Journal of Corporate Accounting & Finance, 20(4), 63-77. Woehrle, L. S. and Abou-Shady, L. (2010) Using Dynamic Value Stream Mapping and Lean Accounting Box Scores to Support Lean Implementation, American Journal of Business Education, 3(8), 67-75. The Factors Which Caused The Decline In The Amount Of The Newly One Family Houses Sold In Us Ali Cüneyt Çetin1,Jing Li-Kole2 1Department of Accounting and Finance,Suleyman Demirel University Faculty of Economics and Administrative Sciences, Isparta, Turkey 2State University of New York at Oswego, New York, USA E-mails: cuneytcetin@sdu.edu.tr,likole@oswego.edu Abstract The new privately owned one-family house sold (C25) is recognized as great indicator for economy. The monthly data in February 2011 was 250,000 houses sold. Compared to five years ago, 1,061,000 in 2006 were decreased by 76%. What are the causes to the dramatic decline of number of C25? The purpose of this paper is to analyze factors that determine the decline of number of C25 in US. Therefore, in this study, dependent variable is the new privately owned one-family house sold. Independent variables include 30 years mortgage rate, real personal income, unemployment rate, population, and house price index. The results indicate when the interest rate increases 1%, the number of new privately owned one-family houses sold decreases by 20 thousand. When the unemployment rate increases 1%, the 264 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo number of new privately owned one-family houses sold decreases 81 thousand, holding all other variables constant. We thought when price goes up the demand should go down. But it doesn’t fit in this study. Income and house sold have positive relationship but it’s not significant. It shows that real personal income and unemployment have a high correlation. For the population variable, the coefficient is a negative number. Even though the p-value indicates that this result is not significant, we still couldn’t figure out the cause of this negative relation. The result of monthly dummy test indicates that none of the months has significant effects. However, from March to July the slopes of the months have positive or lower negative effects. Consequently, it’s impossible to determine all the causes to the number of new house sold since many factors are interrelated. However, through our series of statistical tests, we could be able to conclude that current mortgage rate is significant at 1% level; mortgage rate at lag one time period is significant at 5% level; both real personal incomes at lag one time period and unemployment rate at lag two time period are significant at 10% level. Keywords: house sold, mortgage rate, income level, unemployment rate, population increases, house price index 1.INTRODUCTION Sales of new and existing privately owned single-family homes16 represent the number of housing units sold. New homes are newly constructed houses that are sold by the developer to the first owner. Existing homes are houses that are at least one year old. The number of new and existing homes available for sale indicates the inventory of unsold houses that are on the market. A home is typically the largest single item bought by householders. Economic output is increased far more by the purchase of a new house than of an existing house because of the materials and construction work required in building a new house, although renovation work is sometimes done when an existing house is purchased. While existing-home sales have a much smaller direct impact on the economy than new-home sales, existing and new-home sales are in fact closely linked because existing-home owners often can afford to buy a new home only by selling their current home. Thus, the market for existing homes strongly influences sales of new homes. In addition, both new and existing home sales generate purchases of furniture, appliances, and other house furnishings, which is a secondary stimulus to the economy. Home sales are sensitive to changes in economic conditions related to employment, personal income and saving, interest rates, housing starts, housing affordability index, and mortgage delinquency and foreclosure. Although housing is a necessity of living, home sales are highly cyclical because households are most likely to purchase a home during prosperous times when they can best afford it, but they tend to defer a home purchase during depressed times when they can least afford it (Stephenson, 2010). 16 It’s commonly known as C25. 265 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo The new privately owned one-family house sold17 (C25) is recognized as great indicator for economy. The Housing Sales Survey is conducted by the Bureau of the Census under contract with the U.S Department of Housing and Urban Development. Sales of single-family homes were 250,000, according to the new monthly data18 in February 2011. Compared to five years ago, 1,061,000 in 2006 were decreased by 76%. What are the causes to the dramatic decline of number of C25? The purpose of this paper is to analyze factors that determine the decline of number of C25 in US. 2.LITERATURE REVIEW A large number of studies on the housing market have been undertaken recently. In recent years, researchers have devoted much of their effort to identify factors that determine the housing market mechanism (Sander and Testa 2009; Lyytikäinen, 2009; Fratantoni and Schuh, 2003; Taylor, 2007; Bradley, Gabriel, and Wohar, 1995; Vargas-Silva, 2008). Many factors have been cited (Ewing and Wang, 2005; Baffoe-Bonnie, 1998; Huang, 1973; Thom, 1985) as sources of housing market dynamics; among these, housing price (Rapach and Strauss, 2009) and housing starts (Lyytikäinen, 2009; Ewing and Wang, 2005; Puri and Lierop, 1988; Huang, 1973) play a very important role. Rising home prices would tend to result in a decrease in the quantity demanded for housing. However, as Campbell and Cocco (2007) found, a positive relationship may exist if rising home prices increase the perceived wealth of house holds, or lead to relaxed borrowing constraints. Their work also suggested that a reverse causality could result, with relaxed borrowing constraints increasing housing demand and therefore prices. Goodwin (1986) noted that inflation –distorted home prices may actually increase demand by acting as inflation hedges, with homeowners using increased home equity to compensate for rising prices in other areas. Unemployment, by lowering a person’s income, would tend to dampen the demand for new housing. Literature concerning the effects of unemployment on housing have largely ignored this simple assumption and instead focused on the effect homeownership has on unemployment. Oswald (1996) found that a 10 percent increase in homeownership increased unemployment by 2 percent. A study using Spanish data by Garcia and Hernandez (2004) that included extensive demographic variables concerning age, income and marital status found that the previous literature was not relevant for the Spanish market, where high homeownership rates were negatively correlated to unemployment. 17 Measures of new-home sales and of new homes available for sale are prepared monthly by the Bureau of the Census in the U.S. Department of Commerce and the U.S. Department of Housing and Urban Development. 18 Gretl is an open-source statistical package, mainly for econometrics. The name is an acronym for Gnu Regression, Econometrics and Time-series Library. Though it can't be considered as a generalpurpose statistical software (its main functions are time series analysis, regression analysis and various econometric tests), it is very useful thanks also to its perfect integration with R. and with two other statistical packages used in seasonal adjustement: Tramo-Seatss and X-12-Arima (http://freestatistics.altervista.org/en/reviews/gretl.php; http://en.wikipedia.org/wiki/Gretl#cite_note-3). 266 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Inflation can produce a number of effects on the housing market. By increasing the price of housing, inflation can be assumed to reduce the demand for housing in inflationary times. Yet if used as an inflation hedge, housing demand may actually increase with inflation (Goodwin, 1986). The tax deductible nature of nominal rates of mortgage interest can actually lower the real cost of capital and therefore stimulates demand and homeownership (Rosen and Rosen, 1980), especially given the fact that capital gains are not taxable for first-time home sales. Kearl’s (1979) often cited work stated that inflation’s effect on housing costs serves to lower housing demand, while Feldstein and Summers (1978) observed that inflation decreases housing’s attractiveness as an investment. Hendershott (1980) confirmed the negative relationship between inflation and housing demand, and found that carrying costs were much more important in determining this demand than capital gains. According to Follain (1982), a 1 percent increase in the anticipated inflation rate reduced homeownership by more than three percentage points for all households with a larger effect occurring for non-elderly married couples. Complicit in this finding was the result that higher interest rates necessarily constrain borrowing. Homeownership usually necessitates borrowing, making the interest rate a key factor in the demand for housing. Aspergis (2003) stated that interest rates were the most important factor influencing housing demand, outweighing both inflation and unemployment as an explanatory variable which reinforced a conclusion suggested by Goodwin (1986), among others. Feldstein and Summers (1978) noted that the tax deductibility of mortgage interest plays a role in increasing the real interest rate, with cost depreciation lowering it. Their work also confirmed the Fisher effect link between inflation and nominal interest rates, with the two variables working together to either increase or decrease housing demand. 3.DATA AND RESEARCH METHODOLOGY Few studies have looked at the factors which caused the decline in the newly one family houses sold (C25) in US. The purpose of this paper is to analyze factors that determine the decline of number of the newly one-family houses sold in US. For this reason, our dependent variable is the new privately owned one-family house sold. People have a tendency to buy a house when the mortgage rate is low. Historically, the new home sales usually have a lagged reaction to changing mortgage rates. Therefore, our first independent variable is long–term mortgage rate. Our prediction to the sign of the slope should be negative. We think people’s income should be another cause to C25. Following the same idea, the unemployment rate will also capture people’s expectation about their future income. If people lose their job, logically, they will not risk borrowing a 30 years mortgage. Another rational thought would be a C25 increase when population increases. So, population in United States is our fourth independent variable. A principle of microeconomics assumes that, if all other factors are equal, as the price of a product or service goes up, demand for that product or service declines. Conversely, if the price declines, demand goes up. Finally, we take the House Price Index for the United States as our last independent variable. Thus, our independent variables include 30 years mortgage rate, real personal income (seasonal adjusted), unemployment rate, population, and house price index. 267 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo After determining our independent variables, we tried to search proper data to answer our question. The sample period is a time series of monthly data beginning February 1, 1980 and ending February 1, 2011. It contains 31 years and a total of 373 data sets. Data are collected from the Federal Reserve Bank of St. Louis economic research database. The reason why we have chosen Federal Reserve Bank of St. Louis economic research database as our resource is twofold. First, most the data sets come with a nice graph which is a good source for visualization. Second, all the data sets have a downloading option in excel. This option made our data input session smooth. However, there are still some problems we have encountered during the data gathering process. Variables such as mortgage rate, income, and unemployment rate are collected monthly. But the house price index is collected quarterly; the population is collected annually. In order to have the same statistical measurement, we duplicated the last two variables in a respective monthly time series. Before we started to perform any test, we made some prediction about our variables’ slope sign and the significance of the variables. We predicted that the slopes of real personal income and a population should be positive. It makes sense when incomes increase people have more money to consume. Similarly, population increase should lead to more people needing houses. We also predicted that the slopes of mortgage rate, unemployment rate, and price index should be negative. As mortgage rates increase, people tend to borrow less to purchase houses. When a high unemployment rate occurs, people are more likely to have lower income expectation. The house price index is the average house price for a given period. Normally, we expect that a price increase leads to a demand decrease. That is the reason why the last three slopes are negative. 4.EMPIRICAL ANALYSIS We used Gretl19 as a tool to perform our entire statistics tests. The first test that we run was the Ordinary Less Squares (OLS). We generate a multiple regression model which include our dependent variable, Housesold and our independent variables, HPIndex(β1), Mortgage(β2), Population(β3), Real personal income(β4), and Unemployment(β5). The result of Ordinary Less Squares model is shown in Table 1. Table 1: Ordinary Less Squares model using observations 1-373 (Dependent variable: Housesold) coefficient std. error t-ratio p-value const 2500.60 806.541 3.100 0.0021 *** HPIndex 0.698577 0.592120 1.180 0.2388 Mortgage -20.3564 7.08586 -2.873 0.0043 19Single-family homes are unattached houses and townhouses, including individually owned and operated housing units as well as single-family townhouse condominiums. Currently, some 66 percent of all U.S. housing consists of single or one-family homes (Listokin, D. and Burchell, R.W., Housing (shelter), Microsoft® Student 2009 [DVD], Redmond, WA: Microsoft Corporation, 2008). 268 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo *** Population RPIncome -5.68910 4.53039 0.0380852 -1.256 0.0735233 Unemployment 033 *** -81.4594 6.11404 Mean dependent var 721.3190 S.D. dependent var Sum squared resid 177.8091 11603099 S.E. of regression 0.2100 0.5180 -13.32 R-squared 0.444071 0.451543 Adjusted R-squared F(5, 367) 46 60.43004 P-value(F) 0.6048 2.63e- 238.4758 7.86e- Log-likelihood -2458.645 Akaike criterion 4929.289 Schwarz criterion 4952.819 Hannan-Quinn 4938.633 Rho 0.959309 Durbin-Watson 0.087015 Excluding the constant, p-value was highest for variable 5 (RPIncome) Housesold=2,500.6+0.699HPindex-20.356Mortgage-5.689Population+0.038RPincome81.459Unemployment According to the Table 1, two variables, mortgage rate and unemployment rate are significant at 1% confidence level (p-value). The Gretl result also shows that the R2 is 0.452. The interpretation of R2 is the proportion of the variable explained by the regression model. In this case, we can use our five independent variables to explain 45% of the reason why the new house sold. There are some surprises due to the sign of the slopes. Initially, we predicted the coefficient of population should be positive since more people need more houses. Nevertheless, the coefficient of the population in the OLS model is about -5. And our prediction for house price index coefficient is negative, but here it is positive 0.699. We need to continue a further investigation of this model or our data sets. Before we make any conclusion, we should interpret the OSL model first. The coefficient for the constant is 2,500. It means that when all the independent variables are zero, the number of houses sold is 2,500, holding all other variables constant. The interpretation doesn’t have much economic meaning in this case. The coefficient for the 30-year Mortgage (β2) rate is negative 20.356. The p-value for the β2 is 0.0043. It shows that the β2 is significant at 1% confidence level. The coefficient for the 269 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo unemployment (β5) is negative 81.459. The p-value for β5 is smaller than 0.001. We can say that with 99% confidence level that the unemployment variable is significant. The p-value is 0.2388 for β1. It means that this variable is not significant at even the 10% confidence level. The coefficient for real personal income is 0.038 and the p-value is 0.605. In order to test the monthly effects, we include 11 month dummy variables in our new model. Since our data is time series, we notice that our Durbin-Watson statistic is equal to 0.084. We also performed a Durbin-Watson test to check the autocorrelation error in the model. Table 2 shows the OLS, using observations for 1980:02 - 2011:02. Table 2: Ordinary Less Squares model using observations 1980:02 - 2011:02 const HPIndex Morate Population RPIncome Unemployee dm1 dm2 dm3 dm4 dm5 dm6 dm7 dm8 dm9 dm10 dm11 Mean dependent var Sum squared resid R-squared F(16, 356) Log-likelihood Schwarz criterion rho Durbin-Watson statistic Dependent variable: Housesold (T = 373) coefficient std. error t-ratio 2657.05 868.735 3.059 0.659071 0.607389 1.085 -21.1217 7.30748 -2.890 -6.51837 4.92045 -1.325 0.0499770 0.0790419 0.6323 -81.2761 6.21222 -13.08 -26.2139 45.8000 -0.5724 -25.4406 45.7532 -0.5560 0.327942 47.4696 0.006908 -4.43171 47.3711 -0.09355 -3.23851 46.5742 -0.06953 -0.805373 46.6899 -0.01725 2.85291 46.3886 0.06150 -8.65891 46.2479 -0.1872 -6.80871 46.2805 -0.1471 -6.62623 46.1451 -0.1436 -8.69124 45.9089 -0.1893 721.3190 S.D. dependent var 11572504 S.E. of regression 0.452989 Adjusted R-squared 18.42562 P-value(F) -2458.152 Akaike criterion 5016.971 Hannan-Quinn 0.960583 Durbin-Watson 0.0870146 p-value p-value 0.0024 *** 0.2786 0.0041 *** 0.1861 0.5276 3.30e-032 *** 0.5674 0.5785 0.9945 0.9255 0.9446 0.9862 0.9510 0.8516 0.8831 0.8859 0.8500 238.4758 180.2971 0.428405 1.24e-37 4950.305 4976.777 0.084113 0 According to the Durbin-Watson test, p-value is equal to zero shows that the model has autocorrelation problem. We should correct the model with a proper statistical method. Since the Durbin-Watson statistic equal to 0.087, it shows a positive first order autocorrelation. The following result is the Prais-Winsten correction model, here we took lag-2 time period. Comparing to our lag-1 period result, the lag-2 period has a DW result closer to 2. This is the reason why we took lag-2 time period. Table 3 shows the Prais-Winsten correction model. Table 3: Prais-Winsten, using observations 1980: 04 - 2011: 02 const HPIndex HPIndex_1 HPIndex_2 Morate Morate_1 270 Dependent variable: Housesold (T = 371) Coefficient std. error t-ratio -205.993 239.504 -0.8601 -0.137471 1.03831 -0.1324 0.122039 1.34883 0.09048 -0.257960 1.01868 -0.2532 -29.3433 9.40833 -3.119 -3.93193 22.0249 -0.1785 p-value 0.3903 0.8947 0.9280 0.8002 0.0020*** 0.8584 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Morate_2 32.0894 15.5183 2.068 Population -1.35884 3.21593 -0.4225 Population_1 6.66001 4.68142 1.423 Population_2 -4.20546 3.35179 -1.255 RPIncome 0.0493773 0.0445178 1.109 RPIncome_1 -0.111914 0.0587686 -1.904 RPIncome_2 0.0560686 0.0457059 1.227 Unemployee 1.68362 15.3669 0.1096 Unemployee_1 29.2876 23.5791 1.242 Unemployee_2 -28.4143 15.1497 -1.876 Statistics based on the rho-differenced data: Mean dependent var 722.4717 S.D. dependent var Sum squared resid 734764.0 S.E. of regression R-squared 0.965114 Adjusted R-squared F(17, 353) 827.8281 P-value(F) rho -0.038935 Durbin-Watson Excluding the constant, p-value was highest for variable 20 (HPIndex_1) 0.0394** 0.6729 0.1557 0.2104 0.2681 0.0577* 0.2207 0.9128 0.2150 0.0615* 238.5867 45.62329 0.963434 2.0e-272 2.076334 After the Prais-Winsten correction (Table 3), we noticed that the Durbin-Watson statistic is 2.076. It means that the autocorrelation error is very low. In this new model, current mortgage rate is significant at 1% level; mortgage rate at lag -1time period is significant at 5% level; both real personal incomes at lag-1 time period and unemployment rate at lag-2 time period are significant at 10% level. The new R Square, 96%, is much higher than the OLS model. It also has a lower t-ratio. These indications might reveal a multicollinearity relationship existing among the independent variables. When a multicollinearity problem exists in this model, it is possible that each of the individual coefficients may be individually insignificant, but the joint effect may have a significant impact on the dependent variable. Since some independent variables in this model are not significant, we decided to perform a Wald-test to test the joint effect of these factors: Price index, real personal income, Unemployment rate, and population. Ho: β1= β2= β3=β5=β7= β8=β9=β10=β12=β13=β14=0; H1: at least one of the β is not zero. The Wald-test result is below: Wald-test formula: F = [(Essr-Essu) / m]/ {ESS / [N − (k + 1)]} Test statistic: F (12, 353) = 1.94718, with p-value = 0.0282136 Since the p-value of the Wald-test is 0.028, we do have enough evidence to reject the nonhypothesis at 5% confidence level. In another word, the joint effects of the non-significant variable are great than zero. Given the result of Wald test, we should continue an investigation the multicollinearity among the independent variables. Therefore, we carried on a series of Auxiliary Regressions. By using Auxiliary regressions, we can compute VIF which is a measure of the effect of multicollinearity on the variance parameter estimates. The auxiliary regression and VIF result is presented in Table 4. Table 4: The Auxiliary regression and VIF result In-Variables VIF In-Variables VIF 271 HPIndex Mortgage PoPula RPI Unemp 433.35 150.01 188.52 238.63 105.828 HPIndex_1 Mortgage_1 Popula_1 RPI_1 Unemp_1 2084.58 392.72 2093.87 1798.31 213.71 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo In-Variables VIF HPIndex_2 Mortgage_2 Popula_2 RPI_2 Unemp_2 1378.97 136.64 1236.1 1208.83 103.95 High VIFs suggest the presence of a multicollinearity problem. When VIF>30 usually indicates a sever multicollinearity. The VIF results for all the variables are great than 30. It means that all the variables are highly correlated. It also means that we have a small sample size. 5.CONCLUSION A house is durable goods and a necessity to most people. The purpose of this paper is to analyze factors that determine the decline of number of C25 in US. The study found that the coefficient for the 30-year Mortgage (β2) rate is negative 20.356. It indicates when the interest rate increases 1%, the number of new privately owned one-family houses sold decreases by 20 thousand, holding all other variables constant. This is not a surprise result for this regression analysis. The mortgage rate plays a critical role in house market. The 30-year mortgage rate decreases more than 50% from 13% in the1980s to 5%-7% in the 2000s. At the same time, the number of houses sold increases about 50% from 541,000 in the 1980s to 1,000,000 in 2006, before the 2007 recession. The coefficient for the unemployment (β5) is negative 81.459. It indicates when the unemployment rate increases 1%, the number of new privately owned one-family houses sold decreases 81 thousand, holding all other variables constant. This result proves our prediction in the sign of the slope. New houses sold and labor markets tend to go together. When the unemployment rate is low, people have a positive expectation for their future income. These expectations will strengthen the house market. Similarly, when a large number of people lose their jobs, the house market will move slowly. It’s also true that these two factors are strong indicators for the economy. Currently, we have a slow house market and a low employment rate. One of the shocking results is the positive sign of coefficient for the house price index (β1). As we explained previously, we thought when price goes up the demand should go down. But it doesn’t fit in this case. One possible explanation is that this is all a function of rising demand and the rising prices for houses simply reflects the rising demand and the inadequate supply of new construction for homes. The second possibility is that rising prices actually cause an increase in demand. This is because the purchase of a house has two components: the usefulness of the house as a place to live, and the anticipated future income to be obtained from selling the house later at a higher price. Rising home prices increase buyers' expectation of future profits from selling their houses, so they are willing to pay more for a house. The coefficient for real personal income is 0.038 and the p-value is 0.605. This result indicates that income and house sold have positive relationship but it’s not significant. This may due to the unemployment rate variable which captures most income effects. In another way, it shows that real personal income and unemployment have a high correlation. For the population variable, the coefficient is a negative number. Even though the p-value indicates that this result is not significant, we still couldn’t figure out the cause of this negative relation. This may be like those weird results that we never understand. In order to test the monthly effects, we include 11 month dummy variables in our new model. The result of monthly dummy test indicates that none of the months has significant effects. 272 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo However, from March to July the slopes of the months have positive or lower negative effects. It means that these few months have more houses sold than other months. Consequently, it’s impossible to determine all the causes to the number of new house sold since many factors are interrelated. However, through our series of statistical tests, we could be able to conclude that current mortgage rate is significant at 1% level; mortgage rate at lag one time period is significant at 5% level; both real personal incomes at lag one time period and unemployment rate at lag two time period are significant at 10% level. REFERENCES Aspergis N. (2003) Housing prices and macroeconomic factors: prospects within the European Monetary Union, International Real Estate Review, Vol. 6 No. 1, pp. 63-74. Baffoe-Bonnie J. (1998) The dynamic impact of macroeconomic aggregates on housing prices and stock of houses: a national and regional analysis, Journal of Real Estate Finance and Economics, 17(2), 179–197. Bradley M. G. Gabriel S. A. and Wohar M. E. (1995) The thrift crisis, mortgage-credit intermediation, and Housing Activity. Journal of Money, Credit, and Banking, 27(2), 476497. Campbell J.Y. and Cocco J. (2007) How do house prices affect consumption? Evidence from micro data, Journal of Monetary Economics, Vol. 54, pp. 591-621. Ewing B.T. and Wang Y. (2005) Single Housing Starts and Macroeconomic Activity: An Application of Generalized Impulse Response Analysis. Applied Economics Letters, 12(3), 187-190. Feldstein M. and Summers L. (1978) Inflation, tax rules, and the long-term interest rate, Brookings Papers on Economic Activity, Vol. 1, pp. 61-109. Follain J.R. Jr (1982) Does inflation affect real behavior: the case of housing, Southern Economic Journal, Vol. 48 No. 3, pp. 570-82. Fratantoni M. and Schuh S. (2003) Monetary Policy, Housing, and Heterogeneous Regional Markets, Journal of Money, Credit and Banking, 35(4), 557-589. Garcia J.A.B. and Hernandez R.J.E. (2004) User cost changes, unemployment and homeownership: evidence from Spain, Urban Studies, Vol. 41 No. 3, pp. 563-78. Goodwin T.H. (1986) Inflation, risk, taxes, and the demand for owner-occupied housing, Review of Economics and Statistics, Vol. 68 No. 2, pp. 197-206. Hendershott P.H. (1980) Real user costs and the demand for single family housing, Brookings Papers on Economic Activity, Vol. 2, pp. 401-52. Huang D.S. (1973) Short-Run Instability in Single-Family Housing Starts, Journal of the American Statistical Association, 68(344),788- 792. Kearl J.R. (1979) Inflation, mortgage, and housing, The Journal of Political Economy, Vol. 87 No. 5, pp. 1115-38. Lyytikäinen, T. (2009) Three-rate property taxation and housing construction, Journal of Urban Economics, 65(3), 305-313. 273 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Oswald A. (1996) A conjecture on the explanation for high unemployment in the industrialized nations: part I, Warwick Economics Research Paper 475, University of Warwick, Coventry, pp. 197-206. Puri A.K. and Lierop J.V. (1988) Forecasting Housing Starts, International Journal of Forecasting, 4, 125-134. Rapach D.E. and Strauss J.K. (2009) Differences in housing price forecastability across US states, International Journal of Forecasting, 25(2), 351-372. Rosen, H.S. and Rosen, K.T. (1980) Federal taxes and homeownership: evidence from time series, Journal of Political Economy, Vol. 88 No. 11, pp. 59-75. Sander W. and Testa W.A. (2009) Education and Household Location in Chicago. Growth and Change, 40(1), 116–139. Stephenson, c. (2010) Home Sales as an Economic Indicator, http://voices.yahoo.com/homesales-as-economic-indicator-5362321.html?cat=3. Taylor J. B. (2007) Housing and Monetary Policy), NBER Working Paper No. W13682. Available at SSRN: http://ssrn.com/abstract=1077808. Thom R. (1985) The Relationship between Housing Starts and Mortgage Availability, The Review of Economics and Statistics, 67(4), 693-696. Vargas-Silva C. (2008) Monetary Policy and the U.S. Housing Market: A VAR Analysis Imposing Sign Restrictions. Journal of Macroeconomics, 30(3), 977-990. Corporate Environmental Reporting: Approaches And Challenges Yasemin Köse Bulent Ecevit University, Faculty of Economics and Administrative Sciences Business Department, Accounting and Finance 67100, Zonguldak, Turkey E-mail:yekose@gmail.com Abstract Sustainable development issue have become increasingly important to a range of stakeholders and attention has focused on the environmental impacts of corporate activities. Within this context, investors and other stakeholders demand for reliable and accurate information regarding environmental performance. Thus sustainable or environmental reporting has arisen as a challenging and attractive growth area for accounting professionals (Bell and Lehman 1999). One of the most challenging issue in environmental reporting is how and what corporations should report to meet demands of various stakeholders. Reporting about environmental issues may embrace information both in traditional financial reports and in any other reports. For environmental reporting, guidelines have been published by various parties since the beginning of the nineties (IIIEE Report 2002). Considerable 274 � 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. 1303 Title A name given to the resource The Factors Which Caused The Decline In The Amount Of The Newly One Family Houses Sold In Us Author Author Ali , Cüneyt Çetin Abstract A summary of the resource. The new privately owned one-family house sold (C25) is recognized as great indicator for economy. The monthly data in February 2011 was 250,000 houses sold. Compared to five years ago, 1,061,000 in 2006 were decreased by 76%. What are the causes to the dramatic decline of number of C25? The purpose of this paper is to analyze factors that determine the decline of number of C25 in US. Therefore, in this study, dependent variable is the new privately owned one-family house sold. Independent variables include 30 years mortgage rate, real personal income, unemployment rate, population, and house price index. The results indicate when the interest rate increases 1%, the number of new privately owned one-family houses sold decreases by 20 thousand. When the unemployment rate increases 1%, the number of new privately owned one-family houses sold decreases 81 thousand, holding all other variables constant. We thought when price goes up the demand should go down. But it doesn’t fit in this study. Income and house sold have positive relationship but it’s not significant. It shows that real personal income and unemployment have a high correlation. For the population variable, the coefficient is a negative number. Even though the p-value indicates that this result is not significant, we still couldn’t figure out the cause of this negative relation. The result of monthly dummy test indicates that none of the months has significant effects. However, from March to July the slopes of the months have positive or lower negative effects. Consequently, it’s impossible to determine all the causes to the number of new house sold since many factors are interrelated. However, through our series of statistical tests, we could be able to conclude that current mortgage rate is significant at 1% level; mortgage rate at lag one time period is significant at 5% level; both real personal incomes at lag one time period and unemployment rate at lag two time period are significant at 10% level. Keywords: house sold, mortgage rate, income level, unemployment rate, population increases, house price index 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/5c03f01c440dc4b3a13c9a24bcb002eb.docx 5f6f16649c684b8f9ed0b3cc8510900b https://omeka.ibu.edu.ba/files/original/150447b5c08fc4356209575f28494b03.pdf 85db73d45ce5258e10447018488e6f00 PDF Text Text The Features and Challenges of Democratization Process in the Balkans Gloria Shkurti Sakarya University Turkey gshkurti09@epoka.edu.al SalihOzcan Epoka University Albania sozcan@epoka.edu.al Abstract: This paper aims to analyze the consolidation of democracy in the Balkans, but mostly demonstrated in the case of Albania. The process of democratization in the Balkans has undergone a long path since the fall of the communist regime in the region. As a consequence the analysis of the democratization in the Balkans has remained vague and difficult to be framed. This research was conducted on bases of quantitative and qualitative researches. In terms of the qualitative research there were conducted nine interviews with people that are competent in this field (such as politicians, analysts, political scientist etc.). The interview consisted of 6 open-ended and fully structured questions. Furthermore these interviews were conducted via e-mail or face-to-face. Secondly, the quantitative study will be based on primary data that is taken from the reports of Freedom House and the Economist Intelligence Unit. On basis of this study the democratization process can be understood through two main approaches: political and social approach. In terms of the political approach there should be considered the history of the Balkans (conflicts, wars and communist regimes). On the other hand there is the social approach related with the ill feelings transmitted from one generation to another, which indirectly affects the consolidation of democracy. Moreover in this article the process of democratization is analyzed also in terms of the external factors such as that EU or USA. In addition there is done also a short comparison, between Balkan states and other ex-communist states (such as: East European states, Czechoslovakia, Poland, etc.). After assessing all the elements, the future of democratization process in the Balkans and especially in Albania gives space for being optimistic and pessimistic at the same time. While optimism is related with the fact that there is no other path to be followed except democratization, pessimism on the other hand is related with the will of the political class. Key words: democratization, consolidation of democracy, the Balkans, challenges, features. 37 � 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. 2468 Title A name given to the resource The Features and Challenges of Democratization Process in the Balkans Author Author SHKURTI, Gloria OZCAN, Salih Abstract A summary of the resource. This paper aims to analyze the consolidation of democracy in the Balkans, but mostly demonstrated in the case of Albania. The process of democratization in the Balkans has undergone a long path since the fall of the communist regime in the region. As a consequence the analysis of the democratization in the Balkans has remained vague and difficult to be framed. This research was conducted on bases of quantitative and qualitative researches. In terms of the qualitative research there were conducted nine interviews with people that are competent in this field (such as politicians, analysts, political scientist etc.). The interview consisted of 6 open-ended and fully structured questions. Furthermore these interviews were conducted via e-mail or face-to-face. Secondly, the quantitative study will be based on primary data that is taken from the reports of Freedom House and the Economist Intelligence Unit. On basis of this study the democratization process can be understood through two main approaches: political and social approach. In terms of the political approach there should be considered the history of the Balkans (conflicts, wars and communist regimes). On the other hand there is the social approach related with the ill feelings transmitted from one generation to another, which indirectly affects the consolidation of democracy. Moreover in this article the process of democratization is analyzed also in terms of the external factors such as that EU or USA. In addition there is done also a short comparison, between Balkan states and other ex-communist states (such as: East European states, Czechoslovakia, Poland, etc.). After assessing all the elements, the future of democratization process in the Balkans and especially in Albania gives space for being optimistic and pessimistic at the same time. While optimism is related with the fact that there is no other path to be followed except democratization, pessimism on the other hand is related with the will of the political class. Key words: democratization, consolidation of democracy, the Balkans, challenges, features. Publisher An entity responsible for making the resource available International Burch University Date A point or period of time associated with an event in the lifecycle of the resource 2014-04-24 Keywords Keywords. Article PeerReviewed Identifier An unambiguous reference to the resource within a given context ISSN 2303-4564 H Social Sciences (General) https://omeka.ibu.edu.ba/files/original/242cb2fec02364c54351a1cb38919ff7.docx e55b19f8f064abae21a983b166b9ee55 https://omeka.ibu.edu.ba/files/original/895a355c6f9f4299b0380400afbe2b7b.pdf ecfdbad4123c3a46800e5127a690be64 PDF Text Text The Feldstein–Horioka Puzzle across EU Members: A Panel Approach İbrahim Örnek University of KahramanmarasSütcü Imam Turkey iornek@hotmail.com SelenUtlu University of Gaziantep Turkey selenu@gmail.com Abstract: The degree of integration to the international capital markets is a crucial issue for the economic policy implementations in developing countries. A major determinant of the degree of international capital mobility is the saving-investment association. One of the biggest problems of developing countries is the insufficiency of savings for financing their investments that is crucial for economic growth. This gap is financed by foreign capital in today’s global economies. It is generally believed that, the correlation between national savings and domestic investments becomes weak when there is high capital mobility between countries. The degree of capital mobility through the domestic savinginvestment interaction is first analyzed by Feldstein and Horioka (1980). Feldstein and Horioka (1980) used regression in the investment ratio against a constant and the saving ratio in a cross section of 16 industrialized countries, which are OECD members, over the period 1960-1974 and found that the coefficient on saving was in the range of 0.850.95. They interpreted this finding as indicating that 85-95 % of national savings was invested in the country of origin, which implied a rejection of perfect capital mobility. The basic conclusion of Feldstein and Horioka’s analysis is that an increase in domestic saving has a substantial effect on the level of domestic investment. However, with perfect world capital mobility, there is little or no relation between the domestic investment in a country and the amount of savings generated in that country. This result was known in the literature as the Feldstein-Horioka Puzzle. Feldstein and Horioka (1980) argued that the relationship between domestic investment and domestic saving rates is related with the international capital mobility and thus caused the rise of a puzzle in the economic literature. The purpose of this paper is to investigate the level of capital mobility in European Union members using the Feldstein–Horioka puzzle proposed by Feldstein and Horioka (1980) in order to investigate relations between saving and investment flows. Keywords: Feldstein–Horioka puzzle, Saving-investment, Capital mobility, European Union, Panel 49 � 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. 2481 Title A name given to the resource The Feldstein–Horioka Puzzle across EU Members: A Panel Approach Author Author ÖRNEK, İbrahim UTLU, Selen Abstract A summary of the resource. The degree of integration to the international capital markets is a crucial issue for the economic policy implementations in developing countries. A major determinant of the degree of international capital mobility is the saving-investment association. One of the biggest problems of developing countries is the insufficiency of savings for financing their investments that is crucial for economic growth. This gap is financed by foreign capital in today’s global economies. It is generally believed that, the correlation between national savings and domestic investments becomes weak when there is high capital mobility between countries. The degree of capital mobility through the domestic saving-investment interaction is first analyzed by Feldstein and Horioka (1980). Feldstein and Horioka (1980) used regression in the investment ratio against a constant and the saving ratio in a cross section of 16 industrialized countries, which are OECD members, over the period 1960-1974 and found that the coefficient on saving was in the range of 0.85-0.95. They interpreted this finding as indicating that 85-95 % of national savings was invested in the country of origin, which implied a rejection of perfect capital mobility. The basic conclusion of Feldstein and Horioka’s analysis is that an increase in domestic saving has a substantial effect on the level of domestic investment. However, with perfect world capital mobility, there is little or no relation between the domestic investment in a country and the amount of savings generated in that country. This result was known in the literature as the Feldstein-Horioka Puzzle. Feldstein and Horioka (1980) argued that the relationship between domestic investment and domestic saving rates is related with the international capital mobility and thus caused the rise of a puzzle in the economic literature. The purpose of this paper is to investigate the level of capital mobility in European Union members using the Feldstein–Horioka puzzle proposed by Feldstein and Horioka (1980) in order to investigate relations between saving and investment flows. Keywords: Feldstein–Horioka puzzle, Saving-investment, Capital mobility, European Union, Panel Publisher An entity responsible for making the resource available International Burch University Date A point or period of time associated with an event in the lifecycle of the resource 2014-04-24 Keywords Keywords. Article PeerReviewed Identifier An unambiguous reference to the resource within a given context ISSN 2303-4564 H Social Sciences (General) https://omeka.ibu.edu.ba/files/original/f67d92e62046a721a2b8c58d76d4b293.docx 3792377453647ac08b42d9f60b4aa363 https://omeka.ibu.edu.ba/files/original/b4afd899e170c6be7c6cc79845ebb62b.pdf 05e0749d349e233e2bdf60323a5b323b PDF Text Text The Feldstein–Horioka Puzzle among EU Members: A Panel Approach İbrahim Örnek University of KahramanmarasSütcü Imam Turkey iornek@hotmail.com SelenUtlu University of Gaziantep Turkey selenu@gmail.com Abstract: The degree of integration to the international capital markets is a crucial issue for the economic policy implementations in developing countries. A major determinant of the degree of international capital mobility is the saving-investment association. One of the biggest problems of developing countries is the insufficiency of savings. This gap is financed by foreign capital in today’s global economies. It is generally believed that, the correlation between national savings and domestic investments becomes weak when there is high capital mobility between countries. The degree of capital mobility through the domestic saving-investment interaction is firstly analyzed by Feldstein and Horioka (1980). The purpose of this paper is to investigate the level of capital mobility in European Union members in a period of 1980-2012, with using the Feldstein–Horiokamethod. Feldstein and Horioka (1980) regressed the investment ratio against a constant and the saving ratio in a cross section of 16 industrialized countries, which are OECD members, over the period 1960-1974 and found that the coefficient on saving was in the range of 0.85-0.95. The basic conclusion of Feldstein and Horioka’s analysis is that an increase in domestic saving has a substantial effect on the level of domestic investment. However, with perfect world capital mobility, there is little or no relation between the domestic investment in a country and the amount of savings generated in that country. This result is known in the literature as the Feldstein-Horioka Puzzle. Feldstein and Horioka (1980) argued that the relationship between domestic investment and domestic saving rates is related with the international capital mobility and thus caused the rise of a puzzle in the economic literature. Keywords: Feldstein–Horioka puzzle, Saving-investment, Capital mobility, European Union, Panel 15 � 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. 2441 Title A name given to the resource The Feldstein–Horioka Puzzle among EU Members: A Panel Approach Author Author ÖRNEK, İbrahim UTLU, Selen Abstract A summary of the resource. The degree of integration to the international capital markets is a crucial issue for the economic policy implementations in developing countries. A major determinant of the degree of international capital mobility is the saving-investment association. One of the biggest problems of developing countries is the insufficiency of savings. This gap is financed by foreign capital in today’s global economies. It is generally believed that, the correlation between national savings and domestic investments becomes weak when there is high capital mobility between countries. The degree of capital mobility through the domestic saving-investment interaction is firstly analyzed by Feldstein and Horioka (1980). The purpose of this paper is to investigate the level of capital mobility in European Union members in a period of 1980-2012, with using the Feldstein–Horiokamethod. Feldstein and Horioka (1980) regressed the investment ratio against a constant and the saving ratio in a cross section of 16 industrialized countries, which are OECD members, over the period 1960-1974 and found that the coefficient on saving was in the range of 0.85-0.95. The basic conclusion of Feldstein and Horioka’s analysis is that an increase in domestic saving has a substantial effect on the level of domestic investment. However, with perfect world capital mobility, there is little or no relation between the domestic investment in a country and the amount of savings generated in that country. This result is known in the literature as the Feldstein-Horioka Puzzle. Feldstein and Horioka (1980) argued that the relationship between domestic investment and domestic saving rates is related with the international capital mobility and thus caused the rise of a puzzle in the economic literature. Keywords: Feldstein–Horioka puzzle, Saving-investment, Capital mobility, European Union, Panel Publisher An entity responsible for making the resource available International Burch University Date A point or period of time associated with an event in the lifecycle of the resource 2014-04-24 Keywords Keywords. Article PeerReviewed Identifier An unambiguous reference to the resource within a given context ISSN 2303-4564 H Social Sciences (General) https://omeka.ibu.edu.ba/files/original/f6dde50712eac1c81fe43b222a8762ae.pdf 01c904f3b08e52c10f201774f0e12c18 PDF Text Text 1st International Conference on Foreign Language Teaching and Applied Linguistics May 5-7 2011 Sarajevo The Findings upon the Designation of Turkish Words among Balkan Languages Fatih ĠYĠYOL Department of Turkish Language and Literature International Burch University, Bosnia Herzegovina fiyiyol@ibu.edu.ba Ahmet Musab KESMECĠ Department of Turkish Language and Literature Sùleyman ġah University, Turkey akesmeci@ssu.edu.tr Abstract: The presence of the Turks in the geography of Balkans can be track back to the centuries before Ottomans. The actual impact of the Turkish language and culture began with the start of Ottoman conquests. With the Ottoman conquest, there have been great changes on the structure of the Balkan communities. As a result of this comprehensive and large impact, thousands of Turkish words entered into the Balkanic Languages. The quantity of Turkish words, their effect of the Balkanic Cultures and Languages have directed the researchers to search these words. The researchers who investigated the Turkish words in the Balkanic Languages faced a fundamental problem. The main problem that the researchers faced was the issue of designation of these words in question. Due to the appearance of the words of Arabic and Persian origin along with Turkish, some researchers have applied the term ―Orientalism‖ for these words in question. Since the vast majority of words in the Balkanic Languages are Turkish, the researchers, considering the fact that Arabic and Persian words entered into the Balkanic Languages through Turkish, have preferred the concept ―Turkism‖ for these words. Researchers, without making a detailed evaluation on either ―Orientalism‖ or ―Turkism,‖ have stated their more general preferences. However, the designation issue of these words, which have such effect on the Balkanic Languages and their numbers getting closer to ten thousand in some languages, requires a detailed analysis in order to formulate an opinion. The purpose of this study is to evaluate the words in question in terms of grammatical, cultural and historical process and to contribute to the issue of designating through Ottoman institutions and community life. Within the scope of this study, the emphasis is given, in addition to all the Balkanic Languages, on the Bosnian-Serbian-Croatian and Albanian Languages where Turkish words are dominantly present. Key Words: Turkish, Balkanic Languages, Orientalism, Turkism, culture and language. Introduction The existence of the Turkish communities in the Balkans and the cultural effects of these communities can be tracked back to a much earlier periods before the Ottoman Empire evoked its impact on the region. It is known that the Avars, Cumans and Pechenegs continued their existence in certain periods and contributed to the cultural structure of this Geography. Settling in the area in the VI. century, the Avars is a Turkic tribe which affected the Balkanic communities from the socio-cultural perspective. ―Ban‖ title that Bosnians and Croatians confer to their rulers is a word of Avar origin. In the Slavic Languages of the Balkans, the word ―Obrovac,‖ which means the place where the Avars live, even seen in today‘s toponomy, derived from the word ―Obri,‖ which is used for the Avars, is pretty significant in displaying the extend of Avars‘ effect in the region (Malcolm, 2002: 6). Before the Ottoman conquest, the presence of the word ―Kaduna/Kadın‖ in Bosnian, Serbian and Croatian is another example for the effect of the Avarian Turkish on the Slavic Languages in the Balkans (Skaljic, 1965: 12). In the Balkans, the effect of pre-Ottoman Turkish communities is also seen in Albanian Language. Ġstyan Schùtz, Hungarian Linguist, states that Turkish was a language which affected the Albanian Language between X-XIV centuries. According to Schùtz, the Albanian men were returning to their villages in winter after working as a shephard. Therefore, an important period of the year, only women and children remained in the villages. When Pecheneg Turks raided these villages, upon seeing the crowd consisting of women and children, 471 �1st International Conference on Foreign Language Teaching and Applied Linguistics May 5-7 2011 Sarajevo they used the ―Katun, kadın‖ phrase for Albanian villages. This word, entering into Albanian, caused the villages to be called ―Katund‖ (Bayraktar, 2009: 1084). The effect of Turks before Ottoman in the Balkanic communities is not limited with the examples we provided. The debate over Proto-Bulgarians being people of Turkish origin, Cuman communication with Romanians in the West Balkans, the presence of Western Huns in the Eastern Europe can be approached from the aspect of the effect of Turkic communities in the Balkans before Ottoman. It is likely that these Turkic tribes contributed to the cultures of the Balkanic communities, which are as similar and complex as to one another. However, considering the effect of the Ottoman Empire in the Balkans, the effects of communities in question are seen to be limited and gradually fused into , indigenous cultures, within the time limited and is seen to be fused into the local cultures. The effect of Ottoman in the Balkanic Cultures With the Ottoman political and military influence on the geopraphy of the Balkans, the interaction between the Turkish culture and the Balkanic communities began. Indirect communication began before the conquests turned into a direct and more comprehensive impact with the conquests. Reconstruction of the conquered lands, establishment of the cities, and occurrence of the Turkish-Islamic cultural basin around these cities, ensured the emergence of permanent, deep and great effects, the results of which has lasted till today. It is possible to classify the Balkanic communities in terms of the socio-cultural effect caused by the Ottomans. The first group 158is Bosnians and Albanians, who were Islamized in vast majorities after the conquest movements. The second group is Serbs, Croats, Macedonians, Greeks, Bulgarians, Romanians who protected their OrthodoxCatholic structures and the Islamization was limited. The interactions of these two groups from Turkish culture and the ratio of Turkish words in their language have been different. With the new religion, while the cultural change in the Bosnian and Albanian communities was deeper and more comprehensive; the other Balkan communities regarded them as ―Turkicized masses.‖159 However, the Balkanic communities in the second group, who preferred to be ―Reaya,‖ have deeply experienced the effect of the Ottoman-Turkish culture, even though not as much as the Islamized communities. According to Maria Todorova, who emphasized the importance of the influence that Ottoman‘s left in the Balkanic communities, there are two actors in the history of the Balkans. One of them is the political and religious influence of Byzantine and the other is the Ottomans, who gave names to the peninsula from their language and established the longest political unity. Todorova has attracted such attention to this effect, ―it is not an exaggeration to conclude that the Balkans are the Ottoman legacy.‖ To Todorova, ―In the field of Demography and the public culture, the Ottoman legacy has left lasting and continuous effect‖(Todorova, 2006: 36). Other researcher, who indicated this effect that Bulgarian historian emphasized, is Vuk Karadzic. Serbian Linguistic Karadzic, while refering to the folk culture of his era, expresses the traditional lives of the Serbians residing in the cities. Karadzic states that while the women in Belgrade wore scarves like Muslim women did and the Serbian men, wore turban and smoked hookah (Castellan, 1995: 148). The effect of the Turkish culture in the Ottoman period in the Balkans can also be seen in language and literature. Author Sofroniy Vraçanski (1739-1813) states that since Bulgarians, living in the cities, knew Turkish better than they knew Bulgarian, they consistently used Turkish words in their works (Yalçın, 2009: 572). Another author, who expresses the effect of Turkish in the Bulgarian cities is Ivan Vazov. Living in the middle of the XIX century, Vazov identified the language spoken in the Bulgarian cities as ―Almost the half of the languages spoken in our cities was Turkish‖ (Öztekten, 2004:32). In the Balkanic communities during the Ottoman period, a literature called ―Alhamijado/Alhamiyado‖ emerged, written in Ottoman alphabet and consisting of Turkish, Arabic, Persian and local Balkanic languages. The representatives of this literature adapted the Ottoman alphabet according to their own languages. Some of the representatives of the Alhamijado Literature wrote their works mainly in Turkish, Arabic and Persian. Yet other repsentatives of this literature prefered their own languages in their literary works. The Alhamijado poets built their works upon Turkish poetry tradition and the culture of dervish lodges. Specially the Alhamijado poets 158 Torbes and Pomaks (Macedonians Muslim), whose origins are controversial, can also be included in this group. Gypsies, who are seen in almost every region of the geography of the Balkans and Tatars, living in Romania, are not taken into the scope of this study. Since the non-Turkish communities are discussed in classification, the Balkan Turks were not included. 159 Vatican, Orthodox churches and Balkanic communities regarded the Islamized communities in the Balkans as ―Turkicized masses.‖ Konstantin Mikhailovich, towards the end of XV century, states this for the Islamized Slavs ―...in spite of everything, the number of our people voluntarily being Turkicized is rising every year...‖ Benedict Kurupesic, for the same community, in 1530, used such a statement"... The Turks left them a religion, young people were Turkicized..."(Nurkiç, 2007: 63). It is accepted opinion that the "Potur" statement, used for Bosnian Muslims, is derived from the verb "Poturçiti / Turkicized"(Malcolm, 2005: 60-61). "Turkization," expressed in the sources, is not used a community‘s change of language and entisite, but for the Islamization of the communities in question. 472 �1st International Conference on Foreign Language Teaching and Applied Linguistics May 5-7 2011 Sarajevo of Bosnian and Albanian origins also became important representatives of classical Turkish poetry and dervish lodge literature. Many poets, lik Muhammed Karamusic, Ziyai, Vahdeti, Dervis-Pasha, Mezaki, Hasan Kaimi, Sukkeri, Asım Yusuf Celebi, Mehmed Meyli, Ahmed Hatem, Fadıl-Pasha Serifovic, Arif Hikmet (Nametak, 1997), were important intellectuals of their own periods writing in Turkish and Balkanic languages. In the works that these poets wrote in their own languages, a deep Turkish effect can be noticed. One of the poets that we clearly see this effect is Hasan Kaimi, a poet of Dervish lodge. Bosnian Turkologist Fehim Nametak makes such an assessment of the head of Sarajevo Hadji Sinan Dervish Convent Hasan Kaimi‘s famous poem ―Ostante se tutuna,160‖ ―We are not so sure either Turkish words are more or Bosnian words are‖ (Nametak, 1989: 121). The Turkish Words that Entered into Balkanic Languages During the Ottoman period, Turkish word entered in ot the Balkanic languaes in many ways. Particularly young people of Balkanic origin receiving their religious education in the Ottoman cities like Istanbul is one of the basic reasons. Young people of Bosnian, Albanian, Torbes, etc., origin obtained classical madrasah-dervish lodge education in Istanbul,Bursa and in other Ottoman learning centers and reached a proficient level of reading and writing literary Turkish. When these young people returned to their countries after being equipped with Ottoman-Islamic culture, they used these words and concepts belonging to the language they learnt along with their own local language and this way Turkish words passed on to the masses of public (Metaj, 2009: 10; SkajliĤ, 1965: 13). Another reason for extensive use of Turkish words in the Balkanic languages is the Balkan native folk poets‘ writing in Turkish or using great many Turkish words in the local language. Attributing the extensive use of Turkish word in Serbian, Croatian, and Bosnian to the folk poets and scholars, Grga Martic uses such remarkable statements: ―For this reason, the number of Turkish words increased in our folk poems and people used these words and they could not write poems with them. These words are similar to salt in a dish, how you cannot get the taste of a dish without adding salt in it, the public could not write poems without those words‖ (SkajliĤ, 1965: 13). The Ottoman perception of urbanization and the effect of Turkish in the Balkan cities it built and developed, due to various reasons like Turkish being the state language, gave a significant number of words to the Balkanic languages. The quantity of the Turkish words in the Balkanic languages and the effect on the public culture differ from one community to another. The Turkish words that are seen in the language we calle Balkan Slavic language, which includes: Bosnian, Serbian and Croatian languages. The most comprehensive study, which examines these words in terms of semantics and morphology and deals with the changes of these words in the languages in question, is Abdullah SkajliĤ‘s work called ―Turcizmi u Srpsko-Hırvatskom Jeziku/ The Dictionary of Turkish Words Available in Serbian and Bosnian Languages‖, which was published in 1965. SkajliĤ states that he discovered 8.878 words and 6.878 terms in these languages (SkajliĤ, 1965: 23). A variety of studies were carried out on the presence of Turkish words in the Albanian language. In Tahir Dizdari‘s researches, the number of Turkish words identified in Albanian is 4406 (Kadiu-Abdiu, 2009: 1230). Ilaz Metaj, in the dictionary called ―Orientalizmat‖ that he published in 2009, identified 3600 words (Metaj, 2009: 7). Recent studies show that the number of Turkish words available in the Albanian language is almost 5000 (Bayraktar, 2009: 1086). Various studies on the number of Turkish words in the other Balkanic languages were conducted as well: but these studies are not as comprehensive as the studies conducted in Serbian, Croatian, Bosnian and Albanian. For this reason, it is difficult to give figures closer to the realistic existence of the Turkish words in Romanian, Greek and Bulgarian. Ivan Gaberov, in the dictionary called ―Reçnik na Çujdite Dumi v Bilgarskiy/ Dictionary of Foreign Words in Bulgarian‖ that he published in 1998, states that 3548 Turkish words are still in use today in Bulgarian language. The most comprehensive study examining the effect of Turkish on Romanian dates back to XX. century. L. Ńaineanu detected 3900 Turkish elements in Romanian. Today, the presence of Turkish words in Romanian is seen to be decreasing compared to early XX. century. Muhummed Nurlu, in his work called ―Turkish Traces in Romanian‖ in 2002, identified 1200 Turkish words. The researchers, who conducted significant studies on Turkish elements in Greek, are K. Kukkidis and P. Georgias. While Kukkidis identifies the existence of Turkish words in Greek as 3000, Georgias does it as 1968161 (Öztekten, 2004). 160 A sample stanza from the poem in question: Frenkler buni satarlar Frenkler bunu satarlar Sudøk içre tutarlar Suduk içre tutarlar Ba ne zehir jutarlar Bak ne zehir yutarlar Ostante se tutuna Tütünden vazgeçin (Ġyiyol, 2010a: 276). 161 We tried to mention the basic works related with the Turkish words in the Balkanic languages. However, the Turkish words in the Balkanic languages and So many studies have been conducted on the Turkish words and their sound, structural 473 �1st International Conference on Foreign Language Teaching and Applied Linguistics May 5-7 2011 Sarajevo The presence of Turkish words in any of the Balkanic languages is seen to differ from one study to another. Although there are several reason for this difference, but it is mostly related to the study‘s scope and period. While some of the researchers deal with the Turkish words in the literary language, yet one section of researchers deal with written and spoken language together. The number of Turkish words in the Balkanic languages varies according to the quality of the source scanning and compilation work. Another reason for changes of Turkish words is related to the period study was conducted. The number of Turkish words gradually decreased with the departure of Ottoman from the region. For this reason, there will be difference in word numbers between the studies conducted at the end of XIX. century and today. The Designation of Turkish Words in the Balkanic Languages The multitude of the presence of Turkish words in the Balkanic lanaguages, the frequent use of these words in the public culture and literary works, revealed the need to designate these words which entered into the Balkanic languages through Turkish. The language and cultural researchers of the Balkanic communities have tried to designate these words, which are found in significant numbers in their languages, in different concepts such as; ―Orientalism,‖ ―Turcizmi-Turcizam/Turkism,‖ ―Arabism,‖ and ―Balkanism.‖ The designation problem of these words, which have been conceptualized as ―Turkism‖ or ―Orientalism‖ by vast majority of the researchers, should be dealt with in various ways. The morphological and semantical features of these words, through which cultural basin they entered into, their equivalences in the folk culture and their positions in the literary language will help to solve the problem. Russian linguist Agnia Desnitskaja deals with the Turkish words in the Balkanic languages starting from the Albanian examples. According to Desnitskaja, the Albanian youth adopted Arabic and Persian words which they are taught at the Ottoman educational institutions. Desnitskaja expresses that since the Ottoman‘s literary language was Persian and religious language was Arabic, therefore these words should be called ―Orientalism.‖ Albanian linguist Tahir Dizdari prefered to use the term ―Orientalism‖for the words which entered into Albanian in the Ottoman period. Dizdari, in his another article, designated the words in question with ―Turkism‖ concept. Tahir Dizdari‘s use of these two concepts indicates that author cannot make a definite choice between these two concepts (Abazı-Egro, 2002: 5). Ilaz Metaj,in his dictionary called ―Orientalizmat/Orientalists‖ in which he brought Turkish words in Albanian together, prefered to use ―Oryantalizm‖ concept for these words.Metah states that he encountered a basic problem in his dictionary work and these words in question have problems of designation. The words in question, according to Metaj, came from three oriental languages like Turkish, Arabic and Persian. For this reason, he prefered to use Orientalism concept for designating these words in question. Ilaz Metaj expresses the effect of Albanian Orientalist Fethi Mehdiu on his choice of this concept (Metaj, 2009: 7). Hanka Vajzovic, in the work entitled ―Orijentalizmi u Knijiţevnom Djelu-Lingvistička Analazia/ The Linguistic Analysis of Orientalisms in the Literay Language,‖ deals with the words which entered into Bosnian, Serbian and Croatian from Turkish and thought Turkish. Vajzovic states that he used ―Turkism‖ concept for the words in question but, nowadays the ―Orientalism‖ concept began to be widespared. According to Vajzovic, although the vast majority of the words in Serbian, Croatian and Bosnian are Turkish, but he stated that also the words of Arabic and Persian can also be found(VajzoviĤ, 1999: 11). While Vajzovic prefered Orientalism concept at the beginning of his work, at the summary section, he states that Orientalism is a synonym of ―Turkism‖ concept. Many linguists designated the words which entered into the Balkanic languages during the Ottoman period as ―Turkism.‖ Abdullah SkajliĤ deals with this matter in his comprehensive work called Turcizmi u Srpsko-Hırvatskom Jeziku. SkajliĤ provides information on the studies which are examining the Turkish elements in the Balkanic languages at the begining of his dictionary. According to him, the studies, which are conducted on this subject, are full of inaccuracies due to several reasons. SkajliĤ gives an example to the more explicit inaccuracies by Otto Blau‘s claim that the word ―Eyvallah‖ is the derivative of the Bosnian word ―Eh! Hvala/ Eh! Thank you‖ (SkajliĤ: 1965: 17). SkajliĤ expresses that most of the words, which entered into Bosnian, Serbian and Croatian, are Turkish or possess the semantic and morphological features of Turkish. According to him, the number of Arabic and Persian words which entered into Bosnian, Serbian and Croatian, not through Turkish, are very limited. For this reason, these words in question should be called ―Turcizmi/Turkishm‖ (SkajliĤ, 1965: 24). According to Eqrem Cabej, who designated the words that entered into the Balkanic languages during the Ottoman period with Albanian examples, these words should be designated as ―Turkism.‖ Cabej states that Arabic and Persian words in Albanian entered into the language though Turkish, therefore it will be more accurate to designate them as ―Turkism.‖ Starting from the Albaniam example, another researcher, who took up the task of designating the words in question, is Norbert Borevsky. According to Borevsky, there are three main and semantic features in the Balkanic languages and the reflection of Turkish words and group of words on social life. See. (SkajliĤ, 1965; Metaj, 2009; Öztekten, 2004). 474 �1st International Conference on Foreign Language Teaching and Applied Linguistics May 5-7 2011 Sarajevo reasons for these words to be called ―Turkism.‖ First, the number of people in the Albanian community who knew Arabic and Persian were very few. The people of Albanian origin, who have a good command of Arabic and Persian, did not live in Albania and did not get into an important communication with the Albanian community. These intellectuals were mostly assigned in the important cities of the Empire like Istanbul. Therefore, there was no significant communication between these superior Arabic-Persian knowing party and the Albanian community. Since these Arabic and Persian words entered in to Albanian through Turkish, they are designated as ―Turkism.‖ Secondly, it is difficult to say that Arabic and Persian words, which entered into Albanian, are directly taken from these languages. These words are also present in Turkish. Sincere there are no any Arabic and Persian words, which are present in Albanian and not available in Turkish, these words should be called ―Turkism.‖ Thirdly, in the Arabic-Persian words in Albanian, there phonetic features of Turkish can be seen. In order to call these words, which are left from the Ottoman period as ―Orientalism,‖ the pronunciation and grammatical features of these words in question should have been out of Turkish usage. However, no such word has been located till now (Abazi-Egro, 2002: 6). The researchers, who were explaining the words that entered into the Balkanic languages during the Ottoman period with ―Turkism‖ concept, have accepted the discourse of the vast majority of these words being Turkish origin as the point of reference. Arabic and Persian words in the Balkanic languages, other than Turkish, have taken shape according to Turkish grammar. Therefore, since these words are Turkicized, it is more appropriate to use ―Turkism‖ concept. The main claim of those defending Orientalism is that even though the majority of these words in question are Turkish, Arabic and Persian words are also available. Therefore these words belong to the east. For this reason, it is more appropriate to call these words in question as ―Orientalist.‖ In our view, the designation of the Turkish words and the other words which entered in the Balkanic languages through Turkish should be considered various angles. The position of these words in the Balkanic languages and cultures, their function in the Balkanic folklore, their semantics, syntax and morphological features should be taken into consideration. When the association of Turkism and Orientalism concepts and the question of whether these concepts will be adequate or not in designation of these words are evaluated, it will be possible to reach a conclusion based on scientific data. One of the basic factors to discuss in designating Arabic, Persian and Turkish words in the Balkanic languages is to consider the historical-cultural basis of the passage of these words in question. The literary language is called ―Ottoman Turkish‖ during the Ottoman Imperial period. In this language, although Turkish is the dominating element, but the influence of Arabic and Persian, the two major languages of the Islamic civilization, is present. Ottoman Turkish accommodated Arabic and Persian elements well and was formed as a language with wide range of vocabulary. So many great poets and authors and significant verse and prose work have emerged from the body of this language. Therefore, the dominant language in the Balkans along with the Ottoman is Ottoman Turkish. Ottoman Turkish has affected the Balkanic languages and Turkish, along with Arabic and Persian, which it harbors in its body, entered into the Balkanic languages. The poets of Classical Turkish Literature and Turkish Dervish Lodge Literature prepared a similar literary ground in the Balkanic communities. The Balkan poets and authors, who are affected with this literary ground, have used the words which they obtained from Ottoman Turkish in their works. Many factors such as Ottoman state system, educational institutions, the effects of dervish lodges deeply affected the languages of the Balkanic communities. If Turkish, Arabic and Persian words in the Balkanic languages are considered in this regard, it will not be appropriate to use expressions like Orientalist or ―Orientalism,‖ which means the words of Eastern languages, for these words from the historical-cultural basis. A part of the words, which entered into the Balkanic languages from Ottoman Turkish, are concepts from the Turkish state tradition. 453 of the Turkish words in Bosnian, Serbian and Croatian are related to state system, administration and law. 166 words in these languages are army and military terms (SkajliĤ, 1965: 25). One part of the words in the Turkish state system and the organization of army are Turkish, and yet another part of the words are of Arabic and Persian origin. In Bosnian, Serbian and Croatian, beside the Turkish words such as: Bajraktar (Flag-bearer), Beg (Gentleman), Beglerbeg (Governor-General), there are some concepts of Arabic origin on state system such as: Kadija (Judge), zabit(Officer), vilajet (Province). Due to some of the concepts of Turkish state system being Arabic-Persian origin, it is not enough to define these words as Orientalism. Along with the words belonging to Ottoman Turkish, Turkish adjuncts also entered into the Balkanic languages.162 The adjuncts, mentioned below, preserve their functionality in the Balkanic languages even today. These adjuncts, which we explained with examples, are of Turkish origin and are not used in Arabic and Persian. For this reason, evaluating these Turkish adjuncts within the concept of Orientalism will be an enforcement in terms of linguistics. The -cı, ci,-cu, cü, -çı,-çi,- çu,-çü adjuncts are in use in Albanian, Bosnian, Serbian, Croatian, Macedonian and Bulgarian. 162 Apart from the adjuncts, which entered into the Balkanic languages, from Ottoman Turkish, there is also –gar (kar) adjunct. This adjunct is used in all Balkanic languages. 475 �1st International Conference on Foreign Language Teaching and Applied Linguistics May 5-7 2011 Sarajevo Bosnian-Serbian-Croatian; KeĦedņija (Felt-maker), kundurdņija (Shoemaker), mejdanņija (Fieldsman) Albanian; Shakaxhi(Joker), inatçi (obstinate), batakçi(crook), sherrxhi (aggressive) Macedonian; ilecija (trickster), kavgacija (aggressive), abacija (wool-cloth maker). -lık,- lik, -luk, -lük; these adjuncts can be seen in all the Balkanic languages except Greek. Bosnian-Serbian-Croatian; bajramluk (festivity outfits), dunjaluk(worldly goods), Ħizmedņiluk(boots-making), Ħobanluk (occupation of a shepherd) Albanian; baballek (stepfather), beqarllek (bachelorhood), budalallek (stupidityk), pazarllek (bargain). Macedonian; pazarlak (bargain), rezilak(disgracefulness), samanlak (haymow), terzilak(tailorship) haremlak (wifehood), fukaralak( poverty). -lı, -li, -lu, -lü, these adjuncts can be seen in all the Balkanic languages except Greek. Bosnian-Serbian-Croatian;Sarajlija (Court-member-Sarajevo-resident), mekteplija (student), (additional) Albanian; nazeli (coy), sheherli (city-dweller), vesveseli (apprehensive), borxli (indebted) Macedonian;tatlija(dessert), sarajlija(court-member), Ģerbetlija(sweet and juicy). yedeklija -ça,- çe,-ca, ce ; these adjuncts can be seen in Albanian, Bosnian,Serbian and Croatian. Bosnian-Serbian-Croatian: Ġlidņa (hot spring) daidņa (maternal uncle) Albanian; hajdutçe (banditry), Turçe (Turkish), çobançe/çabanlık (occupation of shepherd) (SkajliĤ, 1965; Metaj, 2009; Kadiu,Adiu, 2009; Bayraktar, 2009; Oktay, 1999). The words, which entered into the Balkanic languages during the Ottoman period, could be used with prefixes and suffixes of that particular lanaguage. These words, which are used with the adjuncts of the Balkanic languages, can be discussed with examples from Albanian. For example; Pa-gejf(dejected, unhappy) pa-rehati (sick, uncomfortable), çoban-eri (shepherd), çam-ishte (Pine grove), bayrak-as (flag-bearer), oda-tar (office boy, janitor), çay-tore (tea-maker), azdi-sem (getting angry), bırakti-sem (leaving, giving up), çirak-i (apprentice), jeshil-im (green vegetation) etc. (Metaj, 2009). Starting from these examples, Turkish, Arabic and Persian words are seen to adjust to the phonetic and grammatical features of Albanian language. Therefore, these words, available in Albanian, do not belong to the East; they are adopted into Albanian. For this reason, evaluating these words in question with ―Orientalism‖ concepts will mean to disregard this adaptation and transition. The slogans, which entered into the Balkanic languages from Ottoman Turkish, carry the traces of Turkish folklore and social life in terms of function and meaning. The functionality of the following slogans, which are seen in Bosnian-Serbian-Croatian, are not different from their functionality in Turkish; akĢam hajrola/may your evening be good, Allah rahmetejle/ God rest his soul, baĢun sagosum/may the head of your family be alive, bajram mubareč ola/happy holidays, dostlar sagosum/may friends be alive, hoĢgeldum/welcome etc. (SkajliĤ, 1965; Ġyiyol, 2010b). The majority of the words, which entered into the Balkanic languages from Ottoman Turkish have been identified as Turkish as a result of the researches. Arabic and Persian words, which are seen in the Balkanic languages, in terms of phonetic and morphology,are seen to possess the phonetic and morphological features of Turkish. The following words, which are seen in Bosnian-Serbian-Croatian, can be shown as examples of this. Bosnian Bardak Çoban Turkish <Bardak < Çoban Persian < Bârdân <ġubân Patlijan Rakija <Patlıcan <Rakı, arak <Bâdingan Memur <Memur Sejmen <Sey(ğ)men Arabic < Araq <Ma‘mur <Segbân Source: SkajliĤ (1965); Vajzovic (1999). The meanings, scopes and connotations of the words, which entered into the Balkanic languages from and through Turkish, should be taken into consideration. With the term ―Eastern,‖ Western European and 476 �1st International Conference on Foreign Language Teaching and Applied Linguistics May 5-7 2011 Sarajevo American researchers essentially meant the Arabic, Persian and Turkish geographies; but in broader sense, it refers to a region from the Mediterranean to China. With ―Eastern,‖ Balkan linguists meant Turks, Arabs and Iranians; Turkish, Arabic, and Persian by implication. In this respect, there are differences in geography and languages, which Balkan researchers and Orientalists meant. Secondly, according to philogists and anthropologists, such as, Silvestre de Sacy and Ernest Renan, the basic language of Orientalism is Arabic (Said, 2006: 134). Despite the Arabic language being at the center of the Orientalist researches; the vast majority of the words in the Balkanic languages are Turkish. It is seenthat even non-Turkish words are used with Turkish adjuncts, they form compound words when they are combined with Turkish words and the slogans have the functionality in Turkish culture. However, using Orientalism concept for these words will reveal the idea that vast majority of the words, in the Balkanic languages, belonged to the East or these words passed through Arabic culture. If the vast majority of these words in the Balkanic languages were of Arabic origin, or if these words possessed the morphological features of Arabic language and passed on to the Balkanic languages though Arab communities, it would be possible to call these words ―Orientalism‖ or ―Arabism.‖ The Turkish words in the Balkanic languages are naturally adopted by the Balkan communities. Like Arabic and Persian words are naturalized in Turkish language, these words, too, became a part of the languages of the Balkan communities. Abdullah SkajliĤ describes this situation with such expressions, ―Turkism did not forcibly enter into our language and did not leave a negative effect on our language...‖ This natural interaction, which SkajliĤ states, brought thousands of words into the treasures of the Balkanic languages and cultural structures. Using ―Orientalism‖ concept for these words, whose existence in the Balkanic languages exceeds centuries, in a sense, would mean to ―Alienate‖ these words. It will be inconsistent to use ―Balkanizm‖ concept for these words in various aspects because these words pass on from one Balkan language to another. The following words,such as yorgan(quilts), yastık (pillow), döĢek (mattress), kebap (kebab), baĢka (another), tüfek (rifle), ocak (stove), sedir (cedar), yaka (collar), çakır (greyish blue) etc., which are used in every aspect of daily life, predominate a conviction that these words passed on from one Balkan language to another. Even though some of the Turkish words in the Balkanic languages are same words, the number of the words in these languages are enough to refute this concept. Since the number of Turkish words in the Balkanic languages are different, it is not possible to see all these words throughout all the Balkanic languages. Therefore, ―Balkanizm‖ concept does not have enough scope to designate these words. It is a case open for discussion whether it is necessary to use a concept for the words, which belong to Islamic communities and entered into the Balkanic languages during Ottoman period. For the Arabic-Persian words, which are present in Turkish and gradually became Turkicized, there was not any concepts used for them or was no need for it. Gathering the Turkish words in the Balkanic languages under a concept will alienate these words and due to political reasons, it will bring the idea of dismissing these words from the Balkanic languges along with it. During the Yugoslavia period, significant task have been done in the name of eliminating the cultural heritage of the Ottoman period.163 On the other hand, in order to conduct scientific studies on these words, to analyze the effect of these words and word groups on the cultures and languages of the Balkanic communities in various aspects, it is seen that there is a need for designation. Conclusion It is inconsistent in terms of linguistic, cultural and historical process to designate the words, which entered into the Balkanic languages during the Ottoman period and still continue to preserve their functionality today, with the concepts like ―Orientalism,‖ and ―Balkanizm.‖ There are differences between the meaning and scope of the word Orientalism in the studies of language and culture and the Orientalism concept used for the Turkish-Turkicised words in the Balkanic languages. It is seen that it would not be a right choice to designate the Turkish words in the Balkanic languages with ―Orientalism‖ whether in terms of language and culture, or the associations and scope of the word. Gathering the Turkish words in the Balkanic languages under a concept will bring the idea of alienating and dismissing these words in question from the languge along with it. However, it is necessary to gather these Turkish-Turkicized words in the Balkanic languages under a concept for scientific studies. In our view, ―Turkism‖ bears the feature of the most powerful concept meeting the need for designating these words in question. A vast majority of the words in the Balkanic languages being Turkish, passing of these words in the Ottoman period, the use of Turkish adjuncts with these words, these words-slogans in question having the functionality of Turkish cultural life, Arabic and Persian words in the Balkanic languages having Turkish grammatical features and like many factors support our view. 163 In 1946, the schools in Yugoslavia were closed, children‘s reception of Koran education in the mosque was banned, Muslim officers were warned not to have their children circumcised. (Malcolm, 2002: 195). Like the pressure on the religious beliefs of Muslims in Yugoslavia, the similar pressure was on the Turkish words in the language (Korkmaz, 2007: 253-256). 477 �1st International Conference on Foreign Language Teaching and Applied Linguistics May 5-7 2011 Sarajevo References Abazi-Egro, Genciana (2002), ―Arnavutluktaki Tùrkoloji ÇalıĢmaları‖, Bilig, issue 21, Bahar, pp.1-24. Ahmet, Oktay (1999), Makedonca Sözlükte Türk Etkileri, Unpublished Graduate Seminar Workı, Cyril and Methodius Univ. Faculty of Philogy. Aksan, Doğan (2006), Anlambilim, Engin Publication, IV. Edition, Ankara. Bayraktar, Fatma Sibel (2009), ―Arnavutçaya ve Diğer Balkan Dillerine Geçen Tùrkçe Kelimelerin KarĢılaĢtırılması‖, Turkish Studies, Vol. 4/4 summer. pp. 1083-1090. Castellen, Georges (1995), Balkanların Tarihi, 2. Edition, Milliyet Pub., Ġstanbul. Iyiyol, Fatih (2010a), BoĢnak Halk Kültüründe Türk Tekke-Tasavvuf Geleneğinin Ġzleri, Unpublished Doctoral Thesis, Sakarya University, Institute of Social Sciences. Iyiyol, Fatih (2010b), ―BoĢnak Folklorunda Tùrk Kalıp Sôzlerin Etkisi‖, International IX. Language Literature Phraseology Sympozium Proceedings (15-17 October), Sakarya University Publicationhouse, v. II, pp. 2-10. Kadiu Spartak, Xhemile Abdiu (2009), ―Tùrkçe Yapım Eklerinin Arnavutça Yapım Eklerine Etkisi‖, Turkish Studies, Vol. 4/3 Spring, pp. 1229-1241. Korkmaz, Hùseyin (2007), Osmanlının Batı Yakası Bosna, 3F Pub., Ġstanbul. Malcolm, Noel (2002), Bosnia a Short History, Pan Book, London. Metaj, Ilaz (2009), ―Oriantalizmat Shtrirja Leksiko-Semantike Ne Gjuhen Shqipe‖, Shtepia Botuese Drenusha, Prishtine. Nametak, Fehim (1989), Pregled Knjiţevnog Stvaranja Bosansko- Hecegovačkih Muslimana Na Turskom Jeziku, El-Kalem, Sarajevo. Nametak, Fehim (1997), Divanska Knjiţevnost Bošnjaka, Orijentalni Institut u Sarajevu, Sarajevo. Nurkic, Kemal (2007), Bosna-Hersek'te ĠslamlaĢma Süreci, Unpublished Graduate Thesis, Ondokuz Mayıs Univ, Institute of Social Sciences. Öztekten, Özkan (2004), ―Tùrkçenin Dùnya Dillerine Etkisine Genel Bir BakıĢ‖, Türkçenin Dünya Dillerine Etkisi, Prepared by: KARAAĞAÇ, Gùnay, issue. 11-37. Said, Edward W. (2006), Orientalizm,trans. Belma Ülner, Metis Pub., III. Edition, Ġstanbul. ŃkaljiĤ, Abdullah (1965), Turcizmi u SrpsoHrvatsom Jeziku, Svjetlost, Sarajevo. Todorova, Maria (2006), Balkanları Tahayyül Etmek, 2. Edition, ĠletiĢim Pub., Ġstanbul. VajzoviĤ, Hanka (1999), Orijentalizmi u Knijiţevnom Djelu-Lingvistička Analazia, Orientalni Istitut, Sarajevo. Yalçın, Emrullah (2009), ―Tùrk-Bulgar Ortak Kùltùrù‖, Ankara University Institute of the History of Turkish Reformation Atatürk‘s Path Magazine, Issue. 43, Spring, pp. 555-576 478 � 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. 71 Title A name given to the resource The Findings upon the Designation of Turkish Words among Balkan Languages Author Author İYİYOL, Fatih KESMECİ, Ahmet Musab Abstract A summary of the resource. The presence of the Turks in the geography of Balkans can be track back to the centuries before Ottomans. The actual impact of the Turkish language and culture began with the start of Ottoman conquests. With the Ottoman conquest, there have been great changes on the structure of the Balkan communities. As a result of this comprehensive and large impact, thousands of Turkish words entered into the Balkanic Languages. The quantity of Turkish words, their effect of the Balkanic Cultures and Languages have directed the researchers to search these words. The researchers who investigated the Turkish words in the Balkanic Languages faced a fundamental problem. The main problem that the researchers faced was the issue of designation of these words in question. Due to the appearance of the words of Arabic and Persian origin along with Turkish, some researchers have applied the term ―Orientalism‖ for these words in question. Since the vast majority of words in the Balkanic Languages are Turkish, the researchers, considering the fact that Arabic and Persian words entered into the Balkanic Languages through Turkish, have preferred the concept ―Turkism‖ for these words. Researchers, without making a detailed evaluation on either ―Orientalism‖ or ―Turkism,‖ have stated their more general preferences. However, the designation issue of these words, which have such effect on the Balkanic Languages and their numbers getting closer to ten thousand in some languages, requires a detailed analysis in order to formulate an opinion. The purpose of this study is to evaluate the words in question in terms of grammatical, cultural and historical process and to contribute to the issue of designating through Ottoman institutions and community life. Within the scope of this study, the emphasis is given, in addition to all the Balkanic Languages, on the Bosnian-Serbian-Croatian and Albanian Languages where Turkish words are dominantly present. Date A point or period of time associated with an event in the lifecycle of the resource 2011-05 Keywords Keywords. Conference or Workshop Item PeerReviewed P Philology. Linguistics https://omeka.ibu.edu.ba/files/original/5e86b761742fb858ab97d0ad6bb43399.pdf 1bbdea67d590e3f4c67cad8f55eb0cb0 PDF Text Text 3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo The Fishery Potential And Sustainable Aquaculture In Portugal Samet Kalkan1, Mehmet Ali Canyurt2 1Istanbul University, Faculty of Fisheries, Department of Basic Science Laleli, Istanbul, Turkey 2Ege University, Faculty of Fisheries, Department of Aquaculture 35100- Bornova, Izmir, Turkey Abstract In this research, the history of fishery sector and the current situation of fishery sector in Portugal have been investigated, capture and development and potential of aquaculture sector have been studied. Portugal is located in southwestern Europe and it is on the Iberian Peninsula. Portugal has an important place with its total fishery production in Europe. In 1964, total fishery production which was 601.929 tonnes fell down to 207.058 tonnes in 2009. The main reasons of this decrease in total production are sustainable production that cannot be maintainet consistently, misuse of resources and difficult duration of adaptation and adjustments to European Union Regulations. Nearly 97% of total fishery production is from catching, whereas 3% is from farming. In this case it is clear that capture production is more developed than aquaculture production. In 2009, capture production was 200.365 tonnes and the most captured species are sardine, chub mackerel, Atlantic redfishes nei, Atlantic horse mackerel. Fish farming in Portugal, which started with rainbow trout production in 1965, has developed rapidly by gilthead seabream and european seabass production and reached to 6.693 tonnes per year according to 2009 data. According to 2008 data Portugal has 1392 fish farms and they covered 1587 hectares. The main farmed species are grooved carpet shell, gilthead seabream, turbot, pacific cupped oyster, european seabass and rainbow trout. Import and export amounts of Portugal on fisferies are very high compared to Turkey. Portugal has great potential about capture and especially aquaculture production. Thus Portugal has to improve its aquaculture sector within sustainable productions and there must be proper management by fish farms and governement to spread sustainability all over sector. In the future aquaculture of Portugal will start to increase rapidly. Therefore Turkey has to improve the relations with Portugal and they should collaborate closely. Keywords: Portugal, Fishery, Aquaculture, Sustainability, Development 1.INTRODUCTION Portugal is a country situated in southwestern Europe on the Iberian Peninsula. Portugal is bordered by the Atlantic Ocean to the West and South and by Spain to the North and East. The Atlantic archipelagos of the Azores and Madeira are part of Portugal. Portugal is defined with Mediterranean climate (Csa in the south, interior, and Douro region; Csb in the north, centre and coastal Alentejo; and also Semi-arid climate or Steppe climate, and is one of the warmest European countries: According to Agencia Estatal de Meteorología-AEMET data the annual average temperature in mainland Portugal varies from 12 °C (53.6 °F) in the 83 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo mountainous interior north to over 18 °C (64.4 °F) in the South. The sea surface temperature on the west coast of mainland Portugal varies from 13 °C (55.4 °F)-15 °C (59.0 °F) in winter to 18 °C (64.4 °F)-20 °C (68.0 °F) in the summer while on the south coast it ranges from 15 °C (59.0 °F) in Winter and rises in the summer to about 23 °C (73.4 °F) occasionally reaching 26 °C (78.8 °F). 2.HISTORICAL DEVELOPMENT AND PRESENT SITUATION OF PORTUGAL FISHERIES SECTOR Portugal with regard to its geographic location, climate and other characteristics is quite suitable for fisheries and aquaculture activities. The total fish production was around 300.000 tonnes before 1950. With controlled fishing since 1950, fishery production reached a value as high as 601.929 tonnes in 1961 and, with the ups and downs in production period until the 1980’s, the total production in 1982 declined to 259.938 tonnes, rose again to 413.184 tonnes until 1986 with increasing momentum. During the 2000's showed that the decline in production compared to previous years (FAO, 2009) . Portugal shows a complex structure in total fishery production. Possible reasons of decline in production are the wrong government policies, the unbalanced use of resources, unsustainability of establishments, lack of qualified labor force, difficulty of obtaining permission from the governmental institutions, the problems in the EU harmonization process and some economic problems of the country. In addition to those problems the manufacturers are also not supported financially. Nearly 97% of total fishery production is from catching, whereas 3% is from farming. It is clear that capture production is more developed than aquaculture production. In 2009, the total aquatic production in Europe was 15.871.701 tonnes. Portugal is ranked 13th among the European countries in terms of production quantity ranks. While the total production was 601.929 tonnes in Portugal in 1964, Turkey's total production was 121.150 tonnes. Increase in production in the 1980s, continued to raise and in 2009 the production reached 622.962 tonnes in Turkey, while the production in Portugal reached a value of 207.058 tonnes (FAO, 2009). Considering the number of personnel in the sector, according to the years, remained almost the same as the aquaculture sector workers. Number of employees in the fishing and processing sectors were greatly decreased. As the data indicates this decrease has been parallel with the decline in production. Portugal is ranked first in Europe in respect to annual per capita fish consumption. In 2005, annual fish consumption per capita was 55,6 kg, while the consumption per person was 7,0 kg in Turkey (European Commission, 2010). 3.FISHERY SECTOR Portugal, with a 940 km coastline, and a 1.727.408 km2 area is the 3rd largest country in the EU and 11th in the World with respect to an exclusive economic zone (European Commission, 2010). The Fisheries sector in Portugal constitutes a large portion of total production is highly developed. The amount of fishing in Portugal was 200.365 tonnes in 2009. With this amount of production Portugal is ranked 12th among the European countries (excluding Turkey). 84 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Fishing vessels registered in the country according to 2008 data are 8666 pieces. These fishing vessels have 380.730 kW power and 106.624 grostons (FAO, 2009). In 2009, capture production is 200.365 tonnes and the most captured species are sardine (Sardina pilchardus-60.927 tonnes), chub mackerel (Scomber japonicus-14.961 tonnes), Atlantic redfishes nei (Sebastes spp.-10.452 tonnes) and Atlantic horse mackerel (Trachurus trachurus-11.841 tonnes) (FAO, 2009). 4.AQUACULTURE In Portugal, the first fish breeding efforts began in 1965 with rainbow trout. In the 1980s, the farming of trout and even the shellfish has continued to be growing. In the first half of 1990, attention is given to marine fish farming, and marine fish production increased and widely used since that date (Dinis, 1999). The amount of aquaculture production in Turkey has increased rapidly during last 20 years with respect to Portugal. In the last 20 years it has increased 8-9 times. By 2009 annual production reached 165.455 tonnes. However, in Portugal, production was observed to be in a decrease. By the year 2009, 6.693 tonnes of aquaculture production did not meet the production expectations (FAO, 2009). There are 1392 licensed fish farms in the country. Fish farms were located along the coastline of the country in general, covering 1587 hectares. 1226 licenced farms are extensive fish farms in the country, 97 are semi-intensive, while 69 of them operate as intensive. 28% of them has a production capacity of 100-500 tonnes. Aquaculture in the lagoons is very important because they cover a large area in the country. 85% of total aquaculture production comes from the lagoons, 7% in from cages and 8% from tanks ( Salz, 2006). In order to establish a fishery company in Portugal a permission from the Directorate General of Fisheries-Aquaculture and Hunting (GDPA) should be obtained. As a result of preexamination conducted after obtaining permission from GDPA, the Fisheries Research Institute (IPIMAR) should be consulted. Deemed appropriate by the regulatory approvals required for feasibility studies and, respectively, the local Port Management, Veterinary Directorate (DGV), Nature Reserves Conservation Institute (ICN), Environment Directorate (DRA), the Regional Public Health Administration (ARS), and finally the Local Council are to be consulted. After all these transactions, and the application is deemed appropriate in the circumstances, businesses are given approval to start operating (Vaz, 2008). Fish farming of Portugal, which started with rainbow trout production in 1965, has developed rapidly by gilthead seabream and european seabass production and reached to 6.693 tonnes per year according to 2009 data. The main farmed species are grooved carpet Shell (Ruditapes decussatus- 2.340 tonnes), gilthead seabream (Sparus aurata-1.345 tonnes), turbot (Sparus aurata-1.345 tonnes), pacific cupped oyster (Crassostrea gigas-461 tonnes), european seabass (Dicentrarchus labrax-420 tonnes) and rainbow trout Oncorhynchus mykiss-246 tonnes) (FAO, 2009). Examining the data of the last 10 years aquaculture farming fluctuating sea bream and sea bass were monitored. In the southern regions of Portugal, the temperature is not very suitable for trout farming and production, for this reason trout culture takes place in northern region at the suitable temperatures. Very intensive production is in question, although not observed gradual decrease of production due to various reasons. Turbot hatcheries have shown a rapid increase in recent years. 85 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo 5.IMPORT AND EXPORT OF FISHERY PRODUCTS The total value of import and export of fishery products in Portugal is €1.755.363.000 in 2008. Export amount is 131.531 tonnes and value is € 484.760.000. Import amount is 376.293 tonnes and value is €1.273.613.000 in same year. The total value of import and export of fishery products in Turkey is €415.329.000. Export amount is 60.054 tonnes and value is €288.713.000. Import amount is 120.242 tonnes and value is €126.616.000 (European Commission, 2010). 6.CONCLUSIONS AND RECOMMENDATIONS Portugal has a great potential about capture and especially aquaculture production. Fishery production in Portugal has an important position in the European Union. Portugal aquaculture with the support of EU in the coming years is expected to rise rapidly. Thus, Portugal has to improve its aquaculture sector within sustainable productions and there must be proper management by fish farms and governement to spread sustainability all over the sector. In the future, aquaculture of Portugal will start to increase rapidly. Therefore, Turkey has to improve the relations with Portugal and they should be in close collaboration. REFERENCES Agencia Estatal de Meteorología-AEMET. Dinis, M.T., Moreno, C., Noronha, I. (1999) Estudo de Caracterização e Diagnóstico do Subsector da Aquicultura, 48p. European Commission, (2010) Fisheries and Aquaculture in Europe, 12p. European Commission, 2010, Facts and figures on the CFP (Basic Data on the Common Fisheries Policy), Luxembourg, 44p. FAO Fisheries and Aquaculture Department, (2010) The State of World Fisheries and Aquaculture, Rome, 196p. Salz, P., Buisman, E., Smit, J., Vos, B., (2006) Employment in the fisheries sector: current situation, 185p. Vaz, T. (2008) CIHEAM Country Profile: Portugal: Agriculture, Fishery, Food and Sustainable Rural Development, University of Algarve, 25p. 86 � 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. 1230 Title A name given to the resource The Fishery Potential And Sustainable Aquaculture In Portugal Author Author Samet , Kalkan Abstract A summary of the resource. In this research, the history of fishery sector and the current situation of fishery sector in Portugal have been investigated, capture and development and potential of aquaculture sector have been studied. Portugal is located in southwestern Europe and it is on the Iberian Peninsula. Portugal has an important place with its total fishery production in Europe. In 1964, total fishery production which was 601.929 tonnes fell down to 207.058 tonnes in 2009. The main reasons of this decrease in total production are sustainable production that cannot be maintainet consistently, misuse of resources and difficult duration of adaptation and adjustments to European Union Regulations. Nearly 97% of total fishery production is from catching, whereas 3% is from farming. In this case it is clear that capture production is more developed than aquaculture production. In 2009, capture production was 200.365 tonnes and the most captured species are sardine, chub mackerel, Atlantic redfishes nei, Atlantic horse mackerel. Fish farming in Portugal, which started with rainbow trout production in 1965, has developed rapidly by gilthead seabream and european seabass production and reached to 6.693 tonnes per year according to 2009 data. According to 2008 data Portugal has 1392 fish farms and they covered 1587 hectares. The main farmed species are grooved carpet shell, gilthead seabream, turbot, pacific cupped oyster, european seabass and rainbow trout. Import and export amounts of Portugal on fisferies are very high compared to Turkey. Portugal has great potential about capture and especially aquaculture production. Thus Portugal has to improve its aquaculture sector within sustainable productions and there must be proper management by fish farms and governement to spread sustainability all over sector. In the future aquaculture of Portugal will start to increase rapidly. Therefore Turkey has to improve the relations with Portugal and they should collaborate closely. Keywords: Portugal, Fishery, Aquaculture, Sustainability, Development 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/591eb07ccdafcc335c1211721c85ab6f.pdf 0014e308278e48d841e6c6eb1e6d8afa PDF Text Text 2nd International Symposium on Sustainable Development, June 8-9 2010, Sarajevo The Forward and Backward Linkage Effects of the Energy Sector in Turkey Mehmet MERCAN mmercan48@hotmail.com Abdullah ÖZDEMIR abdullahozdemir@hotmail.com Abstract: Energy sector has a great importance for producers and consumers. Energy sector has been found as a leading sector as a result at the input-output analysis. This analysis has been done by using input-output tables which are constructed by goverment Statistical Institude. Turkey is dependent to other countries as energy. To satisfy the development in Turkish economy is only available by reducing the depandencies to the other countries by the energy. Also it should be continued as the leading sector. Key Words: Input-Output Analysis, Energy Sector. Introduction Energy constitutes the most important causes of wealth through a variety of manifestations. Besides water, coal. Petroleum and other valuable resources, the existence of wind and sun are sources of wealth as well. Energy resources are used as inputs by other sectors in manufacturing industries. Therefore, it is important to know the forward and backward linkages of this sector for the general situation of the economy. The importance of energy resources has doubled in Turkey because of the energy crises experienced in recent years. In this context, this study aims to find and interpret the forward and backward linkage effects of the energy sector. The purpose in this study is to observe the direct and indirect effects of input exchange between sectors and their change over the years. The main data set used in the study is the Input-Output Flow Tables about Turkish economy that is prepared by the Turkish Statistical Association (TUIK). The data for 1996, 1998 and 2002 are used in the study. The Importance of the Energy Sector An increase in energy prices also increases the costs of inputs and product prices. Energy prices that are not fixed influence inflation and increase the pressure for economic stagnation through affecting total demand. The more important the use of energy resources in an economy the higher the inflationist pressure against the increases in oil prices (LeBlanc and Chinn, 2004: 8). Increases in the prices of energy resources raise the costs of airways, transportation and the costs of the companies that produce chemical products and therefore, lead to inflation. For this reason, any change in energy prices is watched very closely (Bennet, 2003: 1). Plants in the energy sector should be planned long before the demand for energy exists. Otherwise, delays in planning and investments raise the cost of energy and adversely affect economic activities and societal wealth. It is imperative to determine the potential needs in the energy sector at least ten years in advance, decide the projects to cover the increasing demand, and make necessary political decisions (Gerek, 1998: 370-371). In developing countries like Turkey, the sectors that produce energy have important structural ties with other sectors. Especially electricity sector in Turkey positively affect economic growth because of its backward linkage. In today‘s modern societies, electrical energy used increasingly ignite other sectors of the economy by providing considerable amounts of inputs. The insufficiency of electric energy supply that should increase parallel to economic growth adversely affects economic growth as well as prevents the stimulatory effect on the economy (Terzi, 1998: 63). Input-Output Analysis 685 �2nd International Symposium on Sustainable Development, June 8-9 2010, Sarajevo The input-output model is a model that considers the relationship between the level of activities in the all sectors of economy (Akkaya and Pazarlioglu, 2000: 14). The input-output models are simple mathematical equilibrium models that quantitatively analysis the mutual linkages between production and consumption units on the whole economy scale in a multi-sectoral way. Different from micro economical analysis that focuses on the behaviors of firms and households and macroeconomic analysis that analyzes the whole economy, the input-output analysis‘ focus is on sectors and good exchanges between sectors. The input-output models provide an opportunity to quantitatively analyze the production and use of outputs of productive sectors on whole economical and sectoral basis and fulfill and important gap between partial and total analyses especially in the analysis of empirical problems (Aydogus, 1999: 1-2). In the input-output model, under the assumption that the share of technology or inputs in production costs is constant, the equilibrium prices of goods and services produced in every sector can be obtained as the prices of main inputs (Aydogus, 1993: 36). According to Hirschman, the effects of forward and backward linkages that reflect sectors‘ ―feeding‖ and ―stimulating‖ powers on other sectors must be considered (Hirschman, 1958: 9). In Hirschman‘s unbalanced growth model, one of the most important factors that restricts economic growth is the ability of decision making, especially the ability to take an investment decision. Inferring from Hirschman‘s ideas, a quadruple grouping can be developed. The categories of this grouping that considers forward and backward linkages together can be summarized as follows: Category 1: Sectors that have high forward and backward linkage effects. Category 2: Sectors that have high backward but low forward linkage effects. Category 3: Sectors that have high forward but low backward linkage effects. Category 4: Sectors that have low backward and forward linkage effects. The above arrangement shows sectoral investment priorities from the lowest to the highest. According to this, the sectors in the first category constitute the key sectors in the economy and have the highest investment priority. The scarce resources should primarily be devoted to these sectors. If there are still unused resources, then, they should be devoted to the sectors in the second category. Sectors in the III. and IV. categories come last in terms of investment priorities, that is, these sectors are expected to be stimulated by the key sectors (Aydogus, 1999: 100101). The Forward and Backward Linkage Effects for 1996, 1998, and 2002 The 1996 and 1998 input-output tables prepared by TUIK consist of total 97 sectors and the 2002 table consists of 59 sectors. The forward and backward linkage effects are as follows in terms of sectoral arrangement. The Table consists of 97 sectors but to observe it more clearly it is divided. In Table 1, there are Forward Linkage Effects (FLE) and Backward Linkage Effects (BLE) of 24 sectors. 686 �Sectors 1-Growing of cereals and other crops n.e.c. 2-Growing of vege- tables, horticultural specialities and nursery products 3-Growing of fruit, nuts, beverage and spice crops 4- Farming of animals 5-Agricultural and animal husbandry service activities (excl. veterinary act.) 6-Forestry, logging and related service activities 7- Fishing 8- Mining of coal and lignite 9-Extraction of crude petroleum and natural gas 10- Mining of metal ores 11- Quarrying of stone, sand and clay 12- Mining and Quarrying n.e.c. 13- Production, proces- sing and preserving of meat and meat products 14-Processing and preserving of fish and fish products 15- Processing and preserving of fruit and vegetables 16- Manufacture of vegetable and animal oils and fats 17-Manufacture of dairy products 18-Manufacture of grain mill produtcs, starches and starch products 19-Manufacture of preparad animal feeds 20-Manufacture of bakery products 1996 ĠBE GBE 5,89 1,66 1998 ĠBE GBE 5,07 1,42 1,21 1,54 2,53 1,50 1,20 1,93 1,23 1,89 2,00 1,31 1,14 1,74 1,92 1,87 1,16 1,65 4,31 1,26 1,33 1,23 2,13 1,22 1,41 1,33 1,23 1,61 1,40 1,31 1,42 1,72 1,15 1,50 1,32 1,16 1,38 1,24 1,78 1,17 1,26 1,44 1,25 1,45 1,27 1,23 1,72 2,49 1,55 2,07 1,11 1,13 1,54 1,10 1,86 1,96 2,33 2,19 1,02 1,37 1,53 1,21 1,74 1,70 2,06 1,87 1,57 1,33 1,02 2,14 2,32 2,16 1,55 1,27 1,03 1,81 2,04 2,03 Sectors Agriculture, hunting and related service activities Forestry, logging and related service activities Fishing, operating of fish hatcheries and fish farms; service activities incidental to fishing Mining of coal and lignite; extraction of peat Extraction of crude petroleum and natural gas; service activities incidental to oil and gas extraction excluding surveying Mining of uranium and thorium ores Mining of metal ores Other mining and quarrying Manufacture of food products and beverages Manufacture of tobacco products Manufacture of textiles Manufacture of wearing apparel; dressing and dyeing of fur Tanning and dressing of leather; manufacture of luggage, handbags, saddlery, harness and footwear Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials Manufacture of pulp, paper and paper products Publishing, printing and reproduction of recorded media Manufacture of coke, refined petroleum products and nuclear fuels Manufacture of chemicals and chemical products Manufacture of rubber and plastic products Manufacture of other non-metallic mineral products Table 1: 1996 ,1998 ve 2002 Years Total Backward and Forward Linkage Effect of First Twenty Sector (Direct+Indirect) (Tables was calculated by using the Input-Output Table 1996, 1998 ve 2002 Years) IBE :Forward linkage effect GBE:Backward linkage effect 2002 ĠBE GBE 1,86 3,66 1,35 1,64 1,60 1,45 1,06 1,37 1,05 1,00 1,92 2,14 2,95 2,79 2,98 3,21 3,24 1,00 1,16 1,82 2,52 1,14 3,81 1,32 2,94 1,69 2,88 2,59 2,65 2,30 1,68 3,99 1,65 2,81 2,06 2,69 2,66 5,82 2,63 2,39 �2nd International Symposium on Sustainable Development, June 8-9 2010, Sarajevo 21- Manufacture of sugar 22-Manufacture of cocoa, chocolate, sugar confertionery and other food products n.e.c. 23-Manufacture of alcoholic beverages 24- Manufacture of soft drinks; production of mineral waters 25- Manufacture of tobacco products 26-Manufacture of textiles 27- Manufacture of other textiles 28-Manufacture of knitted and fabrics and articles 29- Manufacture of wearing apperel, except fur apparel 30-Dressing and dyeing of fur; manufacture of articles of fur 31- Tanning and dressing of leather; manufac.of luggage, handbags & harness 32-Manufacture of footwear 33-Sawmilling and planing of wood 34- Manufacture of wood and of products of wood and cork 1,37 2,18 1,33 1,94 1,43 1,24 1,27 1,08 2,96 1,26 1,13 1,13 1,39 2,02 1,56 2,24 2,00 2,45 2,13 2,49 2,37 2,45 1,34 1,16 1,08 1,08 2,67 1,22 1,07 1,53 1,01 1,88 1,51 2,10 1,83 1,76 1,68 1,76 1,88 1,86 1,91 1,14 2,19 1,43 2,46 2,55 2,42 2,17 1,69 1,07 2,06 1,45 1,96 2,00 2,08 1,98 35- Manufacture of paper and paper products 36-Publishing 3,41 1,09 2,10 1,86 2,39 1,09 1,69 1,53 37- Printing and service activities related to printing 38- Manufacture of coke, refined petroleum produtcs 39- Manufacture of basic chemicals, plastics in primary & synthetics rubber 40- Manufacture of fertilizers and nitrogen compounds 1,51 5,79 2,10 1,55 1,52 3,92 1,62 1,13 4,89 1,77 2,16 2,16 1,79 1,30 1,58 1,66 Manufacture of basic metals Manufacture of fabricated metal products, except machinery and equipment Manufacture of machinery and equipment n.e.c. Manufacture of office machinery and computers Manufacture of electrical machinery and apparatus n.e.c. Manufacture of radio, television and communication equipment and apparatus Manufacture of medical, precision and optical instruments, watches and clocks Manufacture of motor vehicles, trailers and semi-trailers Manufacture of other transport equipment Manufacture of furniture; manufacturing n.e.c. Recycling Electricity, gas, steam and hot water supply Collection, purification and distribution of water Construction Sale, maintenance and repair of motor vehicles and motorcycles; retail sale services of automotive fuel Wholesale trade and commission trade, except of motor vehicles and motorcycles Retail trade, except of motor vehicles and motorcycles; repair of personal and household goods Hotels and restaurants Land transport; transport via pipelines Water transport Table 2: 1996 ,1998 ve 2002 Years Total Backward and Forward Linkage Effect of Second Twenty Sector (Direct+Indirect) (Tables was calculated by using the Input-Output Table 1996, 1998 ve 2002 Years) 688 2,35 5,74 2,60 1,94 1,17 2,40 2,21 1,57 2,52 1,73 2,85 2,17 2,27 1,21 1,96 1,98 1,20 1,92 1,37 1,26 3,25 2,98 1,55 2,56 1,02 4,98 1,38 1,54 2,24 2,13 2,62 4,59 1,86 2,53 3,14 1,57 2,10 1,80 4,76 1,89 �2nd International Symposium on Sustainable Development, June 8-9 2010, Sarajevo Sectors 41-Manufacture of pesticides, other agro-chemicals and paints, varnishes 42-Manufacture of pharmaceuticals, medicinal chemicals & botanical products 43-Manufacture of cleaning materials, cosmatics and other chemicals & fibres 44- Manufacture of rubber products 45- Manufacture of plastic products 46-Manufacture of glass and glass products 47-Manufacture of ceramic products 48-Manufacture of cement, lime and plaster related articles these items 49- Cutting and finishing of stone and man. of other non-metallic mineral products n.e.c. 50-Manufacture of basic iron and steel 51-Manufacture of basic precious and non- ferrous metals 52- Casting of metals 53-Manufacture of fabricated metal products, tanks, reser.&steam gen. 54- Manufacture of other fabricated metal products; metal working services 55-Manufacture of general purpose machinery 56- Manufacture of special purpose machinery 57- Manufacture of domestic appliances n.e.c. 58-Manufacture of office, accounting and computing machinery 59-Manufacture of electrical machinery and apparatus n.e.c. 60-Manufacture of radio, television and communication equip- ment & apparatus 1996 ĠBE GBE 1,49 2,01 1998 ĠBE GBE 1,32 1,59 1,68 1,84 1,27 1,54 2,19 1,47 1,64 1,32 1,08 2,06 2,09 2,31 1,83 1,79 1,67 1,39 1,56 1,28 1,10 1,72 1,72 1,69 1,61 1,58 1,24 1,83 1,44 1,60 1,06 4,61 3,05 1,13 1,16 1,54 2,26 2,13 2,18 2,24 1,04 3,28 1,80 1,28 1,50 1,68 1,81 1,74 1,67 1,69 2,40 1,51 2,85 1,10 1,59 1,73 2,10 2,01 2,02 2,04 1,61 2,15 1,81 1,15 1,37 1,06 1,05 1,26 1,73 1,68 1,76 1,57 1,49 1,66 1,82 1,81 1,20 1,44 Sectors Air transport Supporting and auxiliary transport activities; activities of travel agencies Post and telecommunications Financial intermediation, except insurance and pension funding Insurance and pension funding, except compulsory social security Activities auxiliary to financial intermediation Real estate activities Renting of machinery and equipment without operator and of personal and household goods Computer and related activities Research and development Other business activities Public administration and defence; compulsory social security Education Health and social work Sewage and refuse disposal, sanitation and similar activities Activities of membership organisation n.e.c. Recreational, cultural and sporting activities Other service activities Private households with employed persons Table 3: 1996 ,1998 ve 2002 Years Total Backward and Forward Linkage Effect of Third Twenty Sector (Direct+Indirect) (Tables was calculated by using the Input-Output Table 1996, 1998 ve 2002 Years) 689 2002 ĠBE GBE 2,68 1,36 2,41 3,38 2,20 1,91 1,64 2,54 1,59 2,17 4,47 1,26 1,38 2,33 2,20 1,18 1,95 2,90 1,98 2,06 1,60 1,27 1,33 4,64 1,02 1,14 2,32 2,37 2,23 2,14 2,23 1,00 1,12 1,43 1,46 1,70 1,11 1,00 �Sectors 61-Manufacture of medical, precision &optical instruments, watches and clocks 62- Manufacture of motor vehicles, trailers and semi-trailers 63- Building and repairing of ships, pleasure &sporting boats 64-Manufacture of railway and &tramvay lokomo- tives & rolling stock 65-Manufacture of aircraft and spacecraft 66-Manufacture of transport equipment n.e.c. 67-Manufacture of furniture 68- Manufacturing n.e.c. 69-Production, collection and distribution of electricity 70-Manufacture of gas; distribution of gaseous fuels 71-Collection, purification and distribution of water 72-Construction 73-Sale, maintenance and repair of motor vehicles, motorcycles; retail sale of fuel 74-Wholesale trade and commission trade, except of motor vehicles & motorcyles 75-Retail trade, repair of personal and household materials 76-Hotels; camping sites and other provision of short-stay accommodatin 77-Restaurants, bars and canteens 78-Transport via railways 79-Land transport; transport via pipelines 80-Water transport 81- Air transport 82-Supporting and auxiliary transport activities; activities of travel agencies 83-Post and telecom- nications 84-Financial intermedediation, except insurance and pension funding 85- Insurance 86-Real estate activities 87-Renting of machinery and equipment without operator & of personal and household goods 88-Computer and related activities 89- Research and development 90- Other business activities 91-Education 92-Health and social work services 93-Activities of membership organizations n.e.c 94- Recreational, cultural and and sporting activities 95-Other service activities 96- Public services 97-Ownership of dwelling 1996 ĠBE 1,29 GBE 1,82 1998 ĠBE 1,04 GBE 1,56 1,60 1,10 1,30 1,18 1,39 1,07 1,21 4,38 1,16 1,53 1,06 2,40 2,15 1,48 1,93 1,16 2,25 2,24 1,96 1,45 1,77 1,25 2,02 1,45 1,21 1,01 1,02 1,02 1,08 1,08 1,05 3,69 1,18 1,38 1,16 2,17 1,72 1,54 1,48 1,23 1,66 2,01 1,27 1,35 1,18 1,19 1,67 1,29 5,87 1,39 3,54 1,26 2,65 1,61 1,40 1,24 6,05 2,12 1,17 1,15 2,19 5,34 1,34 1,51 1,08 1,46 1,81 1,91 2,20 1,54 1,80 1,97 2,28 1,38 1,48 1,74 1,51 1,60 2,97 1,23 1,80 1,07 5,11 1,70 1,20 1,03 2,22 5,23 1,20 1,55 1,18 1,25 1,69 1,70 1,58 1,35 1,48 1,55 1,86 1,15 1,43 1,44 1,52 1,62 1,11 1,28 3,42 1,02 1,04 1,00 1,38 1,21 1,00 1,00 1,95 1,14 1,75 1,74 1,59 1,47 1,53 1,63 1,00 1,31 1,13 1,30 2,92 1,05 1,03 1,04 1,47 1,10 1,00 1,00 1,53 1,61 1,48 1,53 1,29 1,48 1,48 1,46 1,00 1,25 Table 4:1996 ,1998 ve 2002 Years Total Backward and Forward Linkage Effect of Third Twenty Sector (Direct+Indirect) (Tables was calculated by using the Input-Output Table 1996, 1998 Years) . If the total increase in production caused by the increase in demand by one unit in a sector can be defined as that sector‘s backward linkage effect and the increase in a certain sector‘s production by one unit increase in last demand can be defined as that sector‘s forward linkage effect. In this context, the study includes calculations of both forward and backward linkage effects for 1996, 1998 and 2002. �2nd International Symposium on Sustainable Development, June 8-9 2010, Sarajevo When the tables 1,2,3, and 4 above are analyzed, it is seen that sectors with high forward linkage effects have an important place for creating supply to other sectors. Below are the sectors with high forward linkage effects. As can be seen in Tables 1,2,3, and 4 for the year 1996, the sectors with the highest forward linkage effects are the 79th sector highway transportation (6,05), 1 st sector grain and vegetable plantation (5,89), 74 th sector wholesale and trade brokering (5,87), 38 th sector coke furnace and refined petroleum product manufacturing (5,78), 84th sector intermediary financial institutions (5,34), 39 th sector main chemical materials, synthetic rubber and plastic raw material production (4,88), 50th sector iron-steel industry (4,61), 69th sector production and distribution of electricity (4,38), 9th sector crude oil and natural gas production (4,31), 35 th sector paper and paper product production (3,40), 51st sector main metal industry other than iron and steel (3,04), and 26th sector textile threads and weaving (2,96). As can be seen the other sectors of the economy used the most input from highway transportation and agricultural sector. The energy sub-sectors such as refined petroleum products and electricity production and distribution are among the first five sectors in terms of providing inputs to other sectors. As can be observed in Tables 1,2,3, and 4, the highest forward linkage effect sectors for 1998 total (direct and indirect) are; 84th sector intermediary financial institutions and auxiliary activities (5,22), 79 th sector highway transportation (5,11), 1st sector grain and vegetable plantation (5,07), 38 th sector coke furnace and refined petroleum product production (3,92), 69th sector electricity production and distribution (3,69), 74 th sector wholesale and brokering (3,53), 50th sector iron and steel industry (3,27), 75 th sector retail, and the repair of personal and home equipment (2,96), 26th sector textile thread and weaving (2,67), and 35th sector paper and paper product manufacturing (2,39). The sectors with highest forward linkage effects for 2002, as can be seen in Tables 1,2,3, and 4, are; clothing manufacturing (3,21), electricity, gas, steam and hot water production and distribution (2,97), textile manufacturing (2,97), food and drink manufacturing (2,95), leather tanning and processing; suitcase, handbag, saddler, harness and shoe manufacturing ((2,93), research and development services (2,90), wood and cork products manufacturing (2,88), furniture production (2,84), tobacco products manufacturing (2,79), plastic and rubber production (2,69), airway transportation (2,68). When direct forward linkage effects for 1996 are analyzed, highway transportation, grain and other plants plantation, wholesale and trade brokering coke furnace, refined petroleum products and intermediary financial institutions are the five sectors with highest sector linkage effects. The highest forward linkage effects for 1998 includes the first five sectors including the production of grain and other plants, highway transportation, intermediary financial institutions, wholesale trade and trade brokering, electricity production and distribution. The two sub-sectors of the energy sector are among the first five sectors with the highest forward linkage effects in 1996 and 1998. In 2002, food and drink production, clothing manufacturing, fur processing and dying, wood and cork products production, electricity, gas, steam and hot water production and distribution and research and development services are the first five sectors with highest direct forward linkage effects. The sectors with the highest forward linkage effects are important for reducing dependency on foreigners since they can be used as inputs in other sectors. In terms of their use as inputs in 1996 and 1998 highway transportation, agriculture and electricity production and distribution, petroleum refinery, iron and steel industry, textile, paper products manufacturing, wholesale trade and trade brokering sectors are remarkable. In 2002, clothing manufacturing, electricity production and distribution, petroleum, textile, leather tanning and processing, shoe manufacturing, tobacco, research and development services, wood and cork products manufacturing, plastic and paper products production, and airway transportation rather than highway transportation came forward. When we look at the years 1996, 1998, and 2002 together, the sub-sectors of the energy sector such as petroleum refinery, electricity production and distribution,, cruse oil production, coal and nuclear energy production, and natural gas production are among the first sectors that provide inputs to other sectors. The sectors with the highest backward linkage effects are the sectors which have influence for stimulating the level of production in other sectors. That is, since these sectors demand inputs from other sectors, they stimulate the economy. When the backward linkage effects for 1996, 1998, and 2002 are analyzed, the high linkage effects of the sub-sectors of the manufacturing industry stand out. It is well known that the manufacturing industry is very important in stimulating the level of production in other sectors in developing countries. When 1996 backward linkage effects are examined, meat processing and keeping, clothing, leather tanningsuitcase, handbag production, textile threads-weaving sectors are the first five sectors. 691 �2nd International Symposium on Sustainable Development, June 8-9 2010, Sarajevo In terms of backward linkage effects for 1998, metal industry, chemical materials production, wholesale trade and trade brokering, the activities of financial institutions and highway and pipeline transportation are the first five sectors. The sectors with the highest backward linkage effects in 1996 are shoe manufacturing, meat processing and keeping, clothing manufacturing, leather tanning-suitcase, handbag manufacturing, and textile thread and weaving and finishing. The sectors with the highest backward linkage effects in 1998 are non-alcoholic beverage and spring water production, timber and hardwood industry, meat processing and keeping, vegetative-bestial oil, animal food production. As seen, the sectors that are the sub-sectors of the manufacturing industry are the sectors with the highest backward linkage effects in 1996 and 1998. The five sectors with the highest backward linkage effects in 2002 (direct and indirect) are chemical material production, main metal industry, electricity, gas, steam and hot water production and distribution, highway and pipeline transportation, and wholesale trading and trade brokering. The sectors with high backward and forward linkage effects are described as the locomotives of an economy. When the similar studies are reviewed, it was concluded that the manufacturing industry in the 1980s and 1990s is the locomotive (pioneer) sector. The locomotive sectors in 1996 are plastic products production and iron and steel industries. In 1998, the locomotive sectors are chemical products, synthetic rubber and plastic material manufacturing, iron and steel industry and metal industry. The locomotive sectors in 2002 are electricity, gas, steam and hot water production and distribution, textile products manufacturing, plastic and rubber products manufacturing, coke coal, refined petroleum products and nuclear fuel production and food and beverage production. Even though the manufacturing sector was the locomotive sector in the previous years, in 2002, the energy sector became a locomotive sector and contributed to economic revival. Conclusion In the years analyzed, the sub-sectors of the manufacturing industry in 1996 and 1998 are the sectors with high direct and total backward linkage effects. In 2002, it is seen that the energy sector has both high backward and forward linkage effects. When the Tables above are analyzed in detail, the sub-sectors of the energy sector score high in terms of both backward and forward linkage effects. According to Hirschman‘s categorization, the sectors with high backward and forward linkage effects at the same time are described as the locomotive sectors. Therefore, the sub-sectors of the energy sector in 2002 fit in this category. As a result, investment in the energy sector in Turkey should be increased. In this context, studies aiming to reduce dependency on foreign powers in energy should be done. Especially, the industrial model based on the fossil fuel increases dependency. Turkey can support the other sectors only if can it use resources such as wind, solar and hydrologic energy. References AKKAYA, ġ., PAZARLIOĞLU, M. V., (2000). Ekonometri I, Berk Masa Üstü Yayıncılık, Ġzmir, 581 s. AYDOĞUġ, O., (1993). Türkiye Ekonomisinde Maliyet-Fiyat ĠliĢkileri Sektörel Fiyat OluĢumu ve Enflasyon, 3. Ġzmir Ġktisat Kongresi, Sektörel GeliĢme Stratejileri, Ġzmir, 35-48 ss. AYDOĞUġ, O., (1999). Girdi-Çıktı Modellerine GiriĢ, Gazi Kitabevi, Ankara, 121 s. BENNETT, R. F., (2003). 10 Facts About Oil Prices, Joint Economic Committee, Economic Update, 4 p. TÜĠK, (1985). Türkiye Ekonomisinin Input-Output Yapısı 1985, TĠK Yayınları, Ankara, 87 s. TÜĠK, (1994). Türkiye Ekonomisinin Input-Output Yapısı 1990, TĠK Yayınları, Ankara, 89 s. TÜĠK, (2001). Türkiye Ekonomisinin Input-Output Yapısı 1996, TĠK Yayınları, Ankara, 141 s. TÜĠK, (2004). Türkiye Ekonomisinin Input-Output Yapısı 1998, TĠK Yayınları, Ankara, 127 s. 692 �2nd International Symposium on Sustainable Development, June 8-9 2010, Sarajevo LEBLANC, M.; and CHINN, M., (2004). Do High Oil Prices Presage Inflation? The Evidence from G-5 Countries, UC Santa Cruz Economics Department 2000-05 Working Paper Series, 25 p. TERZĠ, Ġ., (1998). Türkiye‘de Elektrik Tüketimi ve Ekonomik Büyüme ĠliĢkisi: Sektörel Bir KarĢılaĢtırma, Ġktisat-ĠĢletme ve Finans Dergisi, Ġstanbul, ss. 62-71. 693 � 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. 309 Title A name given to the resource The Forward and Backward Linkage Effects of the Energy Sector in Turkey Author Author MERCAN, Mehmet ÖZDEMIR, Abdullah Abstract A summary of the resource. Energy sector has a great importance for producers and consumers. Energy sector has been found as a leading sector as a result at the input-output analysis. This analysis has been done by using input-output tables which are constructed by goverment Statistical Institude. Turkey is dependent to other countries as energy. To satisfy the development in Turkish economy is only available by reducing the depandencies to the other countries by the energy. Also it should be continued as the leading sector. Date A point or period of time associated with an event in the lifecycle of the resource 2010-06 Keywords Keywords. Conference or Workshop Item PeerReviewed HB Economic Theory