76 10 3494 https://omeka.ibu.edu.ba/files/original/51d77364b63243cd6420b482dbfaa0ca.pdf 6e76307f14d5c2779db328c5358fbc0b PDF Text Text 1. International Symposium on Sustainable Development, June 9-10 2009, Sarajevo Determinants of Bank Efficiency in Turkey: A Two Stage Data Envelopment Analysis Ahmet AKIN Faculty of Economics and Administrative Sciences, Fatih University, Istanbul akin@fatih.edu.tr Merve KILIÇ Faculty of Economics and Administrative Sciences, Fatih University, Istanbul mervekilic@fatih.edu.tr Selim ZAĐM Faculty of Economics and Administrative Sciences, Fatih University, Istanbul szaim@fatih.edu.tr Abstract: Financial industry plays an important role in the economy and banks are indispensable players in the financial industry. Therefore, the evaluation of banks’ performance became a popular research topic in all over the world, and also in Turkey. There are different techniques to determine the banks’ performance. Among those techniques, Data Envelopment Analysis (DEA), which is a non-parametric technique, has been widely used in the banking sector. In this research, we analyzed the efficiency of Turkish Banking Industry with Data Envelopment Analysis (DEA) methodology between 2002 and 2007. All the banks that constantly operated in the years between 2002 and 2007, excluding investment and development, participation banks, get into the analysis. So there are four groups of banks in the research, those are state-owned deposit banks, privately-owned deposit banks, foreign banks founded in Turkey, and foreign banks having branches in Turkey. In the research model, number of employees, interest expenses, non-interest expenses and total deposit are determined as input, total credits, interest revenue and non-interest revenue are determined as output. This analysis aims to explain the variation in efficiency scores with a set of explanatory variables, such as size, ownership type, nationality, being publicly held. According to results, the efficiency levels do not change very much between 2002 and 2007. The efficiency scores reached top level in 2005 and 2006. The results of regression application denote that all of the explanatory variables have a significant effect on banks’ efficiency levels. According to regression analysis results, size negatively affects the efficiency levels of banks. Publicly listed banks operate more efficient than not publicly listed banks. Foreign owned banks operate more efficient than their domestic peers. Furthermore, state owned banks are less efficient than non-state banks. Keywords: Efficiency, Data Envelopment Analysis, Tobit Regression Model, Turkish Banking Sector. 1. Introduction With the changes in economical environment, financial institutions have an essential role in the developing countries’ economy. Especially banks are fundamental players in the financial industry. So the evaluation and assessment of banks’ performance, efficiency, and effectiveness have attracted considerable attention. Measurement of efficiency of banking institutions serves two important purposes. It helps to benchmark of an individual bank against the “best practice” banks and secondly, it helps to evaluate the impact of various measures on the efficiency and performance of these institutions (Das et al. 2009). But the performance measurement in banking sector is not so straightforward, because there are some difficulties in determining inputs and outputs of a bank for efficiency measurement. There is not consensus on that subject. Furthermore, banks may not be homogeneous with respect to types of output they produced. In Turkey context, some reforms were applied in banking industry after 1980s. The banking industry experienced some financial crises in November 2000 and February 2001. The efficiency level of banking sector decreased in those years. This situation required restructuring of banking sector in Turkey. There are some researches that investigate the performance of banking sector after liberalization policies in 1980s or the effects of financial crises on banking sector. There is not much study that investigates the recent efficiency of Turkish Banking Industry. 32 �1. International Symposium on Sustainable Development, June 9-10 2009, Sarajevo There are a lot of techniques to determine the banks’ performance. One of them is ratio analysis. In ratio analysis, measures such as return on assets (ROA), return on investment (ROI), liquidity ratios give only one dimension of performance. In this type analysis, different measures can give contradictory results. The regression analysis eliminates this disadvantage but it can only handle one output at a time. In the most of industries corporations especially in banking operate with many inputs and many outputs. Therefore there exists a requirement for efficiency measurement method besides ratio and regression analysis. Data envelopment analysis which was originally introduced by Charnes et al. (1978) is capable of solving multiple inputs and outputs and enables to see complete picture of performance of a company. This paper examines the efficiency of Turkish banking sector for the period 2002-2007. In this study the effects of size, ownership type, being publicly held on performance are also analyzed. There are two stages of this research. First stage is the analysis of efficiency levels of banks with Data Envelopment Analysis methodology and the second stage is determination of the effects of bank size, ownership type, nationality and being publicly held on bank efficiency scores by Tobit Regression. The paper is structured in the following way. Section 2 includes a brief review of the literature about bank performance and explanatory variables’ effects on banks’ efficiency levels. Section 3 denotes the research sample of this study, input and output variables that are used, and outlines the non-parametric Data Envelopment Analysis methodology. In the fourth section the results of the study are charted and the findings are discussed. And in the fifth section the conclusions of the study are presented. 2. Literature Review In a rapidly changing financial market worldwide, bank regulators, managers, and investors are concerned about how efficiently banks transform their expensive inputs into various financial products and services (Işık & Hassan 2002). So the investigation of the financial institutions has been motivated by academics, policy makers, bankers. There a lot of studies Sufian (2008), Işık (2008), Rezitis (2006), Işık and Hasan (2002), Das et al. (2009), Mercan et al. (2003) that examine the efficiency levels of banks with different methods. Some researchers investigated also the effects of some explanatory variables on bank efficiency, such as, size, type of ownership, bank configuration, being publicly traded or not. Bank size is generally measured by banks’ amount of assets. Jackson and Fethi (2000), Mercan et al. (2003), Rezitis (2006) analyzed the effect of the bank size on efficiency found a positive relationship between size and efficiency. Işık and Hassan (2002) determined a negative relationship between bank size and efficiency. Chen et al. (2005) indicated that large and small banks are more efficient than medium banks. Aly et al. (1990) investigated the effect of size on the overall efficiency, technical efficiency, allocative efficiency and pure technical efficiency of banks and they measured size as total deposits in thousand of dollars and number of bank branches. They found that size is positively related to efficiency, regardless of whether size was measured as total deposits or number of branches. There is no consensus on how bank size affects bank efficiency, but general view large banks are more efficient than small and medium sized banks. Hypothesis 1. Large sized banks are more efficient than small sized banks. The market hypothesis supposed that publicly traded banks should operate more efficient than not publicly traded. But studies that analyze the relationship between being publicly listed or not and bank efficiency generally indicate there is not a significant relationship. Sufian (2009) investigated the effect of being publicly listed on bank performance and did not find evidence of higher efficiency levels of the publicly listed banks. Havrylchyk (2006) studied on being publicly traded effect the performance of banks, but observed no impact of publicly traded on banks efficiency. Mamatzakis et al. (2008) discriminated the banks as publicly listed or not and analyzed the effect of being publicly listed on efficiency, the results do not reveal significant differences between publicly traded or not traded banks. Hypothesis 2. Being publicly held has no affect on bank’s performance. Jackson and Fethi (2000) analyzed the effect of ownership type on banks’ performance and according to their results state ownership worsens efficiency. Işık and Hassan (2002) found that private banks operate more cost efficient than banks in public sector. Mercan et al. (2003) classified the banks according to type of ownership as state-owned, private and foreign. State-owned banks had lowest performance in their study. Sufian (2009)’s study showed that the foreign banks are likely to be more efficient than domestically owned banks. Jackson et al. (1998) analyzed the performance of banks during the period 1992-96 in Turkey. Among three ownership types, private and foreign banks showed greater productivity growth compared to state owned banks. Chen (1998), Chen and Yeh (2000) analyzed the efficiency differences between private and public banks in Taiwan. The results indicated that private banks operate more efficient than public banks. Havrylchyk (2006) assessed the efficiency of foreign and domestic banks, showed that foreign banks are operating in a higher level of efficiency than domestic banks. Also, his study showed that state banks are more efficient than other domestic banks. Chen et al. (2005) grouped the Chinese banks as state owned commercial banks, national-joint 33 �1. International Symposium on Sustainable Development, June 9-10 2009, Sarajevo equity banks, regional-joint equity banks and investment banks to determine the relation between efficiency level and type of ownership. State banks showed a relatively higher efficiency level. Lensink et al. (2008) analyzed 2095 banks in 105 countries over the years 1998-2003 and found that foreign ownership negatively affects bank efficiency. Bonin et al. (2005) suggested that foreign-owned banks are more cost-efficient than other banks. Hypothesis 3. Private banks operate more efficiently than public banks. Hypothesis 4. Foreign ownership positively affects bank efficiency. 3. Research Methods 3.1. Sample The research sample of this study includes all the banks that operated in Turkey constantly between 2002–2007, excluding investment and development, and participation banks. This data set should be as homogeneous as possible to be meaningful for relative efficiency measurement for DEA application. So there are four groups of banks in the research, state-owned deposit banks, privately-owned deposit banks, foreign banks founded in Turkey, and, foreign banks having branches in Turkey. Total thirty-one banks from those groups are determined and get into the analysis. Table 1: The Banks in the Analysis B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 B12 B13 B14 B15 B16 ABN AMRO Bank N.V. Adabank A.S. Akbank T.A.S. Alternatif Bank A.S. Anadolubank A.S. Arap Türk Bankası A.S. Bank Mellat Citibank A.S. Denizbank A.S. Eurobank Tekfen A.S. Finans Bank A.S. Fortis Bank A.S. Habib Bank Limited HSBC Bank A.S. JPMorgan Chase Bank N.A Millennium Bank A.S. B17 B18 B19 B20 B21 B22 B23 B24 B25 B26 B27 B28 B29 B30 B31 ING Bank A.S. Societe Generale (SA) Sekerbank T.A.S. Tekstil Bankası A.S. Turkish Bank A.S. Turkland Bank A.S. Türk Ekonomi Bankası A.S. Türkiye Cumhuriyeti Ziraat Bankası A.S. Türkiye Garanti Bankası A.S. Türkiye Halk Bankası A.S. Türkiye Đs Bankası A.S. Türkiye Vakıflar Bankası T.A.O. Unicredit Banca di Roma S.P.A. WestLB AG Yapı ve Kredi Bankası A.S. 3.2. Measurement of Variables The necessary data set from the income statements and balance sheets of the banks is obtained from the annual issues of the Bank Association of Turkey. In the banking performance literature, there is no definite consensus on the determination of bank inputs and outputs. But there are two main approaches to determine the inputs and outputs that can be used for efficiency measurement; production approach and intermediation approach (Thanassoulis 1999; Sealey & Lindley 1977; Anthanassopoulos 2009). According to production approach, banks are regarded as using labor and capital to generate deposits and loans, and according to intermediation approach deposits are regarded as being converted into loans (Avkıran 2006). Avkıran (2006) summarized two approaches, and showed inputs and outputs for two approaches. Under production approach, number of employees, occupancy, furniture and equipment, other noninterest expenses are determined as input, number of demand deposits, time deposits, real estate loans, installment loans and commercial loans are determined as output. Under intermediation approach, deposits, debentures, other liabilities, shareholder equity, number of employees, physical capital, non-interest expenses are regarded as inputs, loans, securities, deposits with other banks, except central bank, non-interest income are regarded as outputs. Sufian and Majid (2007) employed DEA method to investigate the effects of merger and acquisitions on Singaporean domestic banking groups’ efficiency. They estimate two alternative models and they used total deposits as input, total loans and non-interest income as output in the first model, non-interest and interest income as output and interest and non-interest expense as input in the second model. Aysan and Ceyhan (2008) determined the inputs as labor, capital and loanable funds, outputs as short- and long-term 34 �1. International Symposium on Sustainable Development, June 9-10 2009, Sarajevo credits, off-balance sheet items, and other earning assets. Jackson et al. (1998) used number of employees and the sum of non-labor operating expense, the direct expenditure on buildings and amortization expenses as inputs, loans, demand deposits and time deposits as output under value-added approach. Wheelock and Wilson (1999) investigated the technical progress, inefficiency and productivity change between 1984 and 1993. They employed three inputs: labor, physical capital, purchased funds, five outputs: real estate loans, commercial and industrial loans, consumer loans, all other loans, total demand deposits by adopting intermediation approach. Oral and Yolalan (1990), Ayadi et al.(1998) determined inputs as interest paid on deposits, expenses on personnel, administration etc. and total deposits, outputs as total loans, interest and non-interest income. Aly et al. (1990) employed a non-parametric frontier approach in their study to calculate the overall, technical, pure technical, allocative and scale efficiencies of a sample of 322 independent banks. And, they determined the inputs as labor, capital, loanable funds, outputs as real estate loans, commercial and industrial loans, consumer loans, all other loans and demand deposits. Bergendahl (1998) applied DEA to Nordic Banks by using loan volumes, deposit volumes, and gross revenues as output, costs of personnel, cost of material and the volume of credit losses as input. Işık (2008) investigated the technical X-efficiency and productivity growth of novo banks and established banks by using a non-parametric frontier method. By employing intermediation approach the outputs that are used in his research are, short-term loans, long-term loans, and other earning assets, the inputs are labor, capital, and loanable funds. Havrylchyk (2006) investigated the efficiency of the Polish Banking industry between 1997 and 2001, and under intermediation approach he determined the inputs as capital, labor and deposits, outputs as loans, government bonds, and off-balance sheet items. Chen et al. (2000) analyzed the operating efficiency of 34 commercial banks in Taiwan banking sector. Under intermediation approach they determined outputs as provision of loan services, portfolio investment, and non-interest income, inputs as bank staff, assets and bank deposits for this analysis. Liu (2009) used slack-based efficiency measures to measure the efficiency of 24 banks in Taiwan; he employed deposits, interest and non-interest expenses as input, loans, interest income and non-interest income as output in his study. By taking into consideration the literature intermediation approach is used in the analysis. Number of employees, interest expenses, non-interest expenses, and total deposits are determined as input; total loans, interest income, and non-interest income are determined as output. All variables are measured in thousands of Turkish Liras, except number of employees. 3.3. Measurement of Efficiency The efficiency measurement is generally performed in several methods such as ratio analysis, parametric and non-parametric methods. In the ratio analysis, efficiency is measured with the calculation of several ratios of financial units. The financial unit with the highest output over input or lowest input over output is determined as efficient. But for the calculation of efficiency of financial units which operate multi-input and multi-output ratio analysis is not suitable. Another criticism about the ratio analysis is that some ratios denote that the firm has a successful level of performance but other may show the opposite. The regression analysis does not suffer from that disadvantage, but it assumes a priori form of functional relationship between inputs and outputs, in addition regression analysis can only handle one output at a time (Manadhar & Tang 2002). In the most of industries corporations especially in banking operate with many inputs and many outputs. Therefore there exists a requirement for efficiency measurement method besides ratio and regression analysis. There are another two techniques called as parametric and non-parametric enable efficiency measurement with many input many output. One of the nonparametric techniques which is widely used to measure efficiency is Data Envelopment Analysis (DEA). 3.3.1. The Data Envelopment Analysis (DEA) Model Data envelopment analysis (DEA) is a linear based programming model which was first proposed by Charnes et al. in 1978 twenty years after Farrell’s seminal work for evaluating activities of not-for-profit entities participating in public programs. Recent years a variety of DEA applications have been seen for evaluating the performances of different kinds of entities engaged in many different activities in many different contexts in many different countries (Cooper et al. 2004). DEA assess the comparative efficiency of homogeneous organizational units, such as bank branches, schools, tax offices, and hospitals (Thanassoulis 1999). DEA responds to the need for satisfactory procedures to assess the relative efficiencies of multi-input multi-output production units (Cook & Seiford 2008). The efficiency score is usually denoted as either a number between zero and one or 0 and 100 percent. The efficiency score of one or 100 percent of a decision making unit shows that decision making unit is efficient relative to other units in the research sample. In addition to providing meaningful scalar efficiency values, DEA is designed to determine the sources and estimate the amounts of inefficiencies that might present in the various output and input vectors (Charnes et al. 1991). The most important advantage of DEA over other traditional econometric frontier method is that it does not require prior 35 �1. International Symposium on Sustainable Development, June 9-10 2009, Sarajevo assumption (such as standard forms of statistical regression analysis) about the analytical form of the production function (Avkıran 1999; Banker 1984; Cooper et al. 2004). In addition, DEA enable to calculate the efficiency of decision making units that operates multi-input and multi-output. DEA is a valuable benchmarking tool, because it identifies inefficiencies in decision making units by comparing them with similar decision making units regarded as efficient (Avkıran 2006). Unlike other benchmarking tools that rely on the managers’ observation, comparison, DEA enables to identify best practices that are too complex to be identified (Sherman & Ladino 1995). On the other hand, the main problem about DEA model is that, it is a non-parametric method, so it is sensitive to the measurement problems (Al-Sharkas et al. 2008). The relative performance measurement of DEA is a two-staged process (Mercan et al. 2003): (i) Determining the best performing decision making units that produces greatest output with the least input. Assigning a DEA performance-index value of unity (1) to such decision making units and placing them on the efficient frontier. (ii) Determining the DEA performance-index values for all other decision making units in the set. Such values are represented by the distance of the less efficient units from the above defined efficient frontier. The decision making units in this subset use more inputs given an output level or produce less output for a specific level of inputs. DEA determines, the most productive decision making unit, the amount of excess resources used by inefficient decision making units, the amount of excess capacity or ability to increase service outputs in lessproductive units, the set of best-practice service units most similar to the less-productive units, referred to as the best-practice reference set (Sherman & Ladino 1995). Mathematical formulation of DEA model can be stated as: m Max Z o = ∑u r =1 n ∑v i =1 ro y ro (1) io x io Subject to the constraints: m ∑ r =1 n u rj y rj ∑ ≤ 1 for j = 1, 2, k v ij x ij i =1 (2) u ,v ro io ≥0 for r =1, m; and i = 1, n (3) Where: Zo : Efficiency score of oth decision making unit. : Observed value of input i for the decision making unit j. x ij yrj : Observed value of output r for the decision making unit j. u rj , vij : Weights of input r and output i of decision making unit j respectively. k m n : Number of decision making units. : Number of outputs. : Number of inputs. Linear programming expression of the DEA model is like that: m Max Z o = ∑ u ro y ro r =1 (4) Subject to the constraints: n ∑v x i =1 36 io io =1 (5) �1. International Symposium on Sustainable Development, June 9-10 2009, Sarajevo m ∑u r =1 n rj yrj ≤ ∑ vij xij for j = 1, 2, k (6) i =1 u ro , vio ≥ 0 for r =1, m; and j =1, n (7) 4. Results The efficiency scores of each bank included in the sample are shown in Table 2. The efficiencies of the banks are examined between the years 2002-2007 with input oriented CCR model. The banking sector operated higher than 0.8 of efficiency scores in the whole research period. The average scores are 0.87 in 2002, 0.89 in 2003, 0.84 in 2004, 0.91 in 2005, 0.92 in 2006, 0.88 in 2007. The efficiency levels increased after the 2002 and reached 0.92 point in 2006, and again decreased 0.88 in 2007. There is a recovery phase in banking industry performance. There are six banks that operated efficiently in the whole research period. So the 19% percent (six of thirty-one banks) operated constantly efficient from 2002 through 2007. Also, there are thirteen banks that operated inefficiently during all analysis period. So the 41% (thirteen of thirty-one banks) operated inefficiently between 2002 and 2007. Table 2: CCR-I Efficiency Scores (2002-2007) B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 B12 B13 B14 B15 B16 B17 B18 B19 B20 B21 B22 B23 B24 B25 B26 B27 B28 B29 B30 B31 Average 2002 1,00 0,76 1,00 0,86 0,79 1,00 1,00 0,63 0,77 0,73 1,00 0,72 1,00 0,94 1,00 0,81 0,78 1,00 0,64 0,77 0,85 0,76 0,79 0,94 0,97 1,00 0,75 0,72 1,00 1,00 0,88 0,87 2003 0,88 1,00 1,00 0,79 0,99 1,00 1,00 0,65 0,91 0,66 0,90 0,83 1,00 1,00 1,00 0,49 0,84 1,00 0,59 0,91 0,85 0,87 0,97 0,99 1,00 1,00 0,75 0,90 1,00 1,00 0,99 0,89 2004 1,00 0,55 1,00 0,99 0,83 0,77 1,00 0,66 0,75 0,62 0,86 0,78 1,00 0,78 1,00 0,40 0,86 1,00 0,84 0,72 0,82 0,66 0,76 1,00 0,83 1,00 0,82 1,00 0,91 1,00 0,80 0,84 2005 1,00 1,00 1,00 0,90 0,78 0,87 1,00 0,98 0,90 0,65 1,00 0,83 1,00 1,00 1,00 0,68 0,86 1,00 0,92 0,85 0,81 0,79 0,88 1,00 0,96 1,00 0,94 1,00 0,93 1,00 0,78 0,91 2006 1,00 1,00 1,00 1,00 0,82 0,78 1,00 0,97 0,91 0,75 0,96 0,87 1,00 0,98 1,00 0,86 0,85 1,00 0,77 0,88 0,85 0,68 0,85 1,00 0,98 1,00 0,91 1,00 1,00 1,00 0,87 0,92 2007 0,75 1,00 1,00 1,00 0,85 0,60 1,00 0,67 0,81 0,99 0,85 0,69 1,00 0,80 1,00 1,00 0,82 1,00 0,73 0,87 0,66 0,67 0,77 1,00 1,00 1,00 0,86 1,00 1,00 1,00 0,83 0,88 Frequency of Efficiency 3 4 6 2 0 2 6 0 0 0 2 0 2 2 6 1 0 6 0 0 0 0 0 4 2 6 0 4 4 6 0 37 �1. International Symposium on Sustainable Development, June 9-10 2009, Sarajevo The total efficiency, technical efficiency and scale efficiency of the banks are also analyzed and the findings are showed in Table 3. Input oriented CCR efficiency scores give the total efficiency of banks. Input oriented BCC results give the technical efficiency of banks. As CCR scores are divided by BCC scores, the outcome will give the scale efficiency of banks. The average technical efficiency levels of banks are higher than average scale efficiency levels within this period. It is observed that the increase in total efficiency level is mainly resulted from the increase in technical efficiency level. Given our results that Turkish banking sector suffered from scale inefficiency. Table 3: Total Efficiency, Technical Efficiency and Scale Efficiency of the Banks B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 B12 B13 B14 B15 B16 B17 B18 B19 B20 B21 B22 B23 B24 B25 B26 B27 B28 B29 B30 B31 Average Total Efficiency 0,94 0,88 1,00 0,92 0,84 0,84 1,00 0,76 0,84 0,73 0,93 0,79 1,00 0,92 1,00 0,70 0,83 1,00 0,75 0,83 0,81 0,74 0,84 0,99 0,96 1,00 0,84 0,94 0,97 1,00 0,86 0,89 Technical Efficiency 1,00 0,97 1,00 1,00 0,89 0,98 1,00 1,00 0,92 0,80 1,00 0,98 1,00 1,00 1,00 0,83 0,93 1,00 0,92 0,95 0,85 0,87 0,95 1,00 1,00 1,00 0,99 0,95 0,98 1,00 0,99 0,96 Scale Efficiency 0,94 0,91 1,00 0,92 0,95 0,85 1,00 0,76 0,91 0,91 0,93 0,80 1,00 0,92 1,00 0,85 0,90 1,00 0,81 0,87 0,95 0,85 0,88 0,99 0,96 1,00 0,85 0,98 1,00 1,00 0,87 0,92 Table 4: Tobit Regression Results (n=176) Size Nationality Ownership Publicly Listed Constant 38 Coefficient -0.0697283 -0.0762108 0.1843663 0.0757398 0.9239495 Standard Error 0.0231161 0.0217101 0.0341993 0.0248959 0.0145359 t-ratio -3.02 -3.51 5.39 3.09 63.56 P-value 0.003 0.001 0.000 0.003 0.000 �1. International Symposium on Sustainable Development, June 9-10 2009, Sarajevo The effects of bank size, ownership type, nationality and being publicly held on total efficiency are analyzed with Tobit Regression by STATA. Ownership type, nationality, size and being publicly held are employed as dummy variables. In this analysis, DEA efficiency score of a bank is determined as dependent variable. If the coefficient of an explanatory variable is positive, it increases the efficiency level of the bank. In spite of that if the coefficient of an explanatory variable is negative; it decreases the efficiency level of the bank. Significance level of the regression is 95%. The findings imply that all explanatory variables are significantly different from zero and have a significant effect on efficiency score of banks. Banks are grouped as large and small according to their assets levels in this study. Size has a negative effect on efficiency levels of banks, suggesting that the smaller the bank, the more efficient bank will be. There is a scale problem in Turkish banking sector, the banks can not use economies of scale advantage. There may be decreasing return to scale in Turkish banking sector. 1% percent increase in inputs results less than 1% increase in outputs. As the banks grow they become less efficient. Larger banks have lower efficiency which could be due to complex organizational structure and moral hazard behavior (Sufian & Abd. Majid 2007). In Turkey, smaller banks are typically newer and generally specialize in trade and finance and wholesale corporate banking and employ more professional and astute management teams (Işık & Hassan 2002). Because of competition small banks should operate efficient to survive especially in metropolitan markets. The results are accordance with Işık and Hassan (2002) on Turkish banking sector. To analyze the relationship between publicly traded and Turkish banks’ efficiencies a dummy variable is introduced as an explanatory data. Being publicly traded has a positive effect on efficiency levels of banks. So, the publicly traded banks operate more efficient than not publicly traded banks. That finding supports the market discipline hypothesis. According to this hypothesis banks whose shares are publicly traded should exhibit higher efficiency. Thus, the easily transferable ownership structure of firms creates incentives for both shareholders to monitor management performance and for bank management to improve their performance as it contains risks associated with moral hazard practices (Mamatzakis et al. 2008). Banks of different nations may have different outcomes with the same inputs. Thus, in this section the effect of ownership type and nationality on efficiency levels of banks are analyzed. Ownership dummy is determined as state and non-state banks. The positive sign of the coefficient on non-state ownership binary variable implies that non-state ownership improve the efficiency level of the banks. Non-state banks operate more efficient than state banks. There are two major reasons behind the efficiency difference between public and private firms. The first is while all private firms are profit maximizing, public firms would pursue whatever objectives the government demands. The second is while private firms are subject to relatively hard budget; public firms are subject to relatively soft budgets (Işık & Hassan 2002). Nationality has a positive effect on efficiency levels. The positive sign of the coefficient on foreign ownership binary variable implies that foreign ownership improve the efficiency level of the banks. Foreign banks operate more efficient than their domestic counterparts. This may be because of foreign owned banks have better risk management, operational, technological techniques which they enable from their parent banks abroad. The empirical observation that foreign banks perform better compared to domestic banks in developing countries. This suggests that technical ability of banks from developed countries overcomes the home field advantage in developing countries (Jeon & Miller 2005). Berger et al. (2000) explained the differences between home field advantages and global advantages. The global advantage hypothesis denotes that foreign banks might benefit from competitive advantages relative to their domestic banks. Foreign banks may also become more competitive when compared to domestic banks due to an active market for corporate control in the home country, and because they have access to an educated labor force that is able to adapt new technologies (Lensink et al. 2008). The results are accordance with Sufian (2008) on Malaysian banks, Jackson et al. (1998) on Turkish banks, Işık and Hassan (2003) on Turkish banks, Havrylchyk (2006) on Polish banks, Bonin et al. (2005) in transition countries. 5. Conclusion This paper aims to determine the efficiency of Turkish banks between 2002 and 2007. So, the efficiency levels of Turkish banking sector are analyzed during the period 2002-2007 with Data Envelopment Analysis. Then, multivariate regression analysis have been employed in order to detect the determinants of banking efficiency in Turkey. The sample includes thirty-one banks that continuously operated during this period. According to results, the efficiency level of Turkish banking did not change very much in the analysis period. The banking sector operated at stable efficiency level. The average performance values between 0.84 and 0.92 in this period. Additionally, the findings reveal that the average technical efficiency scores of banks are higher than average scale efficiency scores. There is a scale inefficiency problem in Turkish banking sector. The effects of some explanatory data on the banks’ efficiency levels are also analyzed in this research. Size, ownership type, nationality, being publicly listed are improved as dummy variables. Findings imply that 39 �1. International Symposium on Sustainable Development, June 9-10 2009, Sarajevo smaller banks are more efficient than larger banks. As the banks grow they become less efficient. The Turkish banking sector may experience decreasing returns to scale. Publicly listed banks are more efficient than not publicly listed banks. This finding is compatible with market discipline hypothesis which suggests stockholders of the banks can exert market discipline over bank management, so the publicly traded banks are expected to be more efficient. Non-state banks operate more efficient than their state counterparts. This may be because of the goals of those two banks differentiate. Private entities always aim to maximize their profit. The regression analysis results also denoted that foreign banks are more efficient compared to their domestic peers. Foreign banks might profit from better risk management and take advantage of technological improvements. References Al-Sharkas, A.A., Hassan, M.K., & Lawrence, S. (2008). The Impact of Mergers and Acquisitions on the Efficiency of the US Banking Industry: Further Evidence. Journal of Business Finance and Accounting, 35, 5070. Aly, H.Y., Grabowski, C.P., & Rangan, N. (1990). Technical, Scale, and Allocative Efficiencies in U.S. Banking: An Empirical Investigation. Review of Economics and Statistics, 30, 211-18. Anthanassopoulos, A.D. (1998). Nonparametric Frontier Models for Assessing the Market and Cost Efficiency of Large-Scale Bank Branch Networks. 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Chen, T.Y. & Yeh, T.L. (2000). A measurement of bank efficiency, ownership, and productivity changes in Taiwan. The Service Industrial Journal, 20 (1), 95-109. Cook, D. W., & Seiford, L.M. (2008). Data Envelopment Analysis (DEA)-Thirty Years On. European Journal of Operational Research, 19, 1-17. Cooper, W.W., Seiford, L. M. & Zhu, J. (2004). Data Envelopment Analysis: History, Models and Interpretations, in W.W. Cooper, L. M. Seiford and J. Zhu (edi.), Handbook on Data Envelopment Analysis. Kluwer Press: Norwell. Das, A., Ray S.C., & Nag A. (2009). Labor-use efficiency in Indian banking : A branch-level analysis. Omega, 37, 411-425. 40 �1. International Symposium on Sustainable Development, June 9-10 2009, Sarajevo Havrylchyk, O. (2006). Efficiency of the Polish Banking Industry: Foreign Versus Domestic Banks. Journal of Banking&Finance, 30, 1975-1996. Işık, I. (2008). Productivity, technology and efficiency of de novo banks: A counter evidence from Turkey. 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Global Economy Review, 34 (2), 145-165. Lensink, R., Meesters, A. & Naabag, I. (2008). Bank efficiency and foreign ownership: Do good institutions matter?. Journal of Banking & Finance, 32, 834-844. Liu, S.T. (2009). Slack-based efficiency measures for predicting bank performance. Expert Systems with Apllications, 36, 2813-2818. Mamatzakis, E., Staikouras, C., & Koutsomanoli-Filippaki, A., (2008), Cost efficiency of the banking industry in the South Eastern European region. Journal of International Financial Markets, Institutions and Money, 18, 483-497. Manandhar, R., & Tang, J.C.S. (2002). The Evaluation of Bank Branch Performance Using Data Envelopment Analysis: A Framework. Journal of High Technology Management Research, 13, 1-17. Mercan, M., Arnold R., Reha Y., & Ahmet B.E. (2003). The Effect of Scale and Mode of Ownership on the Financial Performance of the Turkish Banking Sector: Results of a DEA-Based Analysis. Socio-Economic Planning Sciences, 37, 185-202. Oral, M., & Yolalan, R. (1990). An empirical study on measuring operating efficiency and profitability of bank branches. European Journal of Operational Research, 46 (3), 282–294. Rezitis, N. A., (2007). Efficiency and Productivity Effects of Bank Mergers: Evidence from the Greek Banking Industry. Economic Modelling , 25, 236-254. Sealey, C. W. Jr., & Lindley, J.T. (1977). Inputs, Outputs, and a Theory of Production and Cost at Depository Financial Institutions. The Journal of Finance, 32 (4), 1251-1266. Sherman, H.D., & Ladino, G. (1995). Managing Bank Productivity Using Data Envelopment Analysis (DEA). Interfaces, 25, 60-73. Sufian, F. (2009). Determinants of Bank Efficiency During Unstable Macroeconomic Environment: Empirical Evidence from Malaysia. Research in International Business and Finance, 23, 54-77. Sufian, F. & Abd. Majid, M.Z. (2007). Bank Performance and the Determinants of Singaporean Banks’ Efficiency. Gadjah Mada International Journal of Business, 9, 19-39. Thanassoulis, E. (1999). Data Enevelopment Analysis and Its Use in Banking. Interfaces, 3, 1-13. www.tbb.org.tr Wheelock, D.C. & Wilson, P.W. (1999). Technical progress, inefficiency, and productivity change in U.S. banking, 1984-1993. Journal of Money, Credit and Banking, 31 (2), 212-234. 41 � https://omeka.ibu.edu.ba/files/original/9683e11e1eaf47673f32afb6f4b70b6d.doc 0034382326ea96da54d7ef03036daf29 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. 132 Title A name given to the resource Determinants of Bank Efficiency in Turkey: A Two Stage Data Envelopment Analysis Author Author AKIN, Ahmet KILIÇ, Merve ZAİM, Selim Abstract A summary of the resource. Financial industry plays an important role in the economy and banks are indispensable players in the financial industry. Therefore, the evaluation of banks’ performance became a popular research topic in all over the world, and also in Turkey. There are different techniques to determine the banks’ performance. Among those techniques, Data Envelopment Analysis (DEA), which is a non-parametric technique, has been widely used in the banking sector. In this research, we analyzed the efficiency of Turkish Banking Industry with Data Envelopment Analysis (DEA) methodology between 2002 and 2007. All the banks that constantly operated in the years between 2002 and 2007, excluding investment and development, participation banks, get into the analysis. So there are four groups of banks in the research, those are state-owned deposit banks, privately-owned deposit banks, foreign banks founded in Turkey, and foreign banks having branches in Turkey. In the research model, number of employees, interest expenses, non-interest expenses and total deposit are determined as input, total credits, interest revenue and non-interest revenue are determined as output. This analysis aims to explain the variation in efficiency scores with a set of explanatory variables, such as size, ownership type, nationality, being publicly held. According to results, the efficiency levels do not change very much between 2002 and 2007. The efficiency scores reached top level in 2005 and 2006. The results of regression application denote that all of the explanatory variables have a significant effect on banks’ efficiency levels. According to regression analysis results, size negatively affects the efficiency levels of banks. Publicly listed banks operate more efficient than not publicly listed banks. Foreign owned banks operate more efficient than their domestic peers. Furthermore, state owned banks are less efficient than non-state banks. Date A point or period of time associated with an event in the lifecycle of the resource 2009-06 Keywords Keywords. Conference or Workshop Item PeerReviewed HB Economic Theory https://omeka.ibu.edu.ba/files/original/3b4bfb2a7e1aa48e7d9e2fa42c76d68d.docx f3fe1455f2a116037178f31a640fe9e3 https://omeka.ibu.edu.ba/files/original/043ce3e6e5d295732da22e658a71df47.pdf ab82ee0627c74c3a17eca64f909fe153 PDF Text Text DETERMINANTS OF CRIME RATE IN EU: A SPATIAL ANALYSIS Gökçe Atlıhan İnce Yavuz İnce ABSTRACT It is essential to ensure peace and security for sustainable development. In recent years, economic and social factors are closely associated with the amount of increased crime, and economic crises increase the amount of crime has become a widespread notion. The purpose of this study is to determine how social and economic factors affect the occurrence of crime, and investigate the effects of the crisis on crime rate. Criminal investigations show that there is significant relation between crime and “place” of the crime. Crime rate shows different distribution characteristics, it decreases in some places, while increases in some places, hence; it is required a spatial perspective. Therefore, Techniques of Spatial Economic Analysis is used in this paper. The promise of using spatial data and analyses for crime control still remains to be demonstrated and depends on the nature of the relationship between crime and place. Theoretical concerns focus on how place might be a factor in crime, either by influencing or shaping the types and levels of criminal behavior by the people who frequent an area, or by attracting to an area people who already share similar criminal inclinations. While the crime rate in the model is the dependent variable, the net migration rate, unemployment rate, education level and per capita gross domestic product will be used as independent variables. Data covers the European Union countries and the year of data is 2010. The effect of these variables is observed to determine the amount of crime and whether or not it comes to a spatial effect is investigated. The relationship between migrations and crime is one of the problems on which for a long time now social research has been concentrating, mainly in countries characterized by important emigrational flows. This paper provides an empirical evaluation of whether one can uncover a link between crimes and, economic and social variables like unemployment rate, education level and per capita gross domestic product using a research methodology, additionally; impacts of last economic crisis on European Union countries are examined. � 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. 2147 Title A name given to the resource DETERMINANTS OF CRIME RATE IN EU: A SPATIAL ANALYSIS Author Author ATLIHAN INCE, Gokce YAVUZ, Ince Abstract A summary of the resource. It is essential to ensure peace and security for sustainable development. In recent years, economic and social factors are closely associated with the amount of increased crime, and economic crises increase the amount of crime has become a widespread notion. The purpose of this study is to determine how social and economic factors affect the occurrence of crime, and investigate the effects of the crisis on crime rate. Criminal investigations show that there is significant relation between crime and “place” of the crime. Crime rate shows different distribution characteristics, it decreases in some places, while increases in some places, hence; it is required a spatial perspective. Therefore, Techniques of Spatial Economic Analysis is used in this paper. The promise of using spatial data and analyses for crime control still remains to be demonstrated and depends on the nature of the relationship between crime and place. Theoretical concerns focus on how place might be a factor in crime, either by influencing or shaping the types and levels of criminal behavior by the people who frequent an area, or by attracting to an area people who already share similar criminal inclinations. While the crime rate in the model is the dependent variable, the net migration rate, unemployment rate, education level and per capita gross domestic product will be used as independent variables. Data covers the European Union countries and the year of data is 2010. The effect of these variables is observed to determine the amount of crime and whether or not it comes to a spatial effect is investigated. The relationship between migrations and crime is one of the problems on which for a long time now social research has been concentrating, mainly in countries characterized by important emigrational flows. This paper provides an empirical evaluation of whether one can uncover a link between crimes and, economic and social variables like unemployment rate, education level and per capita gross domestic product using a research methodology, additionally; impacts of last economic crisis on European Union countries are examined. 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 2013-05-24 Keywords Keywords. Article PeerReviewed Identifier An unambiguous reference to the resource within a given context ISSN 2233-1565 https://omeka.ibu.edu.ba/files/original/6cd99a8304d30d72bc3faaa33bbb38ba.pdf 1ce2f8a336d4f43eea925e0e63774403 PDF Text Text International Conference on Economic and Social Studies, 10-11 May, 2013, Sarajevo Determinants of Crime Rate in EU: a Spatial Analysis Gökçe Atlıhan İnce Gediz University, İzmir, Turkey gokceatlihan@gediz.edu.tr It is essential to ensure peace and security for sustainable development. In recent years, economic and social factors are closely associated with the amount of increased crime, and economic crises increase the amount of crime has become a widespread notion. The purpose of this study is to determine how social and economic factors affect the occurrence of crime, and investigate the effects of the crisis on crime rate. Criminal investigations show that there is significant relation between crime and “place” of the crime. Crime rate shows different distribution characteristics, it decreases in some places, while increases in some places, hence; it is required a spatial perspective. Therefore, Techniques of Spatial Economic Analysis is used in this paper. The promise of using spatial data and analyses for crime control still remains to be demonstrated and depends on the nature of the relationship between crime and place. Theoretical concerns focus on how place might be a factor in crime, either by influencing or shaping the types and levels of criminal behavior by the people who frequent an area, or by attracting to an area people who already share similar criminal inclinations. While the crime rate in the model is the dependent variable, the net migration rate, unemployment rate, education level and per capita gross domestic product will be used as independent variables. Data covers the European Union countries and the year of data is 2010. The effect of these variables is observed to determine the amount of crime and whether or not it comes to a spatial effect is investigated. The relationship between migrations and crime is one of the problems on which for a long time now social research has been concentrating, mainly in countries characterized by important emigrational flows. This paper provides an empirical evaluation of whether one can uncover a link between crimes and, economic and social variables like unemployment rate, education level and per capita gross domestic product using a research methodology, additionally; impacts of last economic crisis on European Union countries are examined. Keywords: Crime Rate, EU countries, Spatial Analysis. 128 � 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. 1515 Title A name given to the resource Determinants of Crime Rate in EU: a Spatial Analysis Author Author ATLIHAN INCE, Gokce Abstract A summary of the resource. It is essential to ensure peace and security for sustainable development. In recent years, economic and social factors are closely associated with the amount of increased crime, and economic crises increase the amount of crime has become a widespread notion. The purpose of this study is to determine how social and economic factors affect the occurrence of crime, and investigate the effects of the crisis on crime rate. Criminal investigations show that there is significant relation between crime and “place” of the crime. Crime rate shows different distribution characteristics, it decreases in some places, while increases in some places, hence; it is required a spatial perspective. Therefore, Techniques of Spatial Economic Analysis is used in this paper. The promise of using spatial data and analyses for crime control still remains to be demonstrated and depends on the nature of the relationship between crime and place. Theoretical concerns focus on how place might be a factor in crime, either by influencing or shaping the types and levels of criminal behavior by the people who frequent an area, or by attracting to an area people who already share similar criminal inclinations. While the crime rate in the model is the dependent variable, the net migration rate, unemployment rate, education level and per capita gross domestic product will be used as independent variables. Data covers the European Union countries and the year of data is 2010. The effect of these variables is observed to determine the amount of crime and whether or not it comes to a spatial effect is investigated. The relationship between migrations and crime is one of the problems on which for a long time now social research has been concentrating, mainly in countries characterized by important emigrational flows. This paper provides an empirical evaluation of whether one can uncover a link between crimes and, economic and social variables like unemployment rate, education level and per capita gross domestic product using a research methodology, additionally; impacts of last economic crisis on European Union countries are examined. Keywords: Crime Rate, EU countries, Spatial Analysis. 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 2013-05-10 Keywords Keywords. Article PeerReviewed Identifier An unambiguous reference to the resource within a given context ISSN 2303-4564 https://omeka.ibu.edu.ba/files/original/e1f52abb1e24c7a5d0bf9e02aef0d48d.pdf e00cc43fa64d9820656c0751071991d4 PDF Text Text DETERMINANTS OF FIRM PROFITABILITY IN CROATIA’S MANUFACTURING SECTOR Lorena Škuflić School of economics and business Croatia lskuflic@efzg.hr Danijel Mlinarić School of economics and business Croatia dmlinaric@efzg.hr Marko Družić School of economics and business Croatia mdruzic@efzg.hr Abstract: This paper investigates determinants of the profitability of industrial firms in Croatia, using data for large, medium and small companies for the period 2003-2014. This paper provides a broad theoretical review of the determinants of profitability analysed in economic literature with special remarks on firm level determinants, and explanation of most used variables such as size of firm, revenues, growth rate of revenues, sales, profit in previous years, ownership, productivity level, financial leverage, cost of inputs, indebtedness. Results from the panel ordinary least squares model for Croatia’s manufacturing sector reveal a positive and statistical significant relationship between profitability, total factor productivity, and concentration measured through Herfindahl-Hirschman index. On the other hand, indebtedness and liquidity show a negative relationship with the firm profitability of Croatia’s manufacturing sector. Keywords: Profitability, Determinants, Manufacturing Industry JEL Classification: D22, L10 1. Introduction Manufacturing industry represents a significant base for the long-term economic growth and development of each nation’s economy, especially when it comes to a small country which has to focus on foreign markets during its process of development. A strong industrial base, export-oriented, competitive in the international market, represents an economic objective of both developed and less developed countries. It is evident that the countries, which are in the process of its economic development, exceed from being industrial countries into being service countries, that is, after a certain degree of achieved economic development, industrial sector is being replaced by the service sector. Accordingly, and in the EU, there has been a reduction in the share of manufacturing industry in the gross domestic product, which, in the economic literature, is known as the process of de-industrialization.1 1 De-industrialization began in the mid-60s in the US and copied to Europe, and its manifestations are reflected in reduced number of employees in the manufacturing industry as a result of increased productivity and decrease in demand towards this segment of the economy. ICESoS 2016 - Proceedings Book 269 �International Conference on Economic and Social Studies (ICESoS’16) Reduction in the share of the manufacturing industry in total national production, regarding the number of employees, was affected by the changes in demand, increased productivity and dynamics in trade. The share of the manufacturing industry varies from country to country, ranging from 5.2% in value added GDP (Luxembourg, the year of 2013) to 22.2% (Germany, in 2013). Following the trend line in the manufacturing industry since 1970 to this date the shares have continuously declined in the European countries. In France, the share fell from 22.3% (1970) to 11.3% (2013), while significantly smaller decrease was recorded in Germany, from 27.8% (1970) to 22, 2% (2013). The country which recorded the largest decline in the share of value added in GDP is Luxembourg, where the share dropped from 41.5% (1970) to 5.2% (2013) (www.unctad.org). If the process of de-industrialization is to be observed from the of micro-entity aspect, then it may be due to the slower growth of those companies in relation to companies of the service sector. The cause for the slower growth lies both on the supply and the demand side. The company, like any other living organism has its path, from starting point to death, during which some companies grow faster, some slower, and some stagnate. Length of the company’s existence on the market is also individual and depends on the degree of adaptability to the environment. Companies that produce products with higher demand, achieve faster growth rates, and with the proper cost management they preserve better financial results, which then allow them a higher investment rate and investment in the production process technology. This simple explanation of profitability circle and company’s existence on the market, can be viewed from the Firm`s Theory aspect. The level of profitability depends on the ultimate goal of a business entity. Firm`s Theory, observed through prism of neoclassical school of economics, focuses on the production aspect, specifically on transformation of inputs into outputs, for which a level of technology and market environment, in which entities work, are important in terms of pricing and volume of production, and the market plays an important role on the output side in the design of consumer behaviour. The neoclassical theory company is based on market analysis models of perfect and imperfect competition, and within these other market structure on the analysis of oligopoly and monopoly, and monopolistic competition. The neoclassical theory company believes that the company is also the entrepreneur who is at the same time unified function of owner and manager, and whose main goal is to maximize profit, or to achieve maximum possible difference between total revenue and total costs. An entrepreneur makes the decision on how many inputs to be employed in order to achieve the planned level of production, taking into consideration the price of inputs and output prices on the market. Hence, in the basis of neoclassical theory, property and institution have no influence on the company’s goal, as well as on the amount of knowledge, technology and cost-effectiveness (as summarized set of production capabilities). For each level of output, the entrepreneur determines that quantity of input that will minimize the expenses, but only output, through which the maximum profit is achieved, is considered to be the balanced level of production, which presents the rational behaviour of entrepreneurs. Generally, by reducing the agriculture share, country carries out a higher level of national income and fewer people are employed in that sector, while the employment in the industry increases and so its share in value added, which gives way to the service sector after a certain stage. This process is associated with the reallocation of resources from less to more effective productions which implies structural changes. 270 ICESoS 2016 - Proceedings Book �Regional Economic Development: Entrepreneurship and Innovation All mentioned provides importance of profitability determinants for firm` performance and on the other hand, undoubted influence on the economy in the whole. Manufacturing industry in Croatia is the important contributor to the Croatian economic growth, especially in the past. It is arguable that the impact of determinants of profitability, throughout the world, is not similar on the firm` financial performance in every country that gives different influence on all stakeholders. Such reason gives motivation for this research which include quantitative data of all manufacturing companies in Croatia for over 11 years, from 2003 until 2014. All data gathered from FINA database. According to that, the aim of this research is to determine which microeconomic factors influence on the profitability of firm`s in Croatian manufacturing industry. The paper is organized as follows. Section 2 presents review of microeconomic determinants of firm profitability what includes theoretical background and related literature with discuss on main characteristics and relations. Section 3 gives extensive overview of methodology, used models and explanation of research results of determinants of profitability. At the end, Section 5 concludes the article and gives main points of research with recommendations for further researches. 2. Review of Microeconomic Determinants of Firm Profitability The connections between the profitability determinants and profitability of companies are well represented in previous researches. Most common question is what drives firm profitability unrelated to the firms’ essence. According to that, models of firm profitability can be classified into two major groups, structure-conduct performance (SCP) and firm effect models (Škuflić, Mlinarić and Družić, forthcoming). There has been a huge volume of literature to date that has sought to identify the determinants of firm profitability. Here are some of them. Gringer and McMKiernan (1991) focused on the determinants of profitability and showed that market share, capital intensity, growth of sales, working capital and decentralization play a significant role in explaining firm profitability. Brush et al. (1999) find that company and industry affect business profitability, but company has the larger influence. In addition to the size of the firms, and investment, some of the other determinants have also affected profitability, such as lagged profitability, a significant determinant of current profit margins, and that industry concentration is positively related to firm profits margins. Further, profit margins are found to be pro cyclical in concentrated industries but counter cyclical in less concentrated industries (McDonald, 1999). Similar, Feeny (2000) found a strong connection of a positive association between capital intensity, size and profitability. In addition, Nunes, Serrasquerio and Sequeria (2009) found a positive relationship between size, growth and profitability. Moreover, they concluded that higher liquidity will not decrease profitability. On the other hand, lower level of debt and lower level of fixed assets are more profitable. Depending on the research, firm-level or industryspecific effects are found to be the dominant factor on firm profitability. According to the all mentioned next Table briefly shows microeconomic determinants and their indicators of firm` profitability. ICESoS 2016 - Proceedings Book 271 �International Conference on Economic and Social Studies (ICESoS’16) Table 1: Microeconomic determinants of firm profitability Determinants Indicators 1. Lagged profitability - net profit from previous year 2. Firm` size - total assets; number of employees 3. Firm growth in industry - growth rate in operating income from the sale of company/growth rate in operating income of the industry 4. Age - years of firm existence 3. Ownership - share of state ownership - share of foreign ownership - cost of production - share of material costs in total costs - growth rate of material costs 4. Cost management - share of labor costs in total costs - growth rate of labor costs - cost of I&R - cost of advertising - financial leverage - leverage ratio - net debt/EBITDA 4. Leverage ratio; capital structure - self-financing coefficient - net debt ratio - share of loans in equity - EBIT/interest coverage ratio 5. Working Capital Productivity; Multifactorial Productivity - operating income/working hours - operating income/capital 6. Tax burden - paid taxes/total costs 7. Export oriented firm - amount of export 8. Import dependence - share of import in total sales 9. Human capital - number of high educated employees 10. Quotation on the stock exchange and EPS - dividends per share - annual growth (decline) rate of market price shares 11. Current ratio 12. Regional affiliation (location) Table 1 shows microeconomic determinants of firm` profitability with their indicators what is in continuation thoroughly explained with all significant literature and authors. CONCENTRATION Generally, in theory of industrial organization it is well known that concentration indicators are good approximation for market power, with positive correlation. There are a lot of researches about positive correlation, one of them is shown in „Oligopoly Theory“ of Stigler (1964). It is demonstrated positive correlation between profit maximization and relative firm size. Saving (1970) also showed same but in correlation 272 ICESoS 2016 - Proceedings Book �Regional Economic Development: Entrepreneurship and Innovation between firm` shares and Lerner index. Same results are shown also in Encaoua and Jacquernin (1980). According to that, most used approximation of concentration are Herfindahl-Hirschman index and concentration index, with positive correlation, what is in accordance with SCP paradigm (see: Bain, 1951; Demsetz, 1973; Peltzman, 1977). Most important concentration indicators, are briefly described below. Concentration index represents sum of market shares of k number of firm`s. This indicator shows share of total revenues or sales of firm in total revenues or sales of an industry. It is possible to calculate market shares of 4, 8, 20 or even 50 largest firms in the industry. The most common concentration ratios are the and the . Next Table shows classification of market structures for concentration ratio . Table 2: Classification of market structures for concentration ratio Market structure 0= Perfect competition 40 < < 0 Monopolistic competition 60 < =< 40 Weak oligopoly =< 60 Extremely oligopoly =< 90 Monopoly Herfindahl-Hirschman index is defined as the sum of the squares of the market shares of the firms within the industry, with equation The Herfindahl index provides a more complete picture of industry concentration than does the concentration ratio. Here are advantages of HHI: a) HHI gives distribution of markeet shares of four (or eight) firms and compozition of market not just for larger firms. b) HHI also gives more weight to larger firms, respectively it recognizes interction between larger concurents. Table 3: HHI classification of market structures HHI Imperfectly competitive market structure HHI < 1000 Monopolistic competition HHI < 1800 < 1000 Monopolistic competition or oligopoly HHI < 1800 Oligopoly or monopoly SIZE In theoretical and empirical economic literature, within the framework of current researches, issues of correlation of company’s size and profitability are indispensable. A variety of researches, as the main evidence of importance of company’s size arguments, that the average cost of operating a small business is higher than the average cost of operating large enterprises (Agiomirgianakis et al., 2013) so it is necessary to observe the relationship between the two variables. Large enterprises have higher levels of ICESoS 2016 - Proceedings Book 273 �International Conference on Economic and Social Studies (ICESoS’16) profitability when compared to small ones, primarily due to economy of scale. On the other hand, small enterprises are often new players on the market, so they take over market shares and profits of large companies (Papadogonas, 2007). According to above mentioned, and considering the majority of studies that show the significance of company’s size to its level of profitability (see Dwyer et al., 2010), we can generally state and assume a positive correlation. According to RBV theory, the positive correlation between company’s size and profitability, is a result of the more accessible access to capital and of suitability for utilization of the economy of scale’s principles, which ultimately leads to higher profitability. Furthermore, other studies also confirm the hypothesis of positive correlation: Gschwandtner (2005), Nunes et al. (2009), Fukao (2006), Asimakopoulos et al. (2009), Stierwald (2010). The most common choice for company’s size variable comes down to property size, number of employees and total sales, of which one of the most frequently used according to (Hirschey, 2008) is total sales. LAGGED PROFITABILITY Getting back to prior period assets, is mentioned in literature as an indispensable determinant because the lagged profitability is related to profitability of the next period. Positive correlation is expected, which has been confirmed in the works of Bothwell et al. (1984) and Fenny and Rogers (1999). AGE In the framework of the resource based view theory, RBV (see Jovanovic, 1982; Wernerfelt, 1984), where specific determinants of business enterprise have the greatest significance, it is assumed that the older the company is it can potentially acquire more resources (Autio, 2005), and the older the company is, it possesses higher amounts of information and more experience, enjoys a better reputation and it is enabled to have wider and better access to financing. However, there are a multitude of studies which confirmed the negative relationship of age and profitability, where as an important argument, the lag of older companies to market changes and innovations have been highlighted (Glancey, 1998). According to studies determinant of age can have a positive and a negative impact on profitability. INDEBTEDNESS Generally, indebtedness does not have to have only negative impact on profitability. Namely, if the borrowed funds are invested in products / services which bring an additional income, with the average profit values, indebtedness will in the long term have a positive impact on profitability. Thus, theoretical studies provide complex and intertwined answers on the impact of debt on profitability. The impact of debt on profitability can be divided according to three basic relations (Kebewar, 2012): a) Signal theory which assumes a positive impact of debt on profitability; b) The theory of agency costs: b1.) The correlation is positive if the capital’s agency costs are between the owners and manager, b2.) The correlation is negative if the agency costs of debt are between owners and lenders; c) The tax aspect - correlation is unpredictable, complex and depends on the tax evaluation of interests, income tax and tax valuation. 274 ICESoS 2016 - Proceedings Book �Regional Economic Development: Entrepreneurship and Innovation Such dual points of view are confirmed by the results of empirical researches, where the negative impact has been proved by Majumdar and Chhibber (1999), Eriotis et al. (2002), and Ngobo Capiez (2004), Goddard et al. (2005), Rao et al. (2007), and Tian Zeitun (2007) and Nunes et al. (2009). On the other hand, a positive impact has been proved by Baum et al. (2006, 2007), Berger and Bonaccorsi (2006), Margaritis and Psillaki (2007). The high rate of invested capital refund reflects the real state of the market, namely its imbalance. The degree of profitability is one of the most important indicators of market power. Studies, that tried to prove the connection between capital refund, industry concentration and also entry barriers, have been carried out. Weiss (1974) determined the link between profits, concentration and entry barriers. Salinger (1984) demonstrated that the MES in concentrated industries is linked with capital refunds, while this link was not found with other entry barriers variables such as the level of advertising (Carlton and Perloff, pp. 260-261). The capital structure, according to Bos and Fethersonu (1993), affects the profitability and the companies’ risk. There are several debt ratios used in studies within the capital structure. Muhammad (2003) concludes in his paper, that a certain level of indebtedness is desirable, but an excessive level leads to financial turmoil. It uses indicators such as the ratio of total indebtedness in relation to properties, total capital debt and long-term debt in relation to capital. Ventoura (2002) proves that the ratio of debt and capital has a negative effect on profitability. Finally, literature on the effect of capital structure and profitability states that there are certain circumstances in which the ratio is positive and also negative. FIRM GROWTH IN INDUSTRY According to Greiner (1972) growth of the company in relation to the profitability can have a positive and a negative effect. In case of a negative ratio, the cause is found in violation of interpersonal relationships within the company due to the increased growth of demands for increasing formal relations in order to achieve the necessary efficiency, which in the short term represents a challenge for the achievement of the desired level of profitability. The positive effect is explained by the increased motivation of employees that, given the growth in the future, they will achieve set goals and thus affect profitability. There are few studies which would undoubtedly lead to a defined impact of the company’s growth on the profitability, for example Roper’s (1999) and Gschwandtner’s (2005) study, whose results studied a statistically insignificant relationship between growth and profitability of the company. However, a positive relationship can be considered more natural as evidenced in the work of Serrasqueiro (2009). OWNERSHIP STRUCTURE High quality and comprehensive display for the significance of the ownership structure to the company’s management was given in the work of Berle and Means (1932), which demonstrate the advantages and disadvantages of public ownership. Since then, there are many studies that speak in favor of one or the other ownership structure, but there are also studies that have shown no effect (positive or negative) of property to profitability as in Demsetz and Lehn (1985), Himmelberg et al. (1999), Demsetz and Villalonga (2001), Holderness and Sheehan (1988) and Denis and Denis (1994). Shirley ICESoS 2016 - Proceedings Book 275 �International Conference on Economic and Social Studies (ICESoS’16) and Walsh (2000) explain the differences in company management, depending on the type of ownership. Finally, it is not possible to unambiguously determine the impact, because there are examples and studies for both types although it is expected that the companies in private ownership are more efficient and therefore more profitable. IMPORT DEPENDENCE According to Peltonen et al. (2008) import can have two different effects on profitability. First of all, it can influence in the way that it increases competitiveness on the market and consequently reduces profitability. It can also affect the competitiveness of companies which will ultimately increase profitability. By using the dynamic panel model Peltonen et al. (2008) demonstrated a negative and significant relationship between imports of goods and profitability. Such attitude is confirmed by the works of Sauner-Leroy (2003) and Boulhol (2005). Hansson (1992) determines a different result with respect to geographic region (country) in which the company operates. In summary, studies researching the determinants of profitability have identified several factors in many countries. However, they do not clearly indicate which factors are the most significant in relation to the firm profitability, although different factors have been identified as determinants of profitability in different countries by using the different methods of study. This is an area this research intends to explore. 3. Methodology, model and research results The analyzed period covers the years 2003-2014, for which the data were available. However, for our sample of companies relevant data are not abundant. All data gathered from Croatian database FINA. The FINA database contains tax return information on an annual basis. Each year all of entities in Croatia return data on their income, expenses, and other financial activities. The results have to be evaluated with the fact that some entities in sample may be used for tax planning purposes rather than for reporting the financial activities of a particular line of business. The use of tax entities for accounting purposes will affect the results of an investigation of the determinants of entity profitability using economic variables. This has influenced our choice of explanatory variables, as discussed above, but also a method of estimation. We have applied the panel data analysis method, using the unbalanced sample to obtain the estimated coefficients. It is well known that determinants are product of specific characteristics of industry and at the end economy, namely different variable has different impact and relation with profitability with other intensity (Škuflić, Mlinarić and Družić, forthcoming). This research employed the most important factors that influence firms profitability and that are commonly utilized through the previous researches. The variables and their used measurements are presented in Table 1. The dependent variable is profitability as measured by net profit before tax. As independent variables consider (1) indebtedness (Debt/EBITDA); (2) concentration (Herfindahl-Hirschman index); (3) liquidity (Current ratio); (4) productivity (Total factor productivity); (5) indebtedness (Indebtedness factor). 276 ICESoS 2016 - Proceedings Book �Regional Economic Development: Entrepreneurship and Innovation Table 4: Variables and measurement Dependent variable Measurement Profitability Net profit before tax Symbol Independent variables Indebtedness Debt/EBITDA ldug_EBITDA Concentration Herfindahl-Hirschman index lhhi2 Liquidity Current ratio lkoefteklik Productivity Total factor productivity LTFP Indebtedness Indebtedness factor lfak_zad Further, while current studies do indicate that panel data analysis is more suitable than other methods of study in determining the profitability of manufacturing companies (Pratheepan, 2014). Therefore this study also hopes to explore the relative importance of determinant of profitability by using the panel data analysis. Panel data (also known as longitudinal or cross sectional time-series data) is a dataset in which the behavior of entities are observed across time. Panel data allows you to control for variables you cannot observe or measure like cultural factors or difference in business practices across companies; or variables that change over time but not across entities. This is, it accounts for individual heterogeneity. With panel data you can include variables at different levels of analysis suitable for multilevel or hierarchical modeling. Some drawbacks are data collection issues, non-response in the case of micro panels or cross-country dependency in the case of macro panels. Usage of fixed-effects (FE) is appropriate in analyzing the impact of variables that vary over time. FE explore the relationship between predictor and outcome variables within an entity. Each entity has its own individual characteristics that may or may not influence the predictor variables. When using FE we assume that something within the individual may impact or bias the predictor or outcome variables and we need to control for this. This is the rationale behind the assumption of the correlation between entity’s error term and predictor variables. FE remove the effect of those time-invariant characteristics so we can assess the net effect of the predictors on the outcome variable. Another important assumption of the FE model is that those time-invariant characteristics are unique to the individual and should not be correlated with other individual characteristics. Each entity is different therefore the entity’s error term and the constant (which captures individual characteristics) should not be correlated with the others. If the error terms are correlated, then FE is no suitable since inferences may not be correct and you need to model that relationship (probably using randomeffects), this is the main rationale for the Hausman test. (Torres-Reyna, 2007) ICESoS 2016 - Proceedings Book 277 �International Conference on Economic and Social Studies (ICESoS’16) For the sample of our research, the fixed effects method is more appropriate than the random effects. This assumption was also formally tested. Breusch-Pagan Lagrange multiplier test is based on verification and selection an appropriate model between Ordinary Least Squares (OLS) or random model. The null hypothesis is when is the variance between the entities (in this case firms) zero, i.e., what implies that there is no panel effects, what allows us to conclude that the usual OLS is suitable method. However, the test result suggest rejection of the null hypothesis with on all levels of reliability, what implies that random model is more appropriate. Further, Hausman test helps us in making decision about better model between fixed and random. Main point is about whether the errors are correlated with regression. The null hypothesis presupposes that they are not, what goes in favour of random model, but if results are opposite, more appropriate is fixed model. The result of Hausman’s test indicates rejection of the null hypothesis at all levels of significance, and fixed effects is better model than random effects. Nevertheless, we have concluded that the fixed effects method should be applied in this case. The results of the estimation are presented in the following table: Table 5: Results of Breusch & Pagan and Hausman tests Breusch and Pagan Lagrangian multiplier test for random effects lPrfPRO[ID,t] = Xb + u[ID] + e[ID,t] Estimated results: 4.771073 .7250876 1.14926 lPrfPRO e u Test: Var 2.184279 .8515208 1.072036 Var(u) = 0 chibar2(01) = 63918.40 Prob > chibar2 = 0.0000 Coefficients (b) (B) fixed random ldug_EBITDA lfak_zad lkoefteklik LTFP lHHI2 sd = sqrt(Var) -.1819732 -.6143266 -.2401719 .0075825 .3131585 -.1885957 -.583628 -.2354695 .0086374 .35242 (b-B) Difference sqrt(diag(V_b-V_B)) S.E. .0066225 -.0306986 -.0047024 -.0010549 -.0392615 .0041334 .0033769 .0037796 .0002127 .0034747 b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(5) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 235.38 Prob>chi2 = 0.0000 278 ICESoS 2016 - Proceedings Book �Regional Economic Development: Entrepreneurship and Innovation The results of panel Ordinary Least Square (OLS), random effects, between effects and fixed affects are reported in Table 6. The basic equation for our model is as follows: Table 6: Results of panel data analysis method )1( )2( )3( )4( VARIABLES OLS Random Between Fixed ldug_EBITDA ***-0.306 ***-0.189 ***-0.364 ***-0.182 )0.0151( )0.0122( )0.0381( )0.0128( ***-0.455 ***-0.584 ***-0.356 ***-0.614 )0.0132( )0.0105( )0.0338( )0.0111( ***-0.199 ***-0.235 ***-0.231 ***-0.240 )0.00688( )0.00690( )0.0145( )0.00787( ***0.0109 ***0.00864 0.00499 ***0.00758 )0.00229( )0.00156( )0.0128( )0.00158( ***0.383 ***0.352 ***0.362 ***0.313 )0.00149( )0.00241( )0.00307( )0.00423( ***15.15 ***15.05 ***14.96 ***14.85 )0.0142( )0.0204( )0.0343( )0.0256( Observations 63,496 63,496 63,496 63,496 R-squared 0.625 0.620 0.462 12,888 12,888 lfak_zad lkoefteklik LTFP lHHI2 Constant Number of ID Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 12,888 The Table above shows dependent variable with profitability which is represent with net profit before tax and five independent variables. All variables are significant on 1%. Concerning statistical significance, estimated parameters and Breusch & Pagan and Hausman tests we have used fixed effects panel models. There is a positive and significant relationship between concentration which is represent with HerfindahlHirschman index and productivity with measurement in total factor productivity with dependent variable (profitability). Contrarily, there is negative and also strong significant relationship between indebtedness which is represent with ratio of debt and EBITDA, liquidity with current ratio and indebtedness factor and profitability. Further more, final and concrete conclusion about relation between profitability and selected microeconomic determinants in Croatian manufacturing industry are given in below. If we change indebtedness by one percent, we would expect profitability to change by -0,182%, in average. If we change Herfindahl-Hirschman index by one percent, we would expect profitability to change by 0,313%, in average. If we change current ratio by one percent, we would expect profitability to change by -0,24%, in average. If we change indebtedness factor by one percent, we would expect profitability to change by -0,614%, in average. If we change total factor productivity by one percent, we would expect profitability to change by 0.00758%, in average. ICESoS 2016 - Proceedings Book 279 �International Conference on Economic and Social Studies (ICESoS’16) The results showed evidence of a strong positive significant relationship between profitability and Herfindahl-Hirschman index and total factor productivity. The strongest negative correlation with profitability has indebtedness factor, followed by current ratio and indebtedness. 4. Concluding remarks Particularities of manufacturing industry, in general sense, considering the changes in macroeconomic and microeconomic indicators with their relation puts determinants of firm profitability for high level of importance for firm`s profitability in Croatia. The main contribution of this paper is the identification of the determinants affecting profitability of manufacturing firms in Croatia. A static panel model method is utilized on a sample of all manufacturing firms with business in Croatia covering the period between 2003 and 2014. Parameter coefficients show that market concentration (Herfindahl-Hirschman index) and total factor productivity had a significant and positive impact on the profitability for the manufacturing industry in Croatia during the study period. The results also showed a significant but negative relationship between indebtedness, current ratio and indebtedness factor. This results implies that concentration and indebtedness factor are determinants with larger influence on profitability and next researches have to absorb that facts. Further, provided results are in line with previous studies in the same area but for future researches we should pay attention to some notes from this research. First of all, there are restrictions about selection of the determinants (different number of determinants give different results), picked econometric tools and usage of different variation of panel (dynamic) models. Inclusive, it is common knowledge that the profitability determinants of manufacturing firms are very important according to the economic development of any country, especially to countries adopting an export what includes oriented industrialization policy within an open economic environment, according to that, more researches in this area are necessary. References • • • • • • • • • Agiomirgianakis, G. M., Magoutas, A. I. i Sfakianakis (2013). Determinants of Profitability in the Greek Tourism Sector Revisited: The Impact of the Economic Crisis, Journal of Tourism and Hospitality Management, Vol. 1 No. 1, 12-17. Autio, E. (2005). Toward a Theory of International New Ventures, Journal of International Business Studies, Vol. 36 No. 1, 9-19. Berle, A. i Means, G. (1932). The Modern Corporation and Private Property. Chicago Bos, T. i Fetherson, T. A. (1993). Capital structure practices on the specific firm. Research in International Business and Finance, Vol. 10, 53-66. Boulhol, H. (2005). Why Haven’t Price-Cost Margins Decreased with Globalization?, Mimeo Brush, T. H., Bromiley, P. and Hendrix, M. (1999). The relative influence of industry and corporation on business segment performance: An alternative estimate. Strategic Management Journal. 20(6), 519-547. Demsetz, H. i Lehn, K. (1985). The Structure of Corporate Ownership: Causes and Consequences. Journal of Political Economy 93, 1155-1177. Demsetz, H. i Villalonga, B. (2001). Ownership Structure and Corporate Performance. Journal of Corporate Finance 7, 209-233. Denis, D. i Denis, D. (1994). Majority Owner-managers and Organizational Efficiency. Journal of Corporate Finance 1, 91-118. 280 ICESoS 2016 - Proceedings Book �Regional Economic Development: Entrepreneurship and Innovation • • • • • • • • • • • • • • • • • • • • • Dwyer, L., Forsyth, P. i Dwyer, W. (2010). Tourism Economics and Policy, Channel View Publications, London, European Central Bank, Online Statistical Database Economics, 13, 235-252. Fenny, S. i Rogers, M. (1999). The Performance of Large Private Australian Enterprises, Working Paper No. 2/99, Melbourne Institute of Applied Economics and Social Research, University of Melbourne Glancey, K. (1998). Determinants of Growth and Profitability in Small Entrepreneurial Firms, International Journal of Entrepreneurial Behavior and Research, Vol. 4 (1), 18-27. Greiner, L. (1972). Evolutions and Revolutions as Organizations Grow, Harvard Business Review, 50, 37-46. Grinyer, H. P. and McKiernan, P. (1991). The determinants of corporate profitability in the UK electrical engineering industry. British Journal of Management, 2(1), 1732. Gschwandtner, A. (2005). Profit Persistence in the ´Very` Long Run: Evidence From Hansson, P. (1992) The discipline of imports: The case of Sweden, The Scandinavian Journal of Economics, Vol. 94, No. 4, 589-597. Himmelberg, C., Hubbard, G. i Palia, D. (1999). Understanding the Determinants of Managerial Ownership and the Link between Ownership and Performance. Journal of Financial Economics 53, 353-384. Holderness, C. i Sheehan, D. (1988). The Role of Majority Shareholders in Publicly Held Corporations. Journal of Financial Economics 20, 317-346. Jovanovic, B. (1982). Selection and the Evolution of Industry, Econometrica, Vol. 50 No. 3, 649-70. McDonald, J. T. (1999). The determinants of firm profitability in Australian manufacturing. The Economic Record, 75 (229), 11-126. Muhammad M. (2003). The relationship between economic growth and capital structure of listed companies: evidence of Japan, Malaysia and Pakistan. The Pakistan Development Review, 42:4 Part II, 727-750. Papadogonas, T. (2007). The Financial Performance of Large and Small Firms: evidence from Greece, International Journal of Financial Services Management, Vol. 2, No. 1/2, 14-20. Peltonen, T. A., Skala, M., Rivera, A. S. i Pula, G. (2008). Imports and profitability in the euro area manufacturing sector – The role of emerging market economies, WPS 918, ECB Pratheepan, T. (2014). A panel data analysis of profitability determinants empirical results from Sri Lankan manufacturing companies. International Journal of Economics, Commerce and Management, Vol. 2, Issue 12. Roper, S. (1999). Modelling Small Business Growth and Profitability, Small Business Sauner-Leroy, J.-B. (2003). The impact of the implementation of the Single Market Programme on productive efficiency and on mark-ups in the European Union manufacturing industry, European Economy, European Commission DirectorateGeneral for Economic and Financial Affairs, Economic Papers, No 192 Schmalensee, R. (1989). Intra-industry profitability diferendec in US manufacturing 1953-1983, Journal of Industrial economics, June 1989, No. 4, Volume XXXVII Serrasqueiro, Z. (2009). Growth and profitability in Portuguese companies: A dynamic panel data approach. Economic Interferences, Vol. 11, Num. 26, 565573. Shirley, M., Walsh, P. (2000). Public versus Private Ownership: The Current State of the Debate. World Bank Policy Research Working Paper 2420 Survivors and Exiters, Applied Economics, 37, 793-806. ICESoS 2016 - Proceedings Book 281 �International Conference on Economic and Social Studies (ICESoS’16) • • • • Škuflić, L., Mlinarić, D. and Družić, M. (2016). The determinants of profitability: evidence from Croatian construction sector. (forthcoming) Torres-Reyna, O. (2007). Panel Data Analysis Fixed and Random Effects using Stata. Princeton Univesity Ventoura-Neokosmidi, N. (2002). Sales efforts, capital structure and monopoly power. Managerial Finance, Volume 28, No. 5 Wernerfelt, B. (1984). A Resource-Based View of the Firm, Strategic Management Journal, Vol. 5 No. 1, 171-80. 282 ICESoS 2016 - Proceedings Book � 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. 3328 Title A name given to the resource DETERMINANTS OF FIRM PROFITABILITY IN CROATIA’S MANUFACTURING SECTOR Author Author Skuflic, Lorena Mlinaric, Danijel Druzic, Marko Abstract A summary of the resource. Abstract: This paper investigates determinants of the profitability of industrial firms in Croatia, using data for large, medium and small companies for the period 2003-2014. This paper provides a broad theoretical review of the determinants of profitability analysed in economic literature with special remarks on firm level determinants, and explanation of most used variables such as size of firm, revenues, growth rate of revenues, sales, profit in previous years, ownership, productivity level, financial leverage, cost of inputs, indebtedness. Results from the panel ordinary least squares model for Croatia’s manufacturing sector reveal a positive and statistical significant relationship between profitability, total factor productivity, and concentration measured through Herfindahl-Hirschman index. On the other hand, indebtedness and liquidity show a negative relationship with the firm profitability of Croatia’s manufacturing sector. Date A point or period of time associated with an event in the lifecycle of the resource 2016 Keywords Keywords. Conference or Workshop Item PeerReviewed H Social Sciences (General) https://omeka.ibu.edu.ba/files/original/1c6b895704384d8b1cc4dec738becddd.pdf a5f7661d67172e97d7edbfc302e9607d PDF Text Text 2nd International Symposium on Sustainable Development, June 8-9 2010, Sarajevo Determinants of Firm Survival in Manufacturing Industry: A Research on Lake Region in Turkey Hakan DEMĠRGĠL Assis.Prof.Dr.,Süleyman Demirel University, Isparta, Turkey demirgil@iibf.sdu.edu.tr Murat KARAÖZ Assoc.Prof.Dr., Akdeniz University, Antalya, Turkey mkaraoz@akdeniz.edu.tr Bekir GÖVDERE Assis.Prof.Dr., Süleyman Demirel University, Isparta, Turkey bgovdere@iibf.sdu.edu.tr Muhlis CAN Res.Assist., Hakkari Üniversitesi, Hakkari, Turkey muhliscan@hakkari.edu.tr Abstract: This paper gives an empirical analysis of determinants of firm survival in the manufacturing sector of Lakes Region (Turkey) from 2003 to 2009. The survival activities of the firms are measured through a four-dimensional, namely firm based, industry/environment based, innovation based and human capital based, questionnaire consisting of 49 questions. For the application logistic regression method is used for the evaluation of survival probability and the findings are compared with the basics of the related literature. According to the results of the empirical work, the effects of the ―Firm Based‖ factors on the survivals and growth performances of the firms located in the region are found to be more significant and positive than the remaining dimensions. Introduction Studies, depending on market entry and exit dynamics, have emphasized on the importance of the the high turbulence level in the industry in recent years. Applies to all sectors and all the economies, most of the new companies stop their activities a few years after entering to the markets. Chance of firms‘ survival and improving their performance for growth depend on following successful strategies which can be adaptable to changing environmental conditions. The difference in survival rate of firms till to the 1980s has not been got attention in the literature as deserved. However, after 80s, there has been an increasing interest in performance of the firms to survive and the studies have focused on the determinants of survival. The relationship between scale and growth rate were investigated in the studies, related to the performance levels of firms in the 1950s and 1960s by using firm-level data. However, nowadays, the studies gives particular importance to firm-level factors which effect the survival rate. Economists have started to use the market selection models which affect the survival of firms to explain the empirical findings. Based on previous studies of the factors, affecting the survival of companies, can be classified under three categories : 1-) Firm-based factors 2-) Industry-based factors 3-) Local factors At a level in firm-based , the most important and best-known variables that affect the chance of firms‘ survival are firm‘s age and its scale. The changes in the scale of the firms occuring in the course of time also reflect the interactions between entry and exit. The most important determinants of the survival chance at a level in the industry are market scale and growth rates, technology properties and product life cycles. Among these three categories, human capital and local factors are evaluated with different measurement in the literature of survival. Human capital is generally a variable related to individual characteristics of the the owner‘s of the company, entrepreneur‘s, manager‘s and employee‘s (Van Praag 2002). Therefore, in the analysis of 210 �2nd International Symposium on Sustainable Development, June 8-9 2010, Sarajevo survival of the firms, human capital variables is not used whether in local or regional level (Acs and Armington 2006). Human capital is evaluated in the firm-based level because of reflecting its own specific effects. In addition to these three categories, especially at the end of the 90s, the impact of the innovative efforts of companies to survive has started to take over. Innovation in a changing environment will provide a competitive position to the company and this will increase the firm‘s potential for being successful in the market. This effect is important for both the firms that are operating in the industry and the firms being freshly established and enter to the market. Innovation can not only increase the chance of survival of new firms by means of enabling them to enter suitable niche markets but also reduce the negative effects of tecnologies which are new and devoloping or desructive upon the avaliable firms. Determinant Features of Survival of Firms Classifying of the factors affecting survival in the industry-based and firm-based would be appropriate to form a starting point. Industrial attributions include both time-based factors and external variables which show the effects amidst the markets. This scope is a result of the form and rate of technical changes and different demand attributions. Therefore, affecting the survival of firms, owned properties are regarded as internal factors (Agarwal and Gort 1997). The other important point should also be noted that the survival of the firms is seen as a function of the variables of the firm and variables of the industry. The entering to the market and existing of the firms from the markets are important process which affects the degree of competition / efficiency and evolution of the industry. These two cases have important impacts on the distribution of resources, increasement of productivity with innovation rate and renewal of the industry. Although, existing of firms from markets have both social and economical effects, the number of studies is very low regarding factors which determine the risk of exist. Factors which increase the effectiveness of the company are the important determinants of survival as well (Perez et al.2004). Most of the recommended practices which increase the probability of survival are expanding export rates and increasing R & D activities ( providing information about foreign markets, guiding in entrance to the market and providing export structure, support policies into investment R & D ) Determinants of survival of the firms which newly enter are avaliable in industrial organization literature. According to Geroski (Geroski 1995), one of the important points related to market entry are age and scale which are directly related with the chance of firm‘ s survival. As first put forward by Stinchcombe (Stinchcombe 1965), new firms are faced with ― barrier to being new‖ and this is a risk of failure compared with large firms. During the period of starting, the firms are faced with the problem of completion in efficieny levels and organizational effectiveness to maintain their speed in the same level with competitors. In order to overcome this case, obtaining of capital and labour force, establishment of business connections with suppliers and finding of customers for products are essential points. The studies related to entry and exit of the firms, performance after entry and evolution of industry do not fully deal with the location, especially the role of region at the time of entry. The problems of the countries in which the risk ratio is different in local center and out of city center and where the economic activities are intensive in only a few local center can be eliminated by application of the development policies. Location selection is the most important strategic decisions of the company at the begining. A large part of the new firms which start operations are located in clusters where the firms have particular relations between each other and where the institutions are geographically concentrated (Pe‘er and Vertinsky 2006). Scale and scope not only allow the creation of organizational capital, but also provide different benefits. Scope provides diversity in the organizational capital while scale gives the depth to the organizational capital. A larger scale and scope of the firm can increase chance of survival when faced with problems of money, connections and provide benefits such as work Schedule (Bercovitz and Mitchell 2007). There are some structural factors such as firm‘s age and its scale which play important role in the industrial economy. Differences among the chance of survival among the firms are seen the results of the selection process. Experience will increase the chance of firm‘s survival and therefore, life curves of the firm has extended with the scale of the firm. Innovation may increase chances of the firms‘ survival by contributing to the development of appropriate strategies. The existing firms in the market is also under risk because of recession/ fluctuation in the industry that are results of tecnological changes occurring in its nature. Innovation activities increase their abilities to being alive permanently. Level of competition, demand predictability, the rate of tecnical change and its structure vary among different industries and these affect the chance of survival. The industry characteristics affecting the survival can be analyzed in two groups (Agarwal and Gort 1997):  Specifications for all products in this group vary in course of time or more distinctly, in the period of consecutive product life cycle. 211 �2nd International Symposium on Sustainable Development, June 8-9 2010, Sarajevo  In the second group, industrial properties change between industries thanks to all stages of the life cycle or a large part of life cycle. The conditions of industry at the time of entry have two significant reasons for firms which newly enter to the industry. Firstly, the firms must make large irremeable investments in order to compete with the firms which are already exist in the industry. If the firms flunk out from the industry, these investments would be appraised as sunk costs and it is considered that these investments affect dynamics and profitabilitiy of the company. Therefore, identification of industry‘s conditions that reflect the nature of cost at the time of entry enable to understand how this kind of structural barrier effect the performance after entry. Having a high human capital attributes of entrepreneur will decrease the ambiguities about the efficiency level and also enable to firm to adapt market condition and enable faster at the time of arranging the capacity so this will reduce the probability of exit. The entrepreneurial human‘s impact on the success of new firm may start before establishment. The entrepreneurs, who have opportunity to capture lucrative market and have ability to access relevant information, get the advantages because of past experience. High level of previous experience is not only increasing economic performance, but also opportunity of obtaining of entrepreneurs and the expected income level of entrepreneurs in alternative business sectors. Therefore, a skilled entrepreneur performs better when he works for himself, also he will have high performance that he needs to go on his business life (Gimeno et al. 1997). Has previously worked in similar industries or individuals who set up a new company in the same region, command the organization of firm associated with business environment, relational features of sector and contex of sector (Santarelli and Vivarelli 2007). One of the factors that increase the survival of institution which is related to founder, owner and employees level of education can be found in lots of studies. The founder of firm can be accepted as the first builder of organization‘s both structure and strategy. The role of the founder is inevitably an important position in capitalist economy. When viewed from this aspect, the level of education of the founder should be considered as one of the important determinant affecting the performance of the firm (Nelson 2003). Research Research Sample The universe of firms located in Lake District region are in manufacturing industry. The number of firms in the sample set is 60. This sample was used in Dr.Bekir Sami Oğuztürk dissertation thesis titled ― The Role of Innovation in Regional Development and a research in Lake District‖. The firms which were applicated in survey in the sample set are compared after 5 years with a new survey which was prepared by us to investigate the cases whether they are survive or not. 39 of 60 firms are located in Isparta, 21 of them are located in Burdur. The survey of the study was applicated by meeting individually with each firms owner, partner, manager, department manager of the firms including active or not active at that time. 12 of the firms (20% of the sample) in the sample ended their activity in 2008. Construction Timber Textile Cosmetic Food Bait Machine Gun Tarımsal Sulama Total ISPARTA 3 6 13 4 8 1 4 39 BURDUR 3 8 8 1 1 21 Table 1. The Sectoral Distribution of Sample Firms Sample included firms in other sectors of the provincial distribution are included in Table 1. Looking at the sectoral distribution of firms, in both provinces is seen as a condensation on the timber industry. Wood products industry is identiy of the region. The industry has a long history in the region and this industry which has provided value added to the region for many years and has also included a large portion of employment. Naturally, in the set of sample, most of companies are performing in timber industry in both cities. Looking at the city-level, after timber sector, 2nd intensive industry is textile in Isparta. The reason is city‘s past experience 212 �2nd International Symposium on Sustainable Development, June 8-9 2010, Sarajevo in carpet and yarn, and also having infrastructure. In Burdur, most of firms are performing in timber industry and in machine industry. Considering high agricultural production level in Burdur, it has a considerable potential in the production of agricultural equipment and machines. Research Data The source of data for study is a survey, consisting of 39 questions about firms‘ survival and growth performance, was applied to the firms between June-July 2008. This survey consists of four main headings: firm information, product and sales information, staff information working in branch, firm‘s innovation performance. Moreover, the data belongs to 2003 was obtained from survey which was used in dissertation thesis of Dr.Bekir Sami Oğuztürk. Influential factors on the growth performance of firms for survival analysis will be collected under four main headings to be used for the growth analysis by making totalitarian evaluation. The headings under which analysis of growth are categorized as firm characteristics, industry / environmental characteristics, innovation activities and human capital ( for entrepreneurs and workers). Variables, grouped under four headings, have detailed explanations above. The symbols and the use of variable are presented in Table-2 VARIABLES Exit (EXT) DEFINITION For the survived firms 1 Otherwise 0 Firm Age (AGE) For the survived firms difference between founding date and year 2008, for the exit firms difference between founding date and exit date. Firm Size (SIZE) Number of employees in 2003 at the firm Export (EXPRT) For the exporter firms 1 Fort he non-exporter firms 0 Diversification (DVSF) Number of plant, branch office and bussiness concerns ecept headquarter Minimum Efficient Scale (MES) Proportion number of emplyees at the firm to number of employees in own sector. Industrial Growth (IGRWT) Mean of sectoral growth rates fort he last four years Location (LCT) Isparta ilinde faaliyet gösteren firmalar için 1, Burdur ilinde faaliyet gösteren firmalar için 0. Innovation Activities (INNO) Ġf the firm has bought/developed; a-) A new product , b-) A new production system and/or technology 1 Otherwise 0 Patents/Industrial Design (PTNT) Number of patents and/or industrial design certificate Research and Development (RD) For the regular R&D activities 1 Otherwise 0 Educational Level of Employees (ELE) (Graduate employment)/(Total employment) Table 2. Variables Used In The Analysis Logistic regression model ( logit) will be used to distinguish the survival firms and failed firms in 5 years period after 2003. Logit model is explained as below with estimation of a specific event / fact with β probality parameters. 213 �2nd International Symposium on Sustainable Development, June 8-9 2010, Sarajevo (1) In logit model, β coefficient is calculated by maximum probabilty method. Thus, taking into consediration of the firm‘ s situation within a specific period, it is possible to calculate the probability of firms‘ survival. Results The main features of firms being inclusive of sample are shown in Table 3. Accordingly, in the region, the firms' average age was 26 and this age level shows that most of the firms were founded in the early 80s and 90s while the industrialization was accelerated in Turkey. The firms in the region have an avarage level of foreign sales opportunity. 57% of firms are exporting. In terms of employees or scale, firms are in middle sizes (average number of employees is 73). Although there is no setted innovative structure and culture, 42% of the firms are engaged in informal R&D activities. Although the structure of firms are generally limited liability and joint-stock company, the most prominent characteristic for all the firms is family business. Finally, it is observed that 83% of the firms‘ founders in the sample have experience in the same or different sectors as an entrepreneurial. COMPANY STRUCTURE EXT AGE SIZE EXPRT DVSF MES IGRWTH LCT INNO PTNT RD ELE NUMBER OF OBSERVATIONS MEAN STANDART DEVIATION 60 1.13 0.34 60 60 60 60 60 60 60 60 60 60 60 60 0.8 26.03 72.78 0.56 1.88 47.84 7.35 1.35 0.53 0.45 0.41 9.27 0.40 22.25 103.2 0.49 1.7 15.4 7.46 0.48 0.50 2.65 0.49 8.64 MINIMUM MAXIMUM 1 (Corporate) 0 3 7 0 1 10 -3.07 1 0 0 0 0 2 (Individual) 1 153 431 1 11 100 19.17 2 1 20 2 38.71 Table 3. Descriptive Statistics The correlation analysis will be made in order to see whether there is a directly relationship amidst explanatory variables which cause an increasement in the estimate values‘ of standart errors of parameters before making econometric analysis. Table 4 contains the results of correlation analysis. When we examine the values of co-efficient resulted from correlation analysis, it can be seen that there is no highly relationship which can cause multicolinearity problem between independent variables. Table 2 will also help us to see the direction of relationship between firms growth rate and survival chances with independent variables The Stata 1.9 statistical program was used for all models while the researh findings are being obtained. In the findings section, the determinants which affect the firms‘ survival including firm-based, industry and environment-based, human capital and innovation-based, growth perfomance factors will be examined with an order. 214 �2nd International Symposium on Sustainable Development, June 8-9 2010, Sarajevo EXT AGE SIZE EXPRT DVSF MES IGRWTH LCT INNO PTNT RD EXT AGE SIZE EXPRT DVSF MES IGRWTH LCT INNO PTNT RD ELE 1 0.32 0.05 0.25 0.28 -0.02 0.09 -0.15 0.08 0.05 0.28 0.26 1 0.18 0.18 0.13 -0.12 0.14 -0.14 -0.01 0.20 0.38 0.41 1 0.44 0.04 0.29 -0.46 -0.33 -0.01 0.16 0.12 0.13 1 0.29 0.23 -0.35 -0.12 -0.13 0.32 0.29 0.11 1 -0.01 -0.12 0.01 -0.03 0.33 0.05 0.43 1 -0.54 -0.24 0.07 -0.21 -0.20 -0.13 1 0.46 -0.07 -0.08 0.22 0.01 1 0.01 0.10 -0.16 -0.19 1 0.18 0.18 0.15 1 0.29 0.37 ELE 1 0.28 1 Table 4. Correlation Analysis EXT AGE Coefficient 0.08 Std.Dev. 0.05 z 1.59 P>|z| 0.11 SIZE 0.00 0.01 -0.68 0.50 DVSF 3.49 1.64 2.14 0.03*** EXPRT 2.36 1.45 1.63 0.10 MES 0.00 0.05 -0.03 0.98 IGRWTH 0.26 0.12 2.08 0.04*** LCT -3.84 2.05 -1.87 0.06** RD -0.73 1.42 -0.52 0.61 PTNT -1.15 0.60 -1.93 0.05** INNO 1.23 1.22 1.01 0.31 ELE 0.06 0.10 0.64 0.52 Constant -2.46 3.69 -0.67 0.51 Significance Level: *=%10, **=%5, ***=%1 Number of obs = 60 Pseudo R2 = 0.4877 LR chi2(11) = 29.29 Log likelihood = -15.381425 Prob > chi2 = 0.0020 Table 5. Factors Affecting Firms‘ Survival Effects of variables on sırvival probabilities of firms are respresented in Table 5. Accordingly, DVSF, IGRWTH, LCT and PTNT variables has significant effects. The mos striking result is that PTNT variable has significant and negative effect. But, only four firms has patent application and even more these firms has exited. ThereforePTNT is not a robust variable with which we could observe the concrete effects of the patent ectivities of the firms on their survival opportunities. Conclusion There are a lot of factors which affect the performance of the companies. The majority of factors are stochastic such as war, natural disasters, change of government, fluctuations in the stock market etc. In this study, the systematic factors which are thought as having an impact in the process of firms survival and the direction of these affects are investigated. The factors including firm, industry, innovation and human capital that affect the performance of firms operating in Lake District are identified by econometric analysis. The results obtaining from emprical analysis clearly demonstrate that new and small-scale firms‘ importance. The results show that generally firms in the region continue their activities, and grow under the 215 �2nd International Symposium on Sustainable Development, June 8-9 2010, Sarajevo traditional factors. Survival results related to literature do not show very large differences. Source of the differences is the variables which do not show significance in the Lake District sample set. Generally, opening branches for going to path differentiation are usually carried out by large firms. However, a large part of the firms going to path differentiation in Lake Region are consisting of small and medium sized firms. This case is one of the important difference that shows small and junior firms in the region perform better. References Acs, Z.J., & Armington, C. (2006). Entrepreneurship, Geography and American Economic Growth. Cambridge University Press, Cambridge, p.45. Agarwal,R., & Gort, M. Firm and Industry Attributes as Determinants of Survival. JEL:O30, L20, p.2. Bercovitz,J., & Mitchell, W. (2007) When is More Better? The Impact of Business Sale and Scope on Long Term Business Survival, While Controlling for Profitability. Strategic Management Journal, Vol.28, 61-79. Geroski,P., (1995). What Do We Know about Entry?. International Journal of Industrial Organisation, Vol. 13, 421-440. Gimeno J., T.B. Folta, A.C. Cooper, & Woo, C.Y. (1997) Survival of the Fittest? Entrepreneurial Human Capital and the Persistence of Underperforming Firms. Administrative Science Quarterly, 42 (4), p.756. Nelson T., (2003). The Persistence of Founder Influence: Management, Ownership and Performance Effects at Initial Public Offering. Strategic Management Journal, Vol. 24, p.707. Pe‘er, A., & Vertinsky, I. (2006). The Survival Value of Clusters for De Novo Entrants. Academy of Management Best Conference Paper, 2006, BPS:N1, p.1. Perez,S.E., A.S. Llopis, & Llopis, J.A.S. (2004) The Determinants of Survival of Spanish Manufacturing Firms. Review of Industrial Organization, Vol.25, p.251. Santarelli E., & Vivarelli, M. Entrepreneurship and the Process of Firms‘ Entry, Survival and Growth. Industrial and Corporate Change, 16 (3), 461-463. Stinchcombe, A. L., (1965) Social Structure and Organizations. in J. G. March, ed., Handbook of Organizations. Chicago: Rand-McNally, 142-193. Van Praag, C.M. (2002). Business Survival and Success of Young Small Business Owners. Small Business Economics, 21 (1), p.3. 216 � 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. 159 Title A name given to the resource Determinants of Firm Survival in Manufacturing Industry: A Research on Lake Region in Turkey Author Author DEMİRGİL, Hakan KARAÖZ, Murat GÖVDERE, Bekir CAN, Muhlis Abstract A summary of the resource. This paper gives an empirical analysis of determinants of firm survival in the manufacturing sector of Lakes Region (Turkey) from 2003 to 2009. The survival activities of the firms are measured through a four-dimensional, namely firm based, industry/environment based, innovation based and human capital based, questionnaire consisting of 49 questions. For the application logistic regression method is used for the evaluation of survival probability and the findings are compared with the basics of the related literature. According to the results of the empirical work, the effects of the ―Firm Based‖ factors on the survivals and growth performances of the firms located in the region are found to be more significant and positive than the remaining dimensions. 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 https://omeka.ibu.edu.ba/files/original/38b2bcec896ec2e96b6772379ae18849.pdf 64236b52013013320d8a0036def1cabe PDF Text Text Journal of Economic and Social Studies Determinants of Foreign Direct Investment: An Empirical Analysis for Turkey Huseyin Kalyoncu Meliksah University Turkey hkalyoncu@meliksah.edu.tr Nadide Tuluce Meliksah University Turkey ntuluce@meliksah.edu.tr Zeynep Ozturk Yaprak Meliksah University Turkey zyaprak@meliksah.edu.tr Abstract: This paper aims to investigate empirically the determinants of FDI for Turkey over the annual period of 1975-2012. Our main interest is to study how different reflecting inflows of FDI in Turkey are. This study examines time series data evidence concerning empirical relevance between FDI attraction and its determinative effects. As a definition, FDI is a direct investment into production or business in a country by an individual or company of another country, either by buying a company in the target country or by expanding operations of an existing business in that country. Unit root and Johansen cointegration tests are used in order to analyze the determinants of FDI for Turkey. Our econometric model expresses foreign direct investment (FDI), as a function of market size (GDP), openness (OPEN) calculated as Export + Import/ GDP, inflation rate (CPI), energy production (EP), labor productivity (LABOR). The major results show that there is a positive effect of GDP, OPEN, EP and LABOR on FDI. But CPI’s effect on FDI is negative in the long run. Volume 5 Number 2 Fall 2015 Keywords: FDI; Time Series; Cointegration JEL Classification: F21, C22 Article History Submitted: 19 November 2014 Resubmitted: 1 July 2015 Resubmitted: 29 July 2015 Resubmitted: 8 September 2015 Accepted: 9 September 2015 http://dx.doi.org/10.14706/JECOSS15524 41 �Huseyin Kalyoncu, Nadide Tuluce, Zeynep Ozturk Yaprak Introduction Economic development of a country depends on utilization of resources for increasing productive capacity. In many developing countries, utilization of resources is rendered impossible by the scarcity of domestic capital. One of these economic problems of developing countries is that they do not have enough national savings to finance their investments. They are in constant need of foreign capital in forms of both direct and indirect investments. Foreign direct investment (FDI) is a process whereby the residents of the source country attain ownership of assets with the intention to control the production, distribution and other activities of a firm in the host country (Khachoo and Khan,2012). Foreign direct investment (FDI) is a way of international loan, by which those countries that have better investment opportunities at the present borrow from those that have capital surplus. FDI can be a crucial instrument to foster economic growth. FDI provides developing countries with the much needed capital for investments and enhances job creation, managerial skills and transfer of technology for less developed countries. Furthermore, FDI encourages technological development and also support the accumulation of physical capital. FDI plays a significant role in the development of international trade, and it helps to establish direct, stable, and long-lasting links between economies. The Organization for Economic Co-operation and Development (OECD) states that; FDI can serve as an important vehicle for local enterprise development, strengthening the competitiveness of both the recipient and investor (Groh and Wich, 2012). For example, Turkey in particular is pursuing further political and monetary integration with Europe. In that case maintaining a government effectiveness that is conducive to foreign investment and increases comparative advantage is integral to its integrationist aspirations. The significance of foreign direct investment (FDI) flows is well documented in literature for both the developing and developed countries. Foreign Direct Investment (FDI) inflows to developing countries have been substantially increasing and, compared to other capital flows, have remained the largest component of net resource flows to developing countries. FDI is a key element in international economic integration. FDI creates direct, stable and long-lasting links between economies. As a definition FDI is a direct investment into production or business in a country by an individual or company of another country, either by buying a company in the target country or by expanding operations of an existing business in that country. It encourages the transfer of technology and know-how between countries, and allows the host economy to promote its products more widely in international markets (Todaro, 1994). The role of foreign direct investment in the development of Turkish economy cannot be over emphasized. Foreign direct investment provides capital for investment; it enhances job creation and managerial skills, and possibly technology transfer. 42 Journal of Economic and Social Studies �Determinants of Foreign Direct Investment: An Empirical Analysis for Turkey We shall present our analysis with a brief history of the Turkish economy. Today, Turkey is one of the most attractive investment destinations for foreign investors. It benefits from a unique strategic location; a young, dynamic and skilled workforce, and a stable political and economic environment. Turkey received foreign investment inflows of only US$18m 33 years ago when it started to host foreign investors. Now, the cumulative value of foreign investments has surged to US$138.3b. While the aggregate volume of foreign investment inflows totalled only US$14.6b during the 80-year period from the establishment of the Turkish Republic to 2003, this figure rose to US$123.7b during the last decade. In other words, Turkey attracted 8.5 times more foreign inward investment over the last decade than it did in the previous 80 years. Turkey now plays a significant role in the global economy and world trade, standing out as a promising emerging market alongside Brazil, Russia, India and China. This status is underpinned by its robust local market and young population. Despite the global economic crisis and the political and social issues that have afflicted neighbouring regions, Turkey exported more goods in 2012 than ever before. Total exports valued at US$152.6b were supplied to 241 countries and regions worldwide. The well-trained and loyal workforce played a notable role in achieving this success. Turkey offers another layer of opportunity by serving as a frontier to other regions. Figure 1 shows the total amount of FDI inflows to Turkey in US Dollar at current prices and current exchange rates in millions. FDI flows to Turkey have been increased largely after 2004. FDI flows into Turkey fell in 2009 due to the global crisis similar to most developed and developing countries. After then FDI started to increase again. Figure 1. FDI Inflows to Turkey (US dollars in millions) Source: Central Bank of the Republic of Turkey, own construction Volume 5 Number 2 Fall 2015 43 �Huseyin Kalyoncu, Nadide Tuluce, Zeynep Ozturk Yaprak Table 1. FDI Inflow to Turkey by Year (USD million) 2010 2011 2012 2013 2014 FDI Total (Net) 9,099 16,176 13,282 12,457 12,530 Equity Investments (Net) 6,221 14,146 10,126 9,298 8,445 Inflows 6,256 16,137 10,759 9,866 8,699 Liquidation Outflows 35 1,991 633 568 254 Intra -company Loans* 384 17 520 110 -236 Real Estate (Net) 2,494 2,013 2,636 3,049 4,321 *Loans of companies with foreign capital are given by foreign partners (www.tcmb. gov.tr) Source: Central Bank of the Republic of Turkey (TCMB), Electronic Data Delivery System, Outstanding External Debt and Balance Of Payments Statistics According to the UNCTAD 2014 World Investment Report, Turkey has become the largest recipient of FDI in West Asia, and is among the fifteen most promising investors for 2014-2016. The country has adopted a series of legislative reforms to facilitate the reception of foreign investment, such as the creation of Investment Support and Promotion Agency of Turkey (ISPAT), a showcase effort undertaken to attract foreign operators. FDI inflows improved in light of the development of publicprivate partnerships for major infrastructure projects, the measures to streamline administrative procedures and strengthened intellectual property protection, the end of FDI screening and the structural reforms carried out with a view to the future accession into the EU. In 2014, Turkey announced a major national infrastructure development plan that should attract major foreign investment. In 2014, the joint venture of Koc Holding (Turkey) and Fiat (Italy) invested USD 300 million in developing automobile production. Also, a number of Chinese companies have invested up to USD 385 million in the electricity distribution company OEDAS. Finally, a Japanese group has invested USD 500 million in a steelworks plant in collaboration with a Turkish company. The countries of the European Union, the Gulf States and the United States are among the main investors in Turkey. The business climate deteriorated in 2014 according to the Doing Business report of the World Bank, the country losing 4 places (55th out of 189 countries). However, FDI amounted to USD 12.5 billion in 2014, an increase on 2013 (Central Bank of the Republic of Turkey, 2014). There exists vast literature on determinants and effects of FDI. The issue has increased in importance due to strong globalization processes. Many developed and developing countries try to attract FDI to support their economic growth and development. Dunning’s eclectic paradigm (1993) was initiating the investigation of the locational advantages of the host countries e.g., income levels, market size, skills, infrastructure, political and macroeconomic stability that determines cross-country pattern of FDI. The determinants of FDI have been analyzed in the literature in many studies. Numerous theoretical and empirical studies (Agarwal, 1980; Brainard, 1993;1997; Gastanaga et. al., 1998; Ekholm, 1998; Zhang and Markusen, 1999; Barros and 44 Journal of Economic and Social Studies �Determinants of Foreign Direct Investment: An Empirical Analysis for Turkey Cabral, 2001; Chakrabarti, 2001; Moosa, 2002) on the determinants of FDI lead us to select a set of explanatory variables that are widely used and found to be significant determinants of FDI. For example Markusen and Maskus (1999), Love and LageHidalgo (2000), Lipsey (2000), Lim (2001), and Moosa (2002), Asiedu (2006), Cleeve (2008), Mhlanga et al. (2010), Vijayakumar et al. (2010), Wang and Swain (1995), Liu (1997), Dees (1998) and Cheng and Kwan (1999) highlight how the domestic market size can relate to the location of FDI. Mainardi (1992) emphasizes the level of importance and growth prospectus of the real per capita GDP in taking investment decisions in a region. Lunn (1980), Schneider and Frey (1985), Culem (1988), Cheng and Gastanaga (2001) and Cleeve (2008), Mohammed and Sidiropoulos (2010) discuss the positive effects of GDP growth rate proxy of market growth. To foreign investors who operate in industries characterized by relatively large economies of scale, the importance of the market size and its growth is magnified. This is because they can exploit scale economies only after the market attains a certain threshold size. The most widely used measures of market size are GDP, GDP/capita and growth in GDP. The coefficients are usually positive. One of the determinants of FDI is labour cost. Labour cost is one of the major components of the cost function, it is mentioned that high nominal wage, other things being equal, deters FDI. This is true for labour-intensive production sectors. Therefore, conventionally, the expected sign for this variable is negative. The studies that find no significant or a negative relationship of wage and FDI are: Goldsbrough (1979), Saunders (1982), Kravis and Lipsey (1982), Flamm (1984), Wheeler and Mody, (1990), Sader (1993), Lucas (1993), Tsai (1994), Wang and Swain (1995), Barrell and Pain (1996), Cheng and Kwan (1999) and Botric and Skuflic (2006) sign that lower wages attract FDI positively. Nonetheless, there are other researchers who have found out that higher wages do not always deter FDI in all industries and have shown a positive relationship between labour costs and FDI (Moore, 1993; and Love and LaveHidalgo, 2000). Because higher wages indicate higher productivity, hi-tech research oriented industries in which the quality of labour matters, prefer high-quality labour to cheap labour with low productivity. Coughlin and Segev (2000) and Cheng and Gastanaga (2001), by the OLS method, indicate that labour productivities and rate of capable labours have positive impact on FDI. Recently, a few researchers have also studied the impact of specific policy variables on FDI in host countries. One of these policy variables is openness of trade. Gastanaga, Nugent, and Pashamova (1998) and Asiedu (2002) focus on policy reforms in developing countries as determinants of foreign direct investment inflows. They find corporate tax rates and degree of openness to foreign direct investment to be significant determinants of FDI. Kravis and Lipsey (1982), Culem (1988), Edwards (1990), Sun (2002), Kuo and Huang (2003), Asiedu (2006), Cleeve (2008), Mhlanga et al. (2010) find significant positive effects on FDI also. Schmitz and Bieri (1972) and Wheeler and Mody find insignificant effects of openness on FDI. Volume 5 Number 2 Fall 2015 45 �Huseyin Kalyoncu, Nadide Tuluce, Zeynep Ozturk Yaprak For foreign investors, economic stability of home country is very important. The economic stability conditions affect the profitability of investment projects. Therefore, foreign investors seek countries which have economic stability. One of the economic stability proxies is inflation rate. Low inflation policies are often offered to multinationals as an incentive to attract FDI inflows. Empirical studies (Schneider and Frey, 1985; Asiedu, 2006; Mohammed and Sidiropoulos, 2010) indicated a negative relationship between inflation and FDI. Likewise the effect of infrastructure on FDI flows is a fairly well-studied topic although the direction and magnitude of influence is generally positive. Biswas (2002) claimed that quality of infrastructure should increase FDI into the host country. He used phone lines per 1000 inhabitants for proxy of infrastructure. Similarly, Vijayakumar et al. (2010) also acknowledge that infrastructure index effects FDI positively. Looking at the fundamental determinants, Markusen (2002) argues that there are two factors that turn out to be crucial for the existence of horizontal FDI: the size of the local markets and the marginal production cost in the case of producing directly in the host market. The first factor is evident: firms invest abroad to serve the local host market. Therefore, the size of the local demand (known also as market size or market potential) will be a determinant for the firm’s investment decision. The second factor, the level of local production costs, will determine whether the firm produces locally to sell locally or it supplies the host market by exporting its home-based production. A large number of studies have been conducted to identify the determinants of FDI but no consensus view has emerged in the sense that there is now idely accepted set of explanatory variables that can be regarded as the “true” determinants of FDI. Chakrabarti (2001) attributes the lack of consensus to“the wide differences in perspectives, methodologies, sample-selection and analytical tools”. Research on FDI has been one of the most crucial areas of international economics. Although there is sizeable research on the determinants of FDI, empirical studies on FDI in developing countries such as Turkey are relatively scarce. This study is important because Turkey had experienced declining and fluctuating foreign investment inflows. Besides, Turkey alone cannot provide all the funds needed to invest in various sectors of the economy. The objective of this study, therefore, is to identify the long run relationship between FDI and some macroeconomic variables. To accomplish this purpose, in this work we have reviewed empirical evidence on the relationship between FDI and other economic variables. This study has modelled FDI with macroeconomic variables in Turkey. The objective of this paper has been to focus solely on the relationship between FDI inflows and macroeconomic variables for Turkey. Cointegration technique, proposed by Engle and Granger (1987) and extended by Johansen (1988), has been applied to evaluate the long-run hypothesis that our variables are cointegrated. The basic idea is that individual time series wander considerably but economic forces tend to make these series stationary. Given the basic economic model, FDI has been hypothesized to be cointegrated with the economic growth, openness, inflation and electricity consumption. 46 Journal of Economic and Social Studies �Determinants of Foreign Direct Investment: An Empirical Analysis for Turkey Finally, we employed cointegration approach to determine the long-run factors contributing to FDI in Turkey. It is important to use this approach in our cointegration test as, during the sample period, the Turkish economy has been subject to serious economic developments. This paper is organized as follows: Section 2 begins by illustrating the inherently multivariate nature of cointegration analysis: several variables must be involved, and this determines the form of the statistical tools required. Section 3 explains econometric methodology. Section 4 presents empirical result. Section 5 concludes. Model Specification and Data The determinants of FDI have been analyzed in the literature in many studies using different models. In order to investigate empirically, the determinants of FDI for Turkey following empirical models were used: FDI t = β 0 + β1GDPt + β 2 OPEN t + β 3 CPI t + β 4 E P t + u t (1) FDI t = β 0 + β1GDPt + β 2 OPEN t + β 3 CPI t + β 4 LABORt + u t (2) where t denotes time, and the variables are defined as: • • • • • • FDI denotes the net foreign direct investments in flows as % of GDP, GDP is gross domestic product (US$) (proxy of market size) , OPEN is openness index (total trade -export+ import / GDP), CPI is consumer price index (annual % - proxy of inflation-as an indicator of macro economic instability) EP is electricity production (kWh-proxy of availability of infrastructures) LABOR is labour productivity (real output divided by total labour input) The data obtained from the World Development Indicators is in yearly format and spans a period of 1975-2012 except labour productivity data. This data has been taken from the OECD online database. The time span allows us to use 38 observations for our time series analysis. E Views 8 is used for all estimations. All data are expressed in real terms. As a first step, we estimate a VAR system for Turkey. We use the Schwarz’ Information Criteria statistics to choose the lag-length. As a general check of our specification we always checked whether the residuals follow a normal distribution, and whether there is any heteroscedasticity or serial correlation. We moved to the cointegration test only after the residuals were homoscedastic and normally distributed. As for the form of the cointegration vector, we preferred to assume that our data is difference stationary and there is no linear deterministic trend in our data. Volume 5 Number 2 Fall 2015 47 �Huseyin Kalyoncu, Nadide Tuluce, Zeynep Ozturk Yaprak Methodology By bridging the gap between domestic savings and investment and bringing the latest technology and management know-how from developed countries, foreign direct investment (FDI) can play an important role in achieving rapid economic growth in developing countries (Mottaleb and Kalirajan, 2010). To shed light on the potential drivers of FDI to Turkey, we perform cointegration methods. Before modelling the data, we consider its basic stationary properties. The preliminary step of our analysis is to check the time series variables are stationary or non-stationary. Most of the time series data generally have trend, cycle, and/or seasonality. By removing these deterministic patterns, the remaining series must be stationary. Therefore, a test of the null hypothesis of non-stationarity is conducted via the well-known Dickey-Fuller procedure. Stationarity in a time series implies to a condition where the series has a constant mean and constant variance. This implies that the mean and variance of stationary time series do not vary over time. We first study the stationarity property of the time-series variables used in the study. The first step in statistical testing the non-stationarity of time series data is to test for random walk. Testing this means to find out whether the variables contain unit root. This is also called the Unit Roots Test. As discussed earlier using the non-stationery series in estimating relations may give spurious results. In case the first difference is stationary (has no unit root) then the series is described having integration of order 1 and is denoted I(1). If two time series are integrated of order or I(1), it is well known that the correlation coefficient between them will tend towards plus or minus unity, whether an economic relationship between them exists or not. One important property of variables having I(1) property is that their linear combination can be I(0). This means the linear combination non-stationary series of I(1) can be stationary. These variables are described as cointegrated variables. A necessary condition for testing for a long-run relationship between variables is that these variables are I(1), i.e., stationary in first differences. We, therefore, use the classical unit root tests, namely, the Augmented Dickey-Fuller (ADF) test (Dickey and Fuller, 1981; Said and Dickey, 1984). ADF test is based on the null hypothesis that a unit root exists in the time series. The null hypothesis is that the variables in question contain unit root and the alternative hypothesis is that the variables are trend stationary. The ADF statistics suggests that all variables are I (1). To determine whether a long-run relationship exists foreign direct investment, economic growth, openness, inflation rate and electricity production are considered. We must not only test whether both variables are integrated of the same order, but we need to find evidence for a cointegration. Here we apply the JJ (1990) procedure to test for the presence of a cointegration. 48 Journal of Economic and Social Studies �Determinants of Foreign Direct Investment: An Empirical Analysis for Turkey Once it is established that series are I(1), we can proceed to test for a long-run relationship between the series. If such a relationship exists, series are cointegrated. To achieve this, we start out with the vector auto regression approach of Johansen (1988) and Johansen and Juselius (1990). In the JJ method, two tests are used to determine the number of cointegrating vectors (r): the trace test and the maximum eigenvalue test. In the trace test, the null hypothesis is that the number of cointegrating vectors is less than or equal to r, where r is 0, 1, or 2. In each case, the null hypothesis is tested against a general alternative. In the maximum eigenvalue test, the null hypothesis r = 0 is tested against the alternative that r = 1, r = 1 against the alternative r = 2, etc. Empirical Results We first perform unit root tests in levels and first differences in order to determine univariate properties of the series used in this study. We, therefore, use the classical unit root tests, namely, the Augmented Dickey-Fuller (ADF) test. The ADF test is based on the null hypothesis that a unit root exists in the time series. The null hypothesis is H0: φ = 0 and the alternative hypothesis is H1: φ ≠ 0. First order integrated series can present stationary linear combinations (I(0)). In these cases, we say variables are cointegrated. It means there is a long-run equilibrium linking the series, generating a kind of coordinated movement over time. In the light of econometric setting presented in the previous section, the empirical results are discussed in this section. The analysis is started by the test of the stationarity properties of the data series. This is the prime requirement for cointegration causality test. The results are presented in Table 1. It is evident from the table that the calculated ADF statistics are less than their critical values in all cases, suggesting that the variables are not level stationary. The results indicate that for Turkey, all the variables are non-stationary in their levels but stationary in their first differences. This means that we can proceed with the Johansen cointegration tests for these countries. However, they are stationary in their first differences. The values in brackets indicate the lag structure in ADF. The Schwarz’s Information Criterion (SIC) was used to determine the number of lags for the cointegration tests. These results indicate that the cointegrating technique has to be applied in order to analyse the long-run relationship between these variables. Johansen and Juselius (JJ) (1990) cointegrating method is utilized for this purpose. Volume 5 Number 2 Fall 2015 49 �Huseyin Kalyoncu, Nadide Tuluce, Zeynep Ozturk Yaprak Table 2. Unit Root Test Results Series ττ Level First Difference τµ τµ τµ ττ τµ FDI -0.548250 (0) 2.915183 (0) -5.352229 (5)* -3.912278 (0)* GDP -1.170291 (0) 1.763019 (0) -5.987356 (0)* -5.330377 (0)* OPEN -2.763083 (0) -1.052409 (0) -5.417465 (0)* -5.497118 (0)* CPI -2.390373 (0) -1.973490 (0) -7.225054 (0)* -7.080614 (0)* LABOR -2.441679 (6) -2.449072 (6) -7.004778 (1)* -7.127342 (1)* EP -2.301159 (0) 0.545275 (0) -6.302344 (0)* -6.109945 (0)* Source: Author’s own calculations. Note: The t statistics refer to the ADF tests. The subscripts μ and τ indicates the models that allow for a drift term and both a drift and a deterministic trend, respectively. Asterisk (*), shows significance at 5% level. Figures in parentheses indicate the lag length. The critical values are obtained from MacKinnon (1991) for the ADF test. ADF test examines the null hypothesis of a unit root against the stationary alternative. The Johansen cointegration test identified cointegrating relationship between FDI inflows and explanatory variables. To find which variables adjust to the long run cointegrating relations, we focus on cointegration in the Vector Autoregressive model (VAR). The VAR model will provide a feasible empirical system for the analysis of our integrated economic time series. Before undertaking cointegration tests, let us first specify the relevant order of lags (p) of the vector autoregression (VAR) model. The Schwarz’s information criterion (SIC) is used to determine the optimal lag length. The SIC criterion yield a VAR (3) for two models. Having confirmed the existence of unit roots for all the data series, the next step is to check possibility of long run equilibrium relationship between them. The cointegration test is applied for the same at the individual level as well as panel level. The Johansen’s maximum likelihood test has been applied. The estimated results of Johansen’s test are reported in Table 2. The results from the trace and max-eigenvalue test are reported in the tables below together with the normalized cointegration vector: 50 Journal of Economic and Social Studies �Determinants of Foreign Direct Investment: An Empirical Analysis for Turkey Table 3. Johansen-Juselius Maximum Likelihood Cointegration Tests Model I: FDI t = β 0 + β 1GDPt + β 2 OPEN t + β 3 CPI t + β 4 E P Trace Test t + ut Maximum Eigenvalue Test Null Alternative Statistic 95 % Critical Value Null Alternative Statistic 95 % Critical Value r=0 r≥1 136.02 69.81 r=0 r=1 53.05 33.87 r≤1 r≥2 82.96 47.85 r≤1 r=2 40.41 27.58 r≤2 r≥3 42.55 29.79 r≤2 r=3 24.90 21.13 r≤3 r≥4 17.65 15.49 r≤3 r=4 17.63 14.26 r≤4 r≥5 0.012 3.84 r≤4 r=5 0.01 3.84 Source: Author’s own calculations. Model II: FDI t = β 0 + β 1GDPt + β 2 OPEN t + β 3 CPI t + β 4 LABORt + u t Trace Test Maximum Eigenvalue Test Null Alternative Statistic 95 % Critical Value Null Alternative Statistic 95 % Critical Value r=0 r≥1 176.0855 69.81889 r=0 r=1 82.56708 33.87687 r≤1 r≥2 93.51844 47.85613 r≤1 r=2 47.27528 27.58434 r≤2 r≥3 46.24316 29.79707 r≤2 r=3 26.15473 21.13162 r≤3 r≥4 20.08843 15.49471 r≤3 r=4 18.41811 14.26460 r≤4 r≥5 1.670316 3.841466 r≤4 r=5 1.670316 3.841466 Source: Author’s own calculations. Notes: We have employed the Schwarz’s information criterion (SIC), in the determination of lag length in the VAR model. The cointegration tests confirm our initial hypothesis regarding the long-run relationship between FDI and other variables. For the first model, the implementation of the JJ procedure indicates that there is a long-run equilibrium relationship among Volume 5 Number 2 Fall 2015 51 �Huseyin Kalyoncu, Nadide Tuluce, Zeynep Ozturk Yaprak FDI, GDP, OPEN, CPI and EP. Also there is a long-run equilibrium for the model two. Table 4. Estimates of Long-Run Cointegrating Vectors FDI GDP OPEN CPI EP 1.000 732,5 8.66E-10 -73,96 0,265 (243,1) (1,6E-09) (33,8) (0,57) FDI GDP OPEN CPI LABOR 1.000 624.19 1.30E-09 -45.68 5.74 (6.9E-10) (26.12) (20.92) (128.36) Source: Author’s own calculations. Note: Numbers in parentheses are standard errors. Estimates of long-run cointegrating vectors are given in Table 8. Our econometric estimates of FDI functions for Turkey suggest that GDP as a proxy of market size related to location of FDI and most effective determinants in model one and two. This means that foreign investors prefer big market size because of scale economies. There is a positive relationship between openness and FDI but this is a small relationship in case of Turkey. There is a negative relationship between FDI and CPI. For foreign investors, home country’s economic stability is important. As CPI is the used proxy of economic stability, results of models are as expected. Energy production is used as a proxy of infrastructure and we found that EP affects FDI positively. Labour productivity is related to FDI positively. High labour productivity means low labour cost. Low labour cost attracts FDI for labour-intensive production sectors. Labour productivities and rate of capable labours have positive impact on FDI. Conclusion During the past ten years we have seen a tremendous growth of foreign direct investment. Further economic development of Turkey depends to a large extent on continuous FDI and policy-making that will facilitate inward investment. The modelling strategy adopted in this study involves two steps: • determining the order of integration of the variables by employing unit-root tests; • if the variables are integrated in the same order, we apply the Johansen – Juselius(1990, 1992, 1994) maximum likelihood method of cointegration 3 to obtain the number of cointegrating vector(s). The long-run relationship between FDI, GDP, OPEN, CPI, EP and LABOR is tested 52 Journal of Economic and Social Studies �Determinants of Foreign Direct Investment: An Empirical Analysis for Turkey by conducting cointegration test over the period 1975 to 2012.In the first place, the intention of the study is to examine the long-run linear relationship between FDI and explanatory variables. The empirical results at the first phase proved that none of the series is stationary and has to be differenced in order to convert the series into stationary. All these series are statistically significant at first difference order and are integrated in the same order. The next test of cointegration established that the FDI inflows are said to have long run linear relationship with GDP, openness, consumer price index, electricity production and labour productivity. Based on the cointegration analysis, stability of these macroeconomic variables will expectedly attract more FDI into Turkey for sustainable economic growth. The above-findings have important policy implications. Firstly, since the market size of the host country has significant effect on FDI, there is need for continuous increase and growth of the nation’s Gross Domestic Product. Secondly, the major results show that there is a positive effect of market size, openness, energy production and labour productivity on FDI. But CPI as a proxy of market stability affects FDI negatively in the long run. At the same time, we provide evidence that is complementary to Açıkalın, Gülve Yaşar (2006), as well as Düzgün (2007) in one important respect. These empirical findings have important key policy implications for Turkish economy. FDI inflows of Turkey can be used to predict the decisions of foreign residents who want to invest in this host economy in the long run with these empirical findings. The scope of this study could be much broader in terms of analyzing the effect of differences in FDI inflows, combined market size, openness, consumer price index, electricity production and labour productivity. This would perhaps give a much broader and clear picture of the determinants of FDI inflows to Turkey. There are many other questions that we should take into consideration in further development of this study. However, it is worth mentioning that the determinants of the FDI and effects on growth in the cointegration framework seem to offer new suggestions for future research. 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Gastanaga, V. M., Nugent, J.B., & Pashamova B. (1998). Host country reforms and FDI inflows: how much difference do they make? World Development, 26(7), 12991314. Goldsbrough, D.G. (1979). The role of foreign direct investment in the external adjustment process. International Monetary Fund Staff Papers, 26,725-754. Groh, A. P., & Wich, M. (2012). Emerging economies’ attraction of foreign direct investment. Emerging Markets Review, 13(2), 210-229. Huang, J. T., Kuo, C. C., & Kao, A. P. (2003).The inequality of regional economic development in China between 1991 and 2001. Journal of Chinese Economic and Business Studies, 1(3), 273-285. Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration with applications to the demand for money. Oxford Bulletin of Economics and statistics, 52(2), 169-210. Khachoo, A. Q., & Khan, M. I. (2012). Determinants of FDI inflows to developing countries: a panel data analysis. MPRA Paper No. 37278 Kravis, I. 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TCMB (Central Bank of the Republic of Turkey), (2014).Outstanding External Debt and Balance Of Payments Statistics. Electronic Data Delivery System, RetrievedMay 7, 2014, from: http://www.tcmb.gov.tr/wps/wcm/connect/TCMB+EN/TCMB+EN/Main+Menu/ STATISTICS/Statistical+Data+%28EDDS%29 Todaro, M.P. (1994). Economic Development (5th Edition). New York and London: Longman. 56 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. 2927 Title A name given to the resource Determinants of Foreign Direct Investment: An Empirical Analysis for Turkey Author Author KALYONCU, Hüseyin TULUCE, Nadide YAPRAK, Zeynep Ozturk Abstract A summary of the resource. Abstract: This paper aims to investigate empirically the determinants of FDI for Turkey over the annual period of 1975-2012. Our main interest is to study how different reflecting inflows of FDI in Turkey are. This study examines time series data evidence concerning empirical relevance between FDI attraction and its determinative effects. As a definition, FDI is a direct investment into production or business in a country by an individual or company of another country, either by buying a company in the target country or by expanding operations of an existing business in that country. Unit root and Johansen cointegration tests are used in order to analyze the determinants of FDI for Turkey. Our econometric model expresses foreign direct investment (FDI), as a function of market size (GDP), openness (OPEN) calculated as Export + Import/ GDP, inflation rate (CPI), energy production (EP), labor productivity (LABOR). The major results show that there is a positive effect of GDP, OPEN, EP and LABOR on FDI. But CPI’s effect on FDI is negative in the long run. 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 2015 Keywords Keywords. Article PeerReviewed HB Economic Theory https://omeka.ibu.edu.ba/files/original/49c1a0dbe8e68725026af0cea5d546a3.pdf 7283c2aa89c62de29776de4f3d63c6ec 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. 3754 Title A name given to the resource Determinants of the Financing Obstacles Faced by SMEs: An Empirical Study of Emerging Economies Author Author Nizaeva, Mirgul Coskun, Ali Abstract A summary of the resource. Ab stract: Small and medium sized enterprises (SMEs) play a crucial role in the economic development of emerging countries. The lack of access to finances is one of the important growth constraints the SMEs face. This study investigates the firm and country specific determinants of the financial constraint levels of SMEs in selected emerging Western Balkan economies. The main determinants of the financing obstacles examined in the sampled countries were: firm size, ownership type, and age, accounting information transparency, the depth of credit information indexes, the banking sector concentration, property registration costs; and per capita GDP. The findings confirm that firm size is a significant determinant of the financial constraint levels of SMEs in the selected economies. Moreover, we found that older firms are financially more constrained in the region. The possible economic implications of the positive association between firm age and financial constraint are discussed. Banking sector concentration level plays crucial role in the external financing of SMEs in developing countries. By closely examining the firm characteristics and country-level factors that determine the degree of the financing obstacles faced by SMEs, we observed that in developing economies overall institutional and financial problems are more important than firm-specific 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 2018 Keywords Keywords. Article PeerReviewed DOI Digital object identifier doi: 10.14706/JECOSS17725 Identifier An unambiguous reference to the resource within a given context ISSN 1986-8499, H Social Sciences (General) https://omeka.ibu.edu.ba/files/original/faa640e764753698cc4d74d08038866d.pdf 47c90557b50c306b35a474516070933b PDF Text Text 3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Yavuz, G. and Serdaroğlu U. ( 2010) “Kalkınma ve Kadın (veya toplumsal cinsiyet) İlişkilendirilişinin Değişimindeki Kavşaklar” in U. Serdaroğlu (ed.) İktisat ve Toplumsal Cinsiyet, Efil Yayınevi, Ankara. Yumuş, A. (2011) Kalkınma Planları Çerçevesinde Toplumsal Cinsiyet Eşitliği Anlayışının Ekonomik, Toplumsal ve Siyasal Boyutları, T.C. Başbakanlık Kadının Statüsü Genel Müdürlüğü Yayınları, Ankara. Determinants Of Turkey Current Account Deficit: An Econometric Analysis M. Metin Dam, İsmet Göçer,Şahin Bulut,Mehmet Mercan Adnan Menderes University, Faculty of Economic and Administrative Sciences Department of Economy Abstract The main causes of the current account deficit in Turkey; the foreign trade deficit, the high ratio of intermediate goods imports, high oil prices and Turkey's energy import dependence, lack of domestic savings, foreign direct investment and low tourism revenues. In this study, the causes of the current account deficit and current account deficit financing structure were examined. In addition, the determinanats of Turkey current account deficit wereanalyzed via VAR methods using the data of 2002-2011 monthly current account deficit, net export, interest on external debt, transfer payments and costs of tourism. As a result of the study, According to variance discrimination results obtained from VAR model composed under this roof, current account deficit is determined by its own shocks in the short term. In addition, current account deficit prediction error variance is determined by tourism expenditures and foreign debt interest rate as well as its own variables. Current account deficit is affected by export, foreign debt interest rate, transfer payments and shock given to tourism expenditures. Keywords: Current Account Deficit, Determinants, VAR, Turkey 1.INTRODUCTION 1.1.What is current account deficit? Current account deficit is the difference between the amount of foreign currency getting in and out a country. Export and tourism make up foreign currency income and import and foreign expenditure make up foreign currency expenditure. Current account deficit is reached: the foreign currency obtained from goods export, service export like tourism(e.g the wage income of those working abroad) and manufacture factors are added and the expenditures made in the same category (import, tourism expenditures, the transfer of the profit gained by 111 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo foreigners) are subtracted from total. İf the figures obtained show a value then it means that you have a current account deficit. The economic relations of a country with outsideworld is monitored in a balance-sheet called payment balance. This balance-sheet shows us how much foreign currency surplus or deficit occurred within the term mentioned demonstrating the foreign currency incomes and expenditures in a balanced approach. Payment balance is made up of two sections. Current deficit balance and capital account. Only current deficit balance will be clarified here. Current account balance consists of 4 subbalances. 1. 2. 3. 4. Goods balance Services balance Investment revenues balance Current account transfers Goods Balance: The difference between foreign currency incomes obtained from the sales abroad and foreign currency costs for goods purchased from abroad by a country. Services Balance: The difference between foreign currency incomes obtained from services such as transport, insurance, tourism and foreign currency costs paid for similar services. Investment Revenues Balance: The difference between the profits gained from the FDI, interest revenues from portfolio investments by a particular country etc. and foreigners’ profits from similar investments in that country and foreign currency revenues in foreign currencies. Currentc Account Transfers: The foreign currency input from workers abroad. Therefore, we can formulate current account balance as; Current Account Balance = Goods Balance + Services Balance + Investment Revenue Balance + Current Account Transfers. If the result of this total is minus(-), current account deficit exists. 1.2. What Are The Effect of Current Account on Economy? An economy whose current account is on the rise needs to grow its capital accounts as well. The foreign dependence of an economy whose capital accounts grow increases. One of the most debated issues in Turkish economy is current account deficit. Given that the final goal of macroeconomic policies is to provide an interior and exterior balance in the economy of a particular economy, an un acceptible and unsustainable current deficit will mean gradual deviation from exterior balance, therefore, in this case, the problem needs solving through economic policies. While the provision and maintenance of interior balance means, in general, price stability and exact employment, exterior balance means the payment balance between the total expenditure and revenues of a particular country. Current account deficit can be explained as a deviation related to exterior imbalances in this regard(Telatar, 2011). 1.3.What are the Objectives of this Study? The aim of this study is to analyse the determinants of current account deficit through 2002:M1-2011:M12 data. This issue needs to be discussed and suggestions for solution need 112 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo to be developed because of the fact that current account deficit reached its peak 2011. The study is important in this respect. The rest of the study consists of 6 main sections. In the primary sections are completed that it is introduction, second section determinants of current account deficit in Turkey, the third section up-to-date data regarding current account deficit in Turkey, the fourth section literature, the fifth section analysis and final section. 2.Determinants of Current Account Deficit in Turkey The determinants of current account deficits (CAD) are now at the centre of international macroeconomics with the recent experience of large imbalances of a number of countries including the USA. The empirical literature appears to focus on the determinants and sustainability of CAD in individual countries or the consequences in a cross-section of countries (Özmen, 2005). The determinants of current account balances are of considerable interest in open economy macroeconomics. Alternative theoretical models have different predictions about the factors underlying current account dynamics and about the sign and magnitude of the relationships between current account fluctuations and these determinants(Chinn and Prasad, 2000). Hence, empirical analysis of the sort undertaken in this paper could help discriminate among competing theories. The current account deficit (CA), we define as follows14: CAt = NXt + rtBt + TRt (1) In the equation (1) current account deficit; explained through trade in goods, interest payments on foreign debt and transfer payments. NX t ; net exports of goods and services, Bt ; bills, bonds, equities, loans and physical capital that exceed the net foreign assets (foreign debt of countries, external debt stock), rt ; international interest rate, rt Bt ; net return on net foreign assets (foreign debt of the countries, the interest on foreign debt) and TRt ; represents transfer payments net of public and private sector. NXt = Xt – Mt, part of CAt has the biggest share is the last period in Turkey. When the country is indebted to rt Bt and CAt is negative value adversely affected.Transfer payments are usually made out of small countries, since there is little outsiders, TRt positive affected CAt. According to this definition, the causes of the current account deficit, external debt and interest payments on trade in goods. 3.Up-to-date data regarding current account deficit in Turkey The republic of Turkey produced 57 billion dolar current account deficit from 1923 to 2002. The current account deficit, which was 48,5 billion dolars in 2010, rose to 77,1 billion dollars in late 2011. Figure 1. Current Account Balance (January 2000 - August 2010. GDP ratio,%) 14In this section, Uygur(2004)were the work of the reference analysis. 113 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Source: Central Bank President D. Yilmaz Submission of Plan and Budget Commission of the Parliament (October 2010). Mehmet Simsek, Turkish Finance Minister, points out that current account deficit is an issue that has both structural and cyclical aspects. He also added that domestic demand in Turkey has grown 8-10 times as fast as that of Europe, and surging oil prices and Arab spring in the region caused the current account deficit to rise to an unpredictably high levels. 4.Literature The studies in which current account deficit is analyzed through exterior balance approach was launched by Husted (1992), and he was followed by Milesi-Ferretti and Razin (1996), Fountas and Wu (1999) and Edwards (2001). Khan and Knight (1983), using pooled cross-section time-series analysis for a sample of 32 non-oil developing countries during the period 1973-80. The empirical results suggest the importance of exercising circumspection in attributing to any single cause the current account imbalances experienced by non-oil developing countries during the 1970s. When foreign Exchange rate falls down, export goods’ prices rise and export is badly influenced. And imported goods’ prices relatively fall down and import increases. (Peker Hotunluoğlu, 2009) Edwards (2005) examined the relation between US dolar and US current account deficit. It was pointed out in the analysis that foreign demand for dollars will lower current accoun deficit and in the near future US foreign deficit will decrease the rate of growth at a remarkable scale. Aristovnik (2006) reached the conclusion in his research on transition economies that, in case current account transactions deficit surpasses 5% of GDP, eonomies generally have trouble with foreign sustainability. Yamak and Korkmaz (2007), in his study in which he used a data set of 2001:04-2005:09 period and modern times series techniques, reached the conclusion that Turkish current account deficit is sustainable in weak form and there is a co-integration relation between export-import series. Peker (2009) analyzed the sustainability of current account transaction deficit in Turkey through co-integration method using 1992:01-2007:12 period monthly data. As a result of the survey, he found out that current account deficit can be sustained at alow level, though a longterm relation between export and import series exists, co-integration co-efficient is 0,8926 114 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo consequently, he concluded that foreign currency revenues are lower than foreign currency expenditures. Oktar and Dalyancı (2011) found out that the sustainability of Turkish economical growth depends on maintenance of current account deficit. He also examined the relation between monetary policies and current account transactions for Turkish economy through time series, and found out that there is no Granger causality between Central Bank of Turkish Republic policy interest rate and current account transactions balance in the short term and an adverse co-integration relation in the long-run. Erdil Sahin (2011) emphasized that current account deficit because of high rate growth depending on domestic demand and execessively valuable Turkish Lira should be recovered through new structural reform policies based on firm growth Fundamentals. He concluded that current account deficit financed by short-term capital entrances like in Turkey, however, is unsustainable due to capital exit risk, whatever size it is. Chen (2011) examined the sustainability of current account deficit on economy policy in G-7 countries through econometric methods and found out that while current account deficit is sustainable for Germany and Japan in the long run, he couldn’t reach positive results for Canada, France, Italy, UK and USA. Kim, Min, Hwang and Mcdonald (2009) concluded in the studies they conducted on the 19812003 period quarter data of far-east countries such as Indonesia, Korea, Malasia, the Phillippines and Thailand that those developing countries had a high growth rate and their current account deficit was sustainable. 5.ANALYSIS 5.1.Data Set 2002:M1-2011:M12 covering the period of this study, five variables were used. What variables stand for; (CAD), the level of current account deficit, (NX), net exports (FID), interest on external debt, (TP) transfer payments and (TE) represents the costs of tourism. Variables were obtained from Central Bank of Turkey Electronic Data Delivery System, balance of payments detailed presentation part. As a result of the analysis, which variable or variables were effective on the variables that detrmine the current account deficit was analyzed. Estimates for all the test and computer package Eviews 5.1 program was used. 5.2.Method Without any restrictions on the VAR models, structural models can be delivered between the dynamic relationships and for this reason, often used in time series (Keating, 1990:453 - 454). Since the VAR model which is most frequently used in Time series of economic studies does not require inernal-external distinction, in any way out of economic theory, it differs from simultaneous equation systems in this respect. Moreover, that lagged values of dependent variables are also included in VAR models makes strong predictions for the future possible. (Kumar, Leona, Gasking, 1995: 365). As a result of estimating VAR model, instead of interpreting the parameters obtained, comments can be made for the future by passing the analysis of residues obtained from the estimated result of the system. The effects of shocks that these are likely to ocur in error terms of the variables in the models are measured with Impulse-Response functionsas shown in 115 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Enders(1995: 305-311), the Variance Decomposition which is determined with the model prediction and measures the prediction error variance another technique is used in the analysis of residuals. It is mention that with technical assistance mentioned, the effects of statistical shocks on the variables will be observed. 5.3.Unit Root Test Static variables are checked in the methods used in time series analysis. A time series is stationary if its average and variance does not change over time and the covariance in a period is dependent on only the distance between two periods not the period the covariance is calculated (Gujarati, 1999: 713). Dickey and the problem of the estimated regression models are faced with a fake because of the (Granger and Newbold, 1974), the obtained results do not reflect the true relationship. In such a case, T and F statistics are lost. Therefore, meaningful and non-stationary time series regression analysis reflect real relationships, but this is a cointegration relationship between the time series is made possible by the presence of (Gujarati, 1999: 725-726). This level of stability study, the variables before Augmented Dickey-Fuller (1979) test was analyzed to compare the results of this test is then Phillips-Perron (1988) test was used. Variable CAD NX FID TE TP Table 1. ADF Unit Root Test ADF Test Level Value 1.Difference 2.Differece -2.758[0] -2.022[12]* -9.457[11]** -1.695[1] -14.142[0]* ---1.414 [6] -5.436 [5]* ---0.003[12] -4.90711]* ---7.736[0] ----- Critical Value (%1) -3.493 -3.489 -3.489 -3.492 -3.486 Note: ADF with Schwarz criterion were tested. Level for all variables in the test format and the intercept was used as the level value. The first difference variables (*) and the second difference (**) and the level values were used. The values in square brackets, variables, states that the length of SIC determined by the appropriate delay. NX CAD and the second by taking the difference of the variables, and TE FID has become stationary by taking first difference. TP was the model-level value. The level of each variable included in the model are stationary. VAR will be estimated prior to model, appropriate for the model determined the length of the delay. To do this, the following tests were used: Table 2. Corelation LM Test 116 Lags LM-Stat Prob 1 2 3 4 5 6 7 8 35.40355 33.27135 30.48034 47.77828 31.62167 23.02558 30.94912 17.11513 0.0812 0.1244 0.2068 0.0640 0.1693 0.5761 0.1907 0.8776 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo 9 10 11 12 22.40669 16.95346 27.58093 20.79169 0.6122 0.8835 0.3275 0.7042 Table 3. VAR Lag Selection Criteria Endogenous Variables Lag LogL LR FPE AIC SC HQ 0 1 2 3 4 5 6 7 8 9 -8784.908 -8676.626 -8604.155 -8563.791 -8542.246 -8483.881 -8418.272 -8390.276 -8348.860 -8315.491 NA 204.6417 130.3155 68.87893 34.78816 88.88556 93.89887 37.49885 51.67573 38.57332* 7.62e+63 1.65e+63 6.94e+62 5.27e+62 5.70e+62 3.16e+62 1.55e+62 1.54e+62 1.22e+62 1.14e+62* 161.2827 159.7546 158.8836 158.6017 158.6651 158.0529 157.3077 157.2528 156.9516 156.7980* 161.4062 160.4954 160.2416* 160.5770 161.2576 161.2627 161.1349 161.6972 162.0133 162.4770 161.3328 160.0550 159.4343 159.4027 159.7164 159.3546 158.8598* 159.0552 159.0043 159.1010 * indicates lag order selected by the criterion LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error AIC: Akaike information criterion SC: Schwarz information criterion HQ: Hannan-Quinn information criterion Table 3 is examined, LR, FPE and AIC values are in the same direction, and 9 is the minimum value for the delay. Both aim to determine the level of consistent delay, and, due to lack of a very long time period covered nine-term delay, the delay level is determined as appropriate for the model. 5.4.Variances Decomposition To investigate the presence of structural breaks related to the variables, using the squares of residuals, and thus return the system investigating the CUSUM structural break related to the variables (Brown, Durbin and Evans, 1975:149-155) chart was used. Figure 2. CUSUM of variables 40 30 20 10 0 -10 -20 -30 117 -40 03 04 05 06 CUSUM 07 08 09 10 5% Significance 11 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Equalities, we can say that the structural break related to other variables. Due to a fracture model variables were observed in the break out will be estimated using an artificial variable to express any. Table 4. Variance Decomposition Results Period S.E. 1 2 3 4 5 6 7 8 9 10 84300967 1.49E+08 1.61E+08 1.63E+08 1.71E+08 1.71E+08 1.78E+08 1.94E+08 2.02E+08 2.04E+08 Period S.E. 1 2 3 4 5 6 7 8 9 10 726.8696 822.0173 835.0815 875.3560 889.0531 904.9741 971.0690 1001.682 1016.415 1029.796 Period S.E. 1 2 3 4 5 6 7 8 9 10 6409819. 8578346. 8867969. 8929659. 9101219. 9537165. 10506134 11332499 11463066 11850968 Period 118 S.E. Variance Decomposition of DDCAD: DDCAD DNX DFID 100.0000 95.09178 90.33680 87.25829 81.11985 80.28052 77.81817 72.94288 69.33291 68.12074 0.000000 2.023950 2.747490 5.747876 7.289460 7.239189 6.709760 6.458122 6.572370 6.577430 0.000000 1.984184 4.052956 4.171563 3.938387 3.901067 5.361156 6.146644 7.840872 8.276226 Variance Decomposition of DNX: DDCAD DNX DFID 62.11543 60.45094 59.89074 61.66188 59.94052 58.08003 53.04461 52.55017 51.33369 50.83004 37.88457 31.35544 31.07529 28.68063 28.02536 28.67585 33.22206 31.41238 30.52297 29.86570 0.000000 0.956160 0.938940 1.468556 1.528729 3.093604 2.904349 3.676091 4.409506 4.308151 Variance Decomposition of DFID: DDCAD DNX DFID 0.960621 1.703198 1.816690 1.843917 1.913250 5.070666 4.620808 4.982647 5.196262 5.048882 3.561922 7.614193 9.815775 10.02791 10.74781 10.02298 8.634321 7.593771 9.053756 11.80834 95.47746 87.11718 82.08521 80.96368 78.29582 72.88628 76.81778 76.05110 74.52841 71.04702 Variance Decomposition of DTE: DDCAD DNX DFID DTE TP 0.000000 0.591259 2.412449 2.333944 7.201878 8.099975 7.610183 10.21749 12.30576 12.54971 0.000000 0.308826 0.450309 0.488322 0.450421 0.479254 2.500734 4.234860 3.948098 4.475894 DTE TP 0.000000 7.233698 7.125766 7.026169 9.333278 9.012475 8.086086 9.583833 9.308097 9.425688 0.000000 0.003761 0.969261 1.162764 1.172110 1.138039 2.742890 2.777520 4.425732 5.570417 DTE TP 0.000000 0.278227 2.768816 2.734430 4.769543 7.228628 5.978435 7.402783 7.297956 7.549994 0.000000 3.287206 3.513511 4.430063 4.273574 4.791440 3.948656 3.969697 3.923618 4.545765 DTE TP �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo 1 2 3 4 5 6 7 8 9 10 3664782. 4203964. 4559527. 4967974. 5040278. 5131554. 5398903. 5870845. 6065571. 6194207. 4.144082 9.252956 8.289357 7.025928 7.202990 8.425076 11.91116 11.86556 12.60093 14.69380 0.903425 0.705626 6.540827 9.021794 9.302489 9.819431 12.21158 17.82288 21.15931 20.81232 0.009031 3.328949 3.097522 5.351039 5.226973 6.159875 6.516126 10.86181 10.38818 10.05785 94.94346 86.21273 78.29637 69.75619 68.86394 66.43832 60.95210 52.02978 48.82419 47.10559 0.000000 0.499736 3.775922 8.845054 9.403608 9.157296 8.409036 7.419963 7.027386 7.330433 Variance Decomposition of TP: Period S.E. DDCAD DNX DFID DTE TP 1 2 3 4 5 6 7 8 9 10 5585095. 5933919. 6556542. 6756191. 7099558. 7377532. 7431274. 7561157. 7734123. 8063976. 10.32720 10.60821 9.127853 10.77251 13.86680 13.17017 14.08089 14.27852 13.79738 20.45568 0.065194 0.778084 1.108046 1.114342 1.179061 2.558330 2.533998 3.692998 3.539359 3.295772 1.202163 1.230677 6.006631 5.727087 7.423425 6.979129 6.881688 6.671441 10.22790 9.469962 0.198028 1.858145 8.924092 11.34185 10.38171 13.20643 13.29330 13.02000 12.45194 11.50355 88.20741 85.52488 74.83338 71.04422 67.14900 64.08594 63.21013 62.33704 59.98342 55.27503 Cholesky Ordering: DDCAD DNX DFID DTE TP Accordingly, the current account deficit is largely determined by its own shocks. Net exports are determined by its own shocks in the short term, and by tourism expenditure and external debt with interest in the long term. It looks that net exports are determined by current account deficit and tourism expenditures as well as its own shocks in the long run. Foreign debt interest rate results from supply shocks and net exports in the long term. Tourism expenditures are affected by net exports and current account deficit in the long term. Supply shocks of transfer payments result from itself in the short term and from tourism expenditures and foreign debt interest rate in the long term. That is, a negative increase in exports affects macroeconomic variables by triggering current account deficit. It is a challenge to take current account deficit that follows an unstable trend to a stable line. In other words, unless a regulation is made in order to break the trend of unrest result in the coninuation of current account deficit. This situation is among basic findings of the survey. One of the most significant consequences of variance decomposition is that current account deficit is determined again by itself. The results obtained are supported by the outcomes of impulse-response analysis. 119 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo 5.5.Impulse Response Function Analysis of basic situation arising as a result, net exports as the determinants of current account deficit, external debt interest, transfer payments and indirect effects of tourism expenditures affect the current account deficit. Response to Cholesky One S.D. Innovations Response of DDCAD to DDCAD Response of DDCAD to DNX Response of DDCAD to DFID Response of DDCAD to DT E Response of DDCAD to T P 1.20E+08 1.20E+08 1.20E+08 1.20E+08 1.20E+08 8.00E+07 8.00E+07 8.00E+07 8.00E+07 8.00E+07 4.00E+07 4.00E+07 4.00E+07 4.00E+07 4.00E+07 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 -4.00E+07 -4.00E+07 -4.00E+07 -4.00E+07 -4.00E+07 -8.00E+07 -8.00E+07 -1.20E+08 -8.00E+07 -1.20E+08 1 2 3 4 5 6 7 8 9 10 -8.00E+07 -1.20E+08 1 2 Response of DNX to DDCAD 3 4 5 6 7 8 9 10 -8.00E+07 -1.20E+08 1 2 Response of DNX to DNX 3 4 5 6 7 8 9 10 -1.20E+08 1 2 Response of DNX to DFID 3 4 5 6 7 8 9 10 1 Response of DNX to DT E 600 600 600 600 500 500 500 500 500 400 400 400 400 400 300 300 300 300 300 200 200 200 200 200 100 100 100 100 0 0 0 0 0 -100 -100 -100 -100 -100 -200 -200 -200 -200 -200 -300 1 2 3 4 5 6 7 8 9 10 -300 1 2 Response of DFID to DDCAD 3 4 5 6 7 8 9 10 2 Response of DFID to DNX 3 4 5 6 7 8 9 10 2 Response of DFID to DFID 3 4 5 6 7 8 9 10 1 8000000 8000000 8000000 6000000 6000000 6000000 6000000 4000000 4000000 4000000 4000000 4000000 2000000 2000000 2000000 2000000 2000000 0 0 0 0 0 -2000000 -2000000 -2000000 -2000000 -2000000 -4000000 -4000000 -6000000 2 3 4 5 6 7 8 9 10 -4000000 -6000000 1 2 Response of DT E to DDCAD 3 4 5 6 7 8 9 10 2 Response of DT E to DNX 3 4 5 6 7 8 9 10 2 Response of DT E to DFID 3 4 5 6 7 8 9 10 1 4000000 4000000 4000000 3000000 3000000 3000000 3000000 2000000 2000000 2000000 2000000 2000000 1000000 1000000 1000000 1000000 1000000 0 0 0 0 0 -1000000 -1000000 -1000000 -1000000 -1000000 -2000000 3 4 5 6 7 8 9 10 -2000000 1 2 Response of T P to DDCAD 3 4 5 6 7 8 9 10 -2000000 1 2 Response of T P to DNX 3 4 5 6 7 8 9 10 2 Response of T P to DFID 3 4 5 6 7 8 9 10 1 6000000 6000000 6000000 6000000 5000000 5000000 5000000 5000000 4000000 4000000 4000000 4000000 3000000 3000000 3000000 3000000 3000000 2000000 2000000 2000000 2000000 2000000 1000000 1000000 1000000 1000000 0 0 0 0 0 -1000000 -1000000 -1000000 -1000000 -1000000 -2000000 -2000000 -2000000 -2000000 -2000000 -3000000 3 4 5 6 7 8 9 10 -3000000 1 2 3 4 5 6 7 8 9 10 2 3 4 5 6 7 8 9 10 4 5 6 7 8 9 10 3 4 5 6 7 8 9 10 3 4 5 6 7 8 9 10 9 10 1000000 -3000000 1 3 Response of T P to T P 4000000 2 2 Response of T P to DT E 5000000 1 10 -2000000 1 6000000 -3000000 9 Response of DT E to T P 4000000 2 2 Response of DT E to DT E 3000000 1 8 -6000000 1 4000000 -2000000 7 -4000000 -6000000 1 6 Response of DFID to T P 8000000 1 2 Response of DFID to DT E 6000000 -6000000 5 -300 1 8000000 -4000000 4 100 -300 1 3 Response of DNX to T P 600 -300 2 -3000000 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 Figure 3. Impuse-Response 6.Conclusion In this survey, which was conducted on the determinants of current account deficit, current account deficit, export, foreign debt interest rate, transfer payments and tourism expenditure were studied. The variables mentioned were subjected to VAR analysis for 2002:M12011:M12 period as a result of stationarity research as long as they are stationary. First, of the variables CAD and NX, the second difference taken, FID and TE the first difference taken, were made stationary. TP was involved in the model with its surface value. Each variable was involved in the model so long as they are stationary. The model’s time-lag length was determined as 9. According to variance discrimination results obtained from VAR model composed under this roof, current account deficit is determined by its own shocks in the short term. In addition, current account deficit prediction error variance is determined by tourism expenditures and foreign debt interest rate as well as its own variables. Current account deficit is affected by export, foreign debt interest rate, transfer payments and shock given to tourism expenditures. 120 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo It was observed that current account deficit is a potential problem in Turkey. It is thought that it can stimulate crisis unless kept under control. However, the precautions taken by the Central Bank of Turkish Republic recently are of great importance in terms of hindering current account deficit. Therefore, not only total demand will be intimidated but also national amount of savings will be raised. In this respect, increasing tourism revenues, keeping short term capital movements under control measures to decrease imports and increase exports could be taken into account. REFERENCE ARISTOVNIK, A. (2006), “Current Account Deficit Sustainability in Selected Transition Economies”. Zb. Rad. Ekon. Fak. Rij. Vol.24 sv.1.81-102 BROWN, R.L., DURBİN, J. and EVANS, J.M. (19759: “Techniques for Testing the Constancy of Regression Relationhips Over Time”, Journal of the Royal Statistical Society, B, 37, Issue 2. CHEN, S.W. (2011), “Are Current Account Deficits Really Sustainable in the G-7 Countries?” Japan and the World Economy 23, 190-201. CHINN, M. and PRASAD, E.S. (2000), “Medium-Term Determinants of Current Account in Industrial and Developing Countries: An Emrical Exploration”, NBER Working Paper, 7581. EDWARDS, S. (2001), “Does the Current Account Matter?”, NBER, WP, No:8275:1-71. EDWARTS, S. (2005), “Is the U.S. Current Account Deficit Sustainable? And if not, How Costly is Adjustment Likely to be?” Naber Working Paper Series 11541 ENDERS, W. (1995), Applied Econometric Time Series: Wiley Series in Probability and Mathemathical Statistics, New York, John Wiley Inc. ERDİL ŞAHİN, B. (2011), “Türkiye’nin Cari Açık Sorunu”. Ekonomi Bilimleri Dergisi, cilt.3, no.2, ISSN:1309-8020 FOUNTAS, S. and WU, J.L. (1999), “Are the U.S. Current Account Deficits Really Sustainable?”, International Economic Journal, 13(3). GRANGER, C.W.J., NEWBOLD, P. (1974). “Spurious regressions in econometrics”. Journal of Econometrics, 2 (2), pp. 111-120. GUJARATI, D. N. (1999). Basic Econometrics, McGraw Hill, Literatür Yayıncılık, 3 rd edition, İstanbul. HUSTED S. (1992), “The Emerging U.S. Current Account Deficit in the 1980s: A Cointegration Analysis,” The Review Of Economics & Statics, February, pp: 159-166. KEATING, J.W. (1990), “Identifying VAR Models Under Rational Expectations”, Journal of Monetary Economics, 25. KIM, B.H.,MIN, H.G.,HWANG, Y.S. and MCDONALD, J.A. (2009), “Are Asian countries’ current accounts sustainable? Deficits, Even When Associated With High Investment, Are Not Costless”, Journal of Policy Modeling, 31: 163–179. 121 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo KHAN, M.S. and KNIGHT, M.D. (1983), “Determinants of Current Account Balances of Non-Oil Developing Countries in the 1970s An Empirical Analysis”. International Monetary Fund, Vol. 30, No. 4, pp. 819-842. KUMAR, V., LEONA, R.P. and GASKING, J.N. (1995), “Aggregate and Disaggregate Sector Forecasting Using Consumer Confidence Measures”, International Journal of Forecasting. MILESI-FERRETTI, G. M. and RAZIN, A. (1996), “Sustainability of Persistent Current Account Deficits”, NBER, WP, 5467. PEKER, O. (2009), “Türkiye’de Cari Açık Sürdürülebilir mi? Ekonometrik Bir Analiz”. Kocaeli Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 17, 1, 164-174 PEKER, O. and HOTUNLUOĞLU, H (22009), “Türkiye´de Cari Açığın Nedenlerinin Ekonometrik Analizi”. Atatürk Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 23, 3, 221-237 PHILLIPS, P.C.B. and PERRON, P. (1988) "Testing for a Unit Root in Time Series Regression", Biometrika, 75,335–346. OKTAR, S. and DALYANCI, L. (2011), “Türkiye Ekonomisinde Para Politikasının Cari İşlemler Dengesi Üzerindeki Etkisinin Ekonometrik Analizi”. Marmara Üniversitesi İ.İ.B.F. Dergisi cilt.3, sayı.1 ss.1-22 ÖZMEN, E. (2005), “Macroeconomic and institutional determinants of current account deficits”, Applied Economics Letters, 12, 557-560. TELATAR, E. (2011), “Türkiye’de Cari Açık Belirleyicileri ve Cari Açık-Krediler İlişkisi”, Bankacılar Dergisi, Sayı 78. UYGUR, E. (2004), “Cari Açık Tartışmaları”, İktisat, İşletme ve Finans, 19(222): 5-20. YAMAK, R. and KORKMAZ, A. (2007), “Türk Cari İşlemler Açığı Sürdürülebilir mi? Ekonometrik Bir Yaklaşım”, Bankacılar Dergisi, 60. Earning Isparta Carpet Business To The Local Economy Again And Ensuring Its Sustainibility By Revising It Nesrin Şalvarci Türeli, Erhan Türeli Süleyman Demirel University, Isparta, Turkey E-mails: nesrintureli@sdu.edu.tr, erhantureli@sdu.edu.tr Abstract Hand-woven carpet, one of the symbols of Isparta has lost its popularity in the sense of business, employment, socio-cultural and economic aspects. In 1960s the carpet industry which provided a great amount of income especially in local areas, and then in the overall city 122 � 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. 1318 Title A name given to the resource Determinants Of Turkey Current Account Deficit: An Econometric Analysis Author Author M. Metin Dam, Metin Dam Abstract A summary of the resource. The main causes of the current account deficit in Turkey; the foreign trade deficit, the high ratio of intermediate goods imports, high oil prices and Turkey's energy import dependence, lack of domestic savings, foreign direct investment and low tourism revenues. In this study, the causes of the current account deficit and current account deficit financing structure were examined. In addition, the determinanats of Turkey current account deficit wereanalyzed via VAR methods using the data of 2002-2011 monthly current account deficit, net export, interest on external debt, transfer payments and costs of tourism. As a result of the study, According to variance discrimination results obtained from VAR model composed under this roof, current account deficit is determined by its own shocks in the short term. In addition, current account deficit prediction error variance is determined by tourism expenditures and foreign debt interest rate as well as its own variables. Current account deficit is affected by export, foreign debt interest rate, transfer payments and shock given to tourism expenditures. Keywords: Current Account Deficit, Determinants, VAR, Turkey 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/03884403535bf7041eb4007bc0e5584b.pdf cfa28b515d85e7a7db99ec8c456316c2 PDF Text Text Determination of Biological Effect of Entomopathogen Fungus on Galleria mellonella (Lepidoptera: Pyralidae) Ş. Evrim Arıcı University of Suleyman Demirel, Faculty of Agriculture, Department of Plant Protection Isparta/TURKEY evrima@ziraat.sdu.edu.tr Mehmet Sedat Sevinç University of Suleyman Demirel, Faculty of Agriculture, Department of Plant Protection Isparta/TURKEY medantinc@mynet.com.tr Đsmail Karaca University of Suleyman Demirel, Faculty of Agriculture, Department of Plant Protection Isparta/TURKEY ikaraca@sdu.edu.tr Ozan Demirözer University of Suleyman Demirel, Faculty of Agriculture, Department of Plant Protection Isparta/TURKEY ozand@ziraat.sdu.edu.tr Abstract: In this study, Entomopathogenic fungi Perlomyces chlamidosporia, Fusarium subglutinans, Fusarium solani, Baveria bassiana were investigated to the effect of larvae great wax moth Galleria mellonella (Lepidoptera: Pyralidae). P. chlamidosporia, F. solani, two isolates of F subglutinans (8, 12), and B. bassiana were cultured for 10 days potato dextrose agar (PDA) medium and incubated in dark conditions at 25 ± 1 ºC. 1x107 spor / ml suspension for each isolates was used for each isolates and Tween 20 was added in. By dipping method suspensions of spore were experimented on Larvae of G. mellonella (Lepidoptera: Pyralidae). Larvae of G. mellonella were cultured in 12-cm plastic petrie dishes at 25 ± 1 0C. 1, 24, 48 hours and 8, 13 days after the applications were eveluated the death larvae of G. mellonella. The mortality rate of G. mellonella was observed between 50-70% but statistical difference between isolates was not determined. The results obtained in the application used the types of Entomopathogenic fungus can be used biological control for G. mellonella. Introduction Great wax moth (Galeria mellonella) is economical pest of honey bees (A. mellifera) and they are widespread in particularly low altitude and temperate reason region which beekeeping land (Allan, 2000), and they known that an important pest which beekeepers fall into trouble to protect their honeycombs all over the world (Sanford, 2003). Great Wax Moth’s mature individuals, pupae, and egg stages don’t damage to effervesced honeycombs but their larvaes do different levels of damage to honeycombs in the appropriate environtenmal conditions (temperature, humidity, food). Great wax moth develops at above 4 °C' temperature and %70 humidity, commonly eliminates possibility of re-use of honeycomb by opening the tunnels in stored effervesced honeycombs. (HaeWoon et al. 1995, Ritter et al., 1992). Such as chemical (aluminum phosphide, methyl bromide, ethylene dibromide, paradichlorobenzene (naphthalene), sulfur), physical applications (cold-hot) and biological insecticides (Bacillus thuringiensis) control methods used in different ways to protect effervesced honeycombs from great wax moth in some countries (Tutkun et al. 1987; Ritter et al. 1992; Ahmad, 1994; Yacobson et al. 1997; Delaware, 2000; Kumova &Korkmaz 2002). 19 �Many chemical substances which using to Great wax moth reduce the chance of marketing or eliminate it because of residue on honey or wax. Today from this perspective, residue and ease of application of insecticide to pest has taken into consideration and lead to new ways is inevitable (Allan, 2000). Biological control is one of the most suitable alternative method for chemical control in different ecosystems and an environmentally friendly alternative that involve the use of natural enemies and pathogens to control pests. A number of entomopathogenic fungi have been identified biological control agents to control insects. These fungal agents are virulent, infect insect by contact persist in the environment for a long time and have one of the largest host list. Recently it was studied on safety of entomopathogenic fungi (Goettel & Jaronski 1997; Goettel & Hajek 2000; Goettel et al. 2001; AliShtayeh1 et al. 2002, Shah & Pill 2003, Copping, 2004, Tuininga et al 2009¸ Zeinat et al. 2009). The aim of the present study was to determine of possible effects of entomopathogenic Fusarium subglutinans, Fusarium solani, Beauveria bassiana and Pochonia clamydosporia on the Galeria mellonella’s larvae Materials and Methods Galeria mellonella and entomopathogenic fungi two isolates of Fusarium subglutinans (8, 12), Fusarium solani, Beauveria bassiana and Pochonia clamydosporia were used in this study. Galeria mellonella were taken to climate room in 25±1 ºC temperature and % 65±5 humidity for reproduction of their number. Food from mix of honey, glycerin, dry wax and wheat bran were taken into the jar which closed with tulle for air circulation and G. mellonella’s larvaes were taken into this jars to be mature individuals. The adults from pupaes were taken into the jars which prepared by same way to laying egg. The larvaes from egg were removed from food environment before the pupal period for application. The fungi were cultured on PDA medium and incubated at 25ºC under dark conditions for 10 days. Suspensions of spore from each isolate were prepared and spore concentrations (1X107) were prepared in distilled sterillized water with the help of haemocytometer. By dipping method suspensions of spore were experimented on last instar stage larvae and newly hatched G. mellonella’s larvaes (3-5 sn), after Tween 20 was added in suspensions. For the control experiment, sterillized water was applied to the test insects. The G. mellonella’s larvaes were put in plastic petri dishes (12 cm) under controlled conditions (25ºC) and the plastic petri dishes were supplied with honey for nutrition purposes. Petri dishes containing G. mellonella’s larvaes were sealed with Parafilm to reduce moisture loss. Samples were then incubated at 25 0C and a photoperiod of 16 hours light for 13 days. For each petri dish had five larvae of G. mellonella five replicants and experiment for each entomopathogenic fungi consisted of five replicate according to completely randomized design. For each experiment daily mortality had been recorded for 13 days. Obtained data from bioassays were analyzed by SPSS version 16.0.1. Data were analyzed by ANOVA and treatment means separated by TUKEY test (P <0:05) to select isolates for a new assay with several cultivars Results and Discussion In this study G. mellonella larvae found to be infected by entomopathogenic fungi and was observed mortality of G. mellonella’s larvae (Table 1). Although a statistical difference was found between the isolates and the control group, no statistical difference was determined between the isolates. Entomophage pathogen Pochonia clamydosporia Fusarium solani Fusarium subglutinans (12) Fusarium Mortality of G. mellonella 24 h 0,2±0,2 48 h 0,6±0,4 8d 1±0,3 13 d 1,4±0,5 0,6±0,4 0,2±0,2 0,6±0,4 0,2±0,2 1,2±0,7 1±0,3 3±0,8 2,8±07 0,6±0,2 1±0,4 1,6±0,7 2,8±0,5 20 �subglutinans (8) Beauveria bassiana 0,4±0,2 0,6±0,6 1,6±0,5 2,8 ±0,6 Table 1: Mortality of G. mellonella larvae It was observed 4% mortality of the larvae of G. mellonella infected with F. subglutinans 12 , 20% with F. subglutinans 8, while was determinated 12% mortality of larvae of G. mellonella infected with Fusarium solani, B.bassiana, and P. clamidosporium in 48 h (Table 2). It was recorded in 13 d 28% mortality of larvae of G. mellonella infected with P. clamidosporium, 60% with F.solani, while was observed 56% mortality of G. mellonella larvae with F. subglutinans 12, F. subglutinans 8 and with B.bassiana. Some researcher indicated similarly results (James & Elzen 2001; Shah & Pill 2003; Mohammed et al. 2009). In addition same researcher used larvae of G. mellonella to determine the relative abundance of entomopathogenic nematodes, and fungi (Chadler et al. 1997; Ali-Shtayeh et al. 2001; Tuininga et al. 2009). Entomophage pathogene Mortality % 48h 13 d 12 12 4 20 28 60 56 56 12 56 Pochonia clamydosporia Fusarium solani Fusarium subglutinans (12) Fusarium subglutinans (8) Beauveria bassiana Table 2: Mortality percentage of G. mellonella ‘s larvae In conclusion; fungal mycel of entomopathogens used in this study had grown on death larvae of G. mellonella. Entomopathogenic fungi can used for biological control and in pest management. References Ahmad, M. (1994). Biological Control of Greater Wax Moth, Galleria mellonella L. Journal of Apicultural Research (Pakistan). Vol. 32(3), 319-323. Allan, L. (2000). Wax Moth and its Control. Departmen of Agriculture Western Australia. http://www.agric.wa.gov.au/agency/pubns/farmnote/2000/f00697.htm. Chandler, D., Hay, D., &Reid, A.P. (1997). Sampling and occurrence of entomopathogenic fungi and nematodes in UK soils Applied Soil Ecology 5 133-141 Copping, L.G. (2004). The Manual of Biocontrol Agents.British Crop Protection Council, Alton, 702 pp Delaware, M. (2000). New Jersey, Pensylvania, Westiginia and the USDA Cooperating. MAAREC Publication, 4.5. Goettel, M.S., & Jaronsky, S.T. (1997). Safety and registration of microbial agents for control of grasshoppers and locust. Mem. Entomol. Soc. Can., 171: 83-99. Goettel, M.S., & Hajek A.E. (2000). Evaluation of non-target effects of pathogens used for management of arthropods. In: Wajnberg E,Scott JK, Quimby PC (eds) Evaluating indirect ecological effects of biological control. CABI Publishing, Wallingford, pp 81–97 Goettel, M.S., Hajek, A.E., Siegel, J.P., & Evans, H.C. (2001). Safety of fungal biocontrol agents. 21 �HaeWoon, O., ManYoung, L., Young Duck, & Chang (1995). Developing Periods and Damage Patterns of Combs by The Greater Wax Moth, Galleria mellonella. Korean Journal of Apiculture 10: 1, 5-10. James, R.R., & Elzen, G.W. (2001). Antagonism between Beauveria bassiana and imidacloprid when combined for Bemisia argentifolii (Homoptera: Aleyrodidae) control. J. Econ. Entomol., 94: 357–61 Kumova, U., & Korkmaz, A. (2002). Peteklerin Büyük Bal Mumu Güvesi (Galleria mellonella L.) ‘ ne Karşı Korunması Üzerine Bir Araştırma. Türkiye 3. Arıcılık Kongresi, Adana. Mohammed S. Ali-Shtayeh1, Abdel-Basit B.M, & Mara’i Rana M. J. (2002). Distribution, occurrence and characterization of entomopathogenic fungi in agricultural soil in the Palestinian area Mycopathologia 156: 235–244, 2002. Ritter, W., Perschill, F., Vogel R. 1992. Comparision of the effect of various methods fort he control of wax moths. Allgemeine Deutsche Imkerzeitung. 26: 1, 11-13. CAB Abstracts 1993-1994, AN:930234018 Sanfort, M.T. (2003). Controlling Wax moth, one of a series of the Entomology and nematology Department, Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences, University of Florida. EDIS Web Site at http://edis.ifas.ufl.edu Shah, P. A., & Pell, J. K. (2003). Entomopathogenic fungi as biological control agents. Appl Microbiol Biotechnol, 61:413–423 Tuınınga, A. R., Mıller J. L., Morath S. U., Danıels T. J., Falco R. C., Marchese M., Sahabı, S., Rosa, D., & Stafford K. C. (2009). Isolation of Entomopathogenic Fungi From Soils and Ixodes scapularis (Acari: Ixodidae) Ticks: Prevalence and Methods. J. Med. Entomol. 46(3): 557-565 Tutkun E., Çakmakçı, L., & Boşgelmez A. (1987). Bal Arısı Kolonilerinde Bacillus thrugiensis Preparatlarının Büyük Mum Güvesi (G. mellonella) Larvalarına Karşı Kullanım Olanakları Üzerinde Araştırmalar. TÜBĐTAK, Tarım ve Ormancılık Araştırma Grubu, Tarımsal Mikrobiyoloji Ünitesi Proje no: Tarmik-8-34 s. Yacobson, B., Navarro, S., Donahaye, E. J., Azrielli, A., Sloyevsky, Y., & Ephrati, H. (1997). Control of Beeswax moths using carbondioxide in flexible plastic and metal structure. In: Proc. Int. Conf. Controlled atmosphere and fumigation in grain storages 21-26 April 1996, Nicosia Cyprus pp: 169-174. Zeinat K.M., S.A. El-Sayed, Radwan T.E.E., & Ghada, S. Abd El-Wahab (2009). Potency Evaluation of Serratia marcescens and Pseudomonas fluorescens as Biocontrol Agents for Root-knot Nematodes in Egypt. Journal of Applied Sciences Research, 4(1): 93-102 22 � 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. 324 Title A name given to the resource Determination of Biological Effect of Entomopathogen Fungus on Galleria mellonella (Lepidoptera: Pyralidae) Author Author Arıcı, S. Evrim Sevinç, Mehmet Sedat Karaca, İsmail Demirözer, Ozan Abstract A summary of the resource. In this study, Entomopathogenic fungi Perlomyces chlamidosporia, Fusarium subglutinans, Fusarium solani, Baveria bassiana were investigated to the effect of larvae great wax moth Galleria mellonella (Lepidoptera: Pyralidae). P. chlamidosporia, F. solani, two isolates of F subglutinans (8, 12), and B. bassiana were cultured for 10 days potato dextrose agar (PDA) medium and incubated in dark conditions at 25 ± 1 ºC. 1x107 spor / ml suspension for each isolates was used for each isolates and Tween 20 was added in. By dipping method suspensions of spore were experimented on Larvae of G. mellonella (Lepidoptera: Pyralidae). Larvae of G. mellonella were cultured in 12-cm plastic petrie dishes at 25 ± 1 0C. 1, 24, 48 hours and 8, 13 days after the applications were eveluated the death larvae of G. mellonella. The mortality rate of G. mellonella was observed between 50-70% but statistical difference between isolates was not determined. The results obtained in the application used the types of Entomopathogenic fungus can be used biological control for G. mellonella. 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 Q Science (General) https://omeka.ibu.edu.ba/files/original/7aed1fe192b2e1bbd39dac1a7534c587.pdf 0c0a8cd59f24cfe1e1c0fa8a7eaf37ff PDF Text Text International Conference on Economic and Social Studies, 10-11 May, 2013, Sarajevo Determination of Cultural Characteristics of Hunters in İstanbul Province to Hunting and Wildlife Management (İstanbul Province Case) İsmail Şafak Aegean Forestry Research Institute, İstanbul, Turkey isafak35@hotmail.com Taner Okan University of İstanbul, Faculty of Forest, İstanbul, Turkey tokan@İstanbul.edu.tr Erdem Hizal University of İstanbul, Faculty of Forest, İstanbul, Turkey hizal@İstanbul.edu.tr Tamer Keçecioğlu Ege University, İzmir, Turkey tamer.kececioglu@ege.edu.tr Caner Işık Adnan Menderes University, Aydin, Turkey kim.caner@gmail.com Sedat Acar Mustafa Kemal Paşa National Park, Turkey sedatacar3495@hotmail.com Turkey has a rather important potential from the point of view of wildlife resources which includes both species diversity and sheltering capacity. On the other hand, wildlife in Turkey has been endangered since wilderness ecosystem and habitats have been damaged and managed badly. There is a rather important role of hunters in this process. Hunting, which means catching of either living or lifeless animals that live freely in the nature, has attracted attention of mankind almost in every term. Hunting has been accepted as an important activity also today as well as in the past. It is not a realistic approach considering the hunting passion which comes to today with the traces from the past as nothing. Today, hunters have become a guaranty of biodiversity and wilderness by getting an environmentalist status. As increasing economic and social welfare, first of all demands of hunters and demands of stakeholders from wildlife sector are getting increased by varying. In this context, it is obligatory to develop and reach a rationalist management of wildlife potential that promises a great future of Turkey. 176 �International Conference on Economic and Social Studies, 10-11 May, 2013, Sarajevo In Turkey, lawful regulations were made in 2003 with act of 4915 to solve problems of wildlife management. Wildlife management in Turkey still live a transition period, problems are going on because new laws and its implementations have not yet established. Among these problems, illegal hunting, uneducated hunters, lack of hunter controlling have important place; basic problems are social, cultural, economic and lack of scientific structure. Associations, institutions and enterprises attach importance for knowing cultural elements and factors that affect them. It has been use an active instrument to convey cultural data and to reach aims. In this context, researches that determine human structure and cultural sensitiveness fields in the management of wildlife resources are very important. There have been seen some researches which related to determine of hunter profile since 2002. But, there are too few studies to determine cultural characteristics of hunters. In this context, it is needed some studies for hunter culture as related to management activities. This research project includes hunting clubs and their members in İstanbul. For this aim, according to sample size, there will be reached primary data using questionnaire method which is prepared for hunters. Some data based on literature research constitute secondary data of research. In this research, questionnaire, called the hunter form, will be developed. Hunter Form; cultural elements of hunters, profile of hunters and level of importance on the issue of hunters were determined. Nine-point Likert Scale was used in some questions to determine hunters’ thinking on the issue. The hunter form is designed in two parts. The first part is related to the demographic characteristics of hunters. The second section means detection levels of the components of the culture of hunters. In this research project, values forming culture of hunters were investigated in context of leaders and heroes, ceremony and symbols, story and legends, language, customs, norms and organizational socialization. Results of research project will be used as basic data in controlling and training of hunters, providing coordination and planning and sources. Data obtained from the hunters were evaluated using frequency, percentage, chi-square test, and Kruskal-Wallis one-way analysis of variance. In this context, there were benefited from SPSS program and others. Keywords: Hunting, Hunting Culture, Hunting and Wildlife Management, İstanbul. 177 � https://omeka.ibu.edu.ba/files/original/f3397baab42f3fdc9be7a3d48a60e930.docx d848a595269e0ed57843527564f4bdd4 https://omeka.ibu.edu.ba/files/original/1ac59d552e60dd523432fd817ffb1081.pdf fe7dda036e8cd40d55092ac21821bfed PDF Text Text Determination of Cultural Characteristics of Hunters for Hunting and Wildlife Management (The Case of Istanbul Province) Ismail Safak Aegean Forestry Research Institute, Izmir, Turkey isafak35@hotmail.com Taner Okan Istanbul University, Istanbul, Turkey tokan@istanbul.edu.tr Erdem Hızal Istanbul University, Istanbul, Turkey hizal@istanbul.edu.tr Tamer Keçecioğlu Ege University, İzmir, Turkey tamer.kececioglu@ege.edu.tr Caner Işık Adnan Menderes University, Aydın, Turkey caner@adu.edu.tr Sedat Acar Mustafa Kemal Paşa National Park, Bursa, Turkey sedatacar3495@hotmail.com Abstract Turkey has a rather important potential from the point of view of wildlife resources which includes both species diversity and sheltering capacity. On the other hand, wildlife in Turkey has been endangered since wilderness ecosystem and habitats have been damaged and managed badly. There is a rather important role of hunters in this process. Hunting, which means catching of either living or lifeless animals that live freely in the nature, has attracted attention of mankind almost in every term. Hunting has been accepted as an important activity also today as well as in the past. It is not a realistic approach considering the hunting passion which comes to today with the traces from the past as nothing. Today, hunters have become a guaranty of biodiversity and wilderness by getting an environmentalist status. Associations, institutions and enterprises attach importance to knowing cultural elements and factors that affect them. It has been used as an active instrument to convey cultural data and to reach aims. In this context, researches that determine human structure and cultural sensitiveness fields in the management of wildlife resources are very important. This article includes hunting clubs and their members in Istanbul. For this aim, according to sample size, there were reached primary data using questionnaire method which is prepared for hunters. Some data based on literature research constitute secondary data of research. In the research, questionnaire form; �cultural elements of hunters, profile of hunters and level of importance on the issue of hunters were determined. Nine-point Likert Scale was used in some questions to determine hunters’ thinking on the issue. The hunter form was designed in three sections. The first part is related to the demographic characteristics of the hunters. The second part reflects culture of Hunters’ Associations. In the last part; the levels of the perception of the cultural components of the hunters have been included. In this study, values forming culture of hunters were investigated in context of leaders and heroes, ceremony and symbols, story and legends, language, customs, norms and organizational socialization. Results of research project will be used as basic data in controlling and training of hunters, providing coordination and planning and sources. Keywords: Hunting, Hunting Culture, Hunting and Wildlife Management, Istanbul, Turkey. Introduction In the past, wildlife sources played an important role for livelihood and development of communities. In this period, hunting and wildlife is used as nutrient (meat), clothing material (leather and fur), rope and thread (sinews and musculocutaneous nerves), fuel (fats), decorative ware (antler, tooth), beverage container (horns), musical instrument (skin, horn), weapon (bone) etc. (Mbaiwa, 2002). As a matter of fact, in many countries even today, people still make use of most of these benefits including nutritional element. Hunting and Wildlife resources managers are faced with a situation such as to meet hunting demands of a section of society without any harm to the environment and by improving the existence of hunting and wildlife. While doing all these things there is an obligation to act in accordance with the Principle of Sustainability. Resource management must ensure coordination between nature (ecosystems), hunters and the institutions managing these resources so as to be able to complete this process successfully. Planning by ignoring the nature which offers hunting and wildlife resources or by not taking hunters into consideration, will result in failure. Hunters should take their place in the organization of hunting and wildlife, thus take on important tasks in combating with poaching and illegal hunting. Planning hunting in Turkey and increasing the contribution of hunting to the national economy depend on the hunters taking place in the organization in a conscious and systematic way. Also, the first way of achieving this is to know the hunters together with their various aspects. In Turkey; studies conducted in relation to hunting and wildlife focus on the biology of game and wild animals and hunting methods. Studies on the social dimensions of hunting and wildlife management have not been dealt with adequately. However, the continuous decrease in the presence of hunting and wildlife day by day, efforts to make hunting and wildlife resources sustainable, cause attentions to focus on the users of these resources. In fact, discussions on the management of hunting and wildlife in Turkey gained momentum in the late 1990s. The workshop (İzmir 23-25th March 1999) focused on the theme of new �approaches in hunting and wildlife management and the final report of the relevant workshop prepared by Geray (1999) are very important in terms of hunting and wildlife management. Synergies gained in the workshop were also continued later on so that the law called 4915 Land Hunting Act, which was necessary for a legal solution to the problems encountered on hunting and wildlife management, was enacted in 2003. In the doctoral dissertation prepared by Iğırcık (2001) and entitled "Socio-Economic Analysis on the Development of the Hunting Potential of Turkey"; the importance of the sustainability in the hunting and wildlife resources management strategy was emphasized and the socioeconomic dimension of hunting was also given in comparison with other countries. Edited by Bora (2001) and published by General Directorate of National Parks and Game Wildlife, "The Book of Basic Education for Sustainable Hunting" forms the primary resource of training courses for the hunters. Also, researches on profile of the hunters, which have a very effective role in the use of game wildlife resources, have been carried out since 2002. For this purpose, 4 pieces of researches, aimed at determining the profile of hunters in the Aegean, Marmara, Eastern and South Eastern Regions, were encountered. Elbek et al. (2002) conducted a study to determine the profile of the land hunters in the Aegean Region. Also, Ay et al. (2005) determined the profile of the hunters who had hunting cards in the Aegean region. Iğırcık et al (2005) investigated the profile of the hunters in the Marmara Region. Fidan et al (2007) interviewed with the land hunters in a total of 16 provinces including 8 cities representing the hunters and huntees in the Eastern Anatolia Region and 8 cities representing the hunters and huntees in the South-eastern Anatolia Region. Bora (2002) announced the results of the survey carried out for the hunters participating in hunter training courses. In Özbay (2006) it was aimed to create a glossary of Elazığ Territory Hunting Terminology by compiling the terms hunters used between each other as a rapid communication medium and which have specific meanings and by analyzing them with regards to structure and origin. In Şafak (2006a) a research was also applied on 96 hunters on İzmir-scale in order to determine the organization culture. Also, in Şafak (2006b) the issue of conflict management in hunting was investigated. Within the scope of this research, “conflicts” that might be subject to the management of game wildlife resources by taking hunters' associations in the province of Izmir and the registered hunters of these associations as an example.In Oğurlu (2008), place of wildlife resources in the economy, the need for resource planning, resource planning method and how to create a model of wildlife management plan unique to Turkey were discussed and the most remarkable major political and administrative errors in today's wildlife management and current managerial problems were mentioned. Also, in Çetinkaya (2010), hunting and hunting provisions in Islamic Fiqh (jurisprudence) were analyzed in terms of Quran, Sunni and different opinions of Islamic sects and the provisions Islam religion presented were tried to be determined. In recent years, the cultural dimension is considered to be very important in terms of management. The fact that "Hunting in Turkish Culture" themed International symposium was held between 15 to 16th November 2006 by the Marmara University Turkic Studies Research and Application Center also reveals research dimension of the subject matter. This symposium shortly aimed to examine and document hunting forms in our culture, hunting regime, kinds of the animals hunted, which animals are hunted in which regions, huntingrelated traditions, beliefs, legal regulations, prohibitions, hunting in literature and etymology of hunting-related words. The culture of the hunter is the total of the results of the beliefs, values, feelings and thoughts, manners and customs created by certain human communities together with the results of �relations between other people; which affect the outcomes of the ways and activities of hunting. The basic elements of the culture of the hunter; can be grouped under the headings of the values, leaders and heroes, rituals and symbols, stories and legends, language and manners and norms. The culture of the hunter gives a distinct identity to the hunters, helps them latch on to hunting and is shared by the hunters (Şafak, 2009). In Geray and Iğırcık (2002), it is indicated that the culture of hunting in Turkey has not yet been established and one of the most important threats to wild animals also comes from the hunters themselves. Once again, it is emphasized that the vast majority of hunters do not have any idea about the animals they hunt, that without obeying any of the rules they can fecklessly hunt wild animals that come their way; of all types and all ages. In the same publication it is expressed that this attitude the hunters have towards game animals and wildlife resources needs to change and it is obligatory that they should adopt new values to become an environmentally-conscious community (Geray and Iğırcık, 2002). Studies are required in order to identify the cultural characteristics of the hunters in Turkey. Most of the problems arise from not knowing the human nature and areas of cultural sensitivity. The Directorate of Nature Conservation and National Parks needs to be aware of the cultural characteristics for an effective and efficient resource management. When training, rewarding, punishing the hunters, when making them peaceful and happy and in efficient management the share of the knowledge of cultural features, in other words, the knowledge of mental, and psychological structure is of great importance (Şafak, 2009). If hunting and wildlife management problems analyzed in terms of their causes, and solution methods, these problems seems to be in a very close relationship with the cultural structure of the society. Indeed, in this study, the hunters in the province of Istanbul are determined by considering them within the scope of cultural characteristics, values, leaders and heroes, rituals and symbols, stories and legends, language, manners and norms and organizational socialization. The aim of this study is to support the creation of a culture oriented and effective strategy by putting forward the recognition of the hunters as of their cultural features so as to protect, develop and attain sustainable management of the hunting and wildlife resources. Material and Method There are 80 hunters' associations in the province of Istanbul. This study has been carried out in 14 of hunters’ associations which are effectively active in Istanbul such as Bakırköy, Beykoz, Büyükçekmece, Çatalca, Kadıköy, Kartal, Kazasker, Kemerburgaz, Silivri, Sultangazi, Şile Oruçoğlu, Ümraniye and Zeytinburnu associations. The data presented in this study have been obtained from field studies carried out together with the managers of the hunter associations and the hunters. In this context, survey method which is considered to be a systematic data collection technique has been utilized. The questionnaire form consists of three sections. In the first part; demographic characteristics of the hunters, in the second part; choices such as personal tendencies of the hunters, hunting ground, and type and frequency of hunting and also in the last part; the levels of the perception of the cultural components of the hunters have been included. Within the scope of the cultural components, the importance the hunters attach to the issues such as values, leaders and heroes, rituals and symbols, stories and legends, language, manners and norms and organizational socialization has been questioned. �For some of the questions which would take place in the hunters' questionnaire form, the Nine-point Likert Scale was used in order to determine the importance the hunters attach to the relevant issues. This scale is described in Figure 1. Their meanings in the Scale are; 1. Of very little Importance, 3. Of little importance, 5. Moderately important, 7. Very Important and 9. Highly important. 2, 4, 6 and 8 are used as preceding and subsequent mid-point of the degrees of importance. Figure 1 Nine-point Likert Scale Of very little Importance Of little importance Moderately important Very Important Highly important Findings Demographic Findings with reference to the hunters The average age of hunters participating in the study in the province of Istanbul is 49.9. As can be seen in Table 1, 54.4% of the hunters are above the age of 50 and approximately 34% of those are in the 35-49 age range. When it comes to the distribution of occupation of the hunters, 33% of the hunters are the artisans, 9.7% are the workers, 8.7% are the drivers and 8.7% are the farmers (Table 2). The professions in the "others" group include biologists, the hospital managers, the fireman, accountants, and the self-employment. On the other hand 49.5% , approximately half, of the hunters are retired. When occupational groups and age distribution are paired with, the state of having ability to create leisure times and having the opportunity to act freely are thought to be effective in cases where individuals turn towards hunting. Because, occupational groups such as artisans, workers, farmers, being at the forefront and nearly half of the participants in the survey being retired people draw this conclusion. Table 1: Age Distribution of the Hunters Age Groups % 18-29 1,9 30-34 9,7 35-49 33,9 50-59 28,2 60 and above 26,1 Table 2: Distribution of Hunters by Occupational Groups Occupational Group Farmers Workers Students Artisans Officers Drivers Engineer Technicians Teachers Others % 8,7 9,7 1,0 33,0 4,9 8,7 5,8 4,9 1,9 21,4 �International Conference on Economic and Social Studies (ICESoS’13), 10-11 May, 2013, Sarajevo On the other hand, the lower ranks of teachers and students can be explained by their lifestyles not being suitable for hunting. However there are no female hunters who participated in the survey. All of the questionnaires were made with male hunters. Table 3: Television channels watched (related to hunting) TV Channel Name Yaban TV Chasse&Peche Documentary Channels I do not watch % 86,4 13,6 7,8 7,8 Hunters participated in the study were seen to prefer visual media usually related to hunting and wildlife as field of training and communication. In Table 3, it can be seen that whether hunters follow TV channels about hunting and wildlife or not. Accordingly, percentage of those who do not follow any channels is 7.8%. Therefore, approximately 92% of the surveyed hunters were confirmed to follow visual media related to hunting and wildlife. In the area the survey was conducted 86.4% portion of the hunters follow Yaban TV, one of the national TV channels in Turkey. Within the documentary channels, TRT Documentary, Chasse & Peche, National Geographic, Discovery, Animal Planet, Toprak TV, and Köy TV are preferred. At all associations where the survey was made it was observed that that one of the mentioned channels was absolutely open. On the other hand, it was also observed that magazines about hunting were followed. Also, it was seen that there are library sections at some hunters' associations. Findings in Concern with the Association Which Hunters are affiliated to and the Culture Maintained in that Association Table 4 shows association membership period of the hunters. Accordingly, mean membership duration of hunters' association is 20,3 years. 58,2 % of the hunters are the members of a hunters' association for more than 15 years. Zeytinburnu Hunting and Shooting Sports Club Association have the longest membership duration of hunters' association of 65 years. On the other hand, Arnavutköy Hunters Shooters and Amateur Fishers Club Association have the shortest membership duration of 1 year. Table 4: Membership duration of hunters' association Membership period 1-5 year (s) less than 6-10 years 11-15 years 16-20 21-25 more than 25 years % 17,5 14,6 9,7 14,6 15,5 28,1 Whereas 63.1% association members know the establishment story of the relevant association, 36.9% do not. In Table 5 the hunters were asked why they had become a member of the association and they are asked to tell their 3 most preferred choices for the 6 �International Conference on Economic and Social Studies (ICESoS’13), 10-11 May, 2013, Sarajevo membership. % 75,7 of the hunters stated that they became a member of the association since they believed in "the necessity that the hunters should be organized". Accordingly, "hunters' association should schedule various hunting programs" is in the second place (35.0%), also for the hunters "the desire to receive training in hunting" is in the third place (30.1%). Besides, the associations were observed to be the environments where the hunters are socialized and where they exchange information on various topics. Table 5: Reasons for Being a Member of the Hunters' Association Reasons for Being a Member of the Hunters' Association Since I believe that the hunters should be organized Since it schedules various hunting programs To receive training in hunting To buy a hunting license at a cheaper price No particular reason To take advantage of opportunities such as clubhouse and amusement arcade Others % 75,7 35,0 30,1 15,5 10,7 8,7 16,5 According to the 74.8% of the hunters who are the current members of the association, the relevant association refers to a location where they meet with their friends. Other reasons are presented in Table 6. Accordingly, "a location where they can hand down to the next generations of the culture of hunter " is in the second place (51.5%), also "conscious hunting" is in the third place (45.6%). Table 6: Meaning of the Hunter Association Which Hunters are affiliated to The things Hunters' Association refers to A location where I meet with my friends A location where the culture of hunter will be handed down to the next generations Conscious hunting Love of nature A place full of my memories Training and application centre for the hunters A place where I spend my free times Dues payments and the bureaucracy Other (Specify) % 74,8 51,5 45,6 32,0 26,2 18,4 16,5 4,9 2,9 When Table 5 and Table 6 are taken together, it was seen that the hunters usually get together at the associations they are affiliated to, exchange information on all matters and they socialize at the same time. Among the ones listed in table 7, the objectives the associations the hunters are affiliated with attach importance to have been scored by the hunters via 1-9 scale. Accordingly, preferences of "to help members fulfill bureaucratic procedures such as hunting permit and hunting license", "to provide solidarity and cohesion among hunters" and "to ensure gathering of the local hunters under a single roof" got the highest scores. There is a contradiction between “Dues payments and the bureaucracy" option, which was preferred by 4,9% in Table 6, and the one that is on the first rank here. In Table 7 it can be seen that whereas hunters ignore "dues payments" title so as to be affiliated with the hunters' associations, the associations make a lot of effort in this regard. The fact that associations are recently entitled to get a share in dues payments to be transferred to public institutions 7 �International Conference on Economic and Social Studies (ICESoS’13), 10-11 May, 2013, Sarajevo is thought to be among the main reasons for this situation. Besides, we can say that associations less care about sociological activities or objectives. For example, "Social activities for hunting, carrying out hunting organizations", option got the minimum score. Similarly, “carrying out studies for the care, protection and reproduction of the game and wildlife animals", “ensuring that new generations teach hunting" and “combating hunters who hunt in an irregular and illegal way” options are scored less than the others. Table 7: The Objectives the Hunters'Associations The Objectives of the Hunters’ Association Carrying out studies for the care, protection and reproduction of the game and wildlife animals Ensuring that new generations learn hunting To ensure gathering of the local hunters under a single roof To provide solidarity and cohesion among hunters To help members fulfil bureaucratic procedures such as hunting permit and hunting license To ensure hunters hunt lawfully Carrying out social activities and hunting organizations Combating hunters who hunt in an irregular and illegal way Help hunters receive training in hunting Importance score 6,3 6,3 7,1 7,2 8,2 7,2 6,0 6,4 6,6 According to the hunters the strengths of the Hunters' Association they are affiliated to be shown in Table 8. Accordingly, "game chats and banter", "love of nature", "making friends", "sharing the game animals" are the prominent headings. None the less, it is possible to say that the hunters are in good condition in the fields of helping and supporting each other. Table 8: Strengths of the Culture at the Hunters’ Association Powerful Components of the Culture Importance score Game chats and banter Love of nature, conscious hunting, environmental awareness, nature conservation Making friends with the hunters of all ages and social classes Affection and respect Hunting with group of friends and sharing the game animals Friendship and fraternity Continuous implementation of hunting from past to present Participating in the activities of solidarity such as hunting festival, banquet and Islamic rituals and ceremonies Participate in the special days, wedding and engagement of the members and sending flowers Sharing members' problems such as illness, death, economic hardships and so on Exhibiting the pictures of the members Going on a hunt organized by the association management Exhibiting mounted game animals 7,8 7,7 7,6 7,5 7,5 7,4 7,4 6,8 6,7 6,5 5,9 5,4 4,0 When the hunters are asked whether there is someone they consider as a leader or they hailed as a hero among themselves, current president of the association is highlighted. 8 �International Conference on Economic and Social Studies (ICESoS’13), 10-11 May, 2013, Sarajevo Association's president, who appears to be the leader in Table 9, has been put forward by 44%. In fact, the question asked has not fully served the purpose. The perception in answering the question has turned towards the principle of the association who has the ability to represent. On the other hand, it was expressed that at some of the associations, there were also prominent members who came to the forefront with the leadership they showed during the hunting activities. Table 9: Leaders or Heroes at Hunters’ Association Leaders or Heroes Current president of the association Some of the members One of the members of the Board of Directors of the Association Former president of the association I have no idea No, there aren’t any % 43,8 17,9 8,0 5,3 10,7 14,3 In Table 10, the hunters were asked about the images expressing the cultural bond they established with the associations they were affiliated. Accordingly, the vast majority of the associations do not have an emblem. In these emblems, often components of the nature such as dogs, rifles, hunters, birds, lakes and mountains are used. Of about 80% of the associations do not have a standard outfit. Also, the majority of the "Yes, respondents" assumed that as a dress. Associations do not usually have a special form of salutation. It was seen that when hunters quit hunting, a special ceremony is held mostly for the members who gained a place in the association (% 40). Substantially, an event is organized at season openings. Table 10: Cultural Images and Activities Reflecting the Hunters' Association Preferred Images and Cultural Activities Yes No I have no idea % % % 65 28,2 6,8 Standard Outfit 15,5 77,7 6,8 Salutation 23,3 69,9 6,8 Common Terms and Concepts 13,6 59,2 27,2 Holding a Jubilee Ceremony for Those Who Quit Hunting 35,9 45,6 18,5 Organizing events such as banquet, entertainment, etc. at seasonal openings or closings 84,40 10,70 4,9 Badges, Flags and Emblems The hunters were asked about the sort of activities organized for the newly joined members at the beginning. According to Table 11; it is seen that introduction/acquaintance response comes forward. On the other hand, "shooting techniques", "weapons maintenance" and "hunting techniques" altogether are significantly answered in relation to hunting. 9 �International Conference on Economic and Social Studies (ICESoS’13), 10-11 May, 2013, Sarajevo Table 11: Activities for Your Newly Joined Members Name of the Event We are introducing our friends We are teaching dog care and training We are teaching hunting techniques We are giving first aid training We provide information about gun care Nature conservation Respect for elders We are teaching shooting techniques We do not organize any events I have no idea % 57,3 13,6 30,1 9,7 16,5 23,3 21,4 21,4 10,7 16,5 Findings on Hunting Culture Surveyed hunters answered the question "What is the motive that sparked your interest in hunting?" by choosing alternatives on 1-9 scale. According to Table 12 it is understood that the most important motivational tool was "love of nature". The response "motive to be with friends" takes the second place and the response “physical activity" is also in the third row. Preferences of "to provide economic benefits" and "to get nutrients" appeared in the bottom rows. "Sense of shooting” and “motive of animal hunting" were in the middle rows of the table. Both the options “to provide economic benefits” and “to get nutrients" got highly low points. This is the point to be considered. Also, the score of "animal hunting" option is in the middle ranks. This situation can be interpreted as a transformation in hunting. Hunting has transformed from absolute activity of animal hunting to a form of recreation. Looking at the results; "love of nature", "being together with "friends", and “physical act" scored greater. As a matter of fact, the observation of the hunting styles of the hunters hunting out with group of friends accompanied by food and drinks, supports this finding. Table 12: The Factors Effective in Sparking Interest in Hunting Factor In the period you first started hunting Love of nature Being together with my friends Physical act Animal hunting Shooting To be alone in nature To get nutrients To provide economic benefits 7,5 7,3 6,5 5,6 4,8 3,8 2,2 1,5 Now 8,7 7,9 8,0 4,2 4,1 4,3 2,0 1,6 When asked about the change of these factors pushing people to hunt in the course of time, in other words, when looked into "whether or not there are changes in motivational tools which are effective in hunting as of the first day when they go hunting and today"; significant changes has not been determined. Just in time, the senses of animal hunting and shooting are (albeit small) on the decline. 10 �International Conference on Economic and Social Studies (ICESoS’13), 10-11 May, 2013, Sarajevo It has been determined that a large portion of hunters, nearly 71.8%, often wants to go hunting with the same group of friends (Table 13). The rate of those who are open to new friendships is 31%. Considered together with the results of the above table, hunters often prefer to go on the hunt as friends and to socialize by doing so. Table 13: Making New Friends for Hunting New-friendship-relations In general, I go hunting with my same friend I prefer new friends even a little I prefer new friends a lot It does not matter with whom I go on hunting % 71,8 31,1 8,7 8,7 A large portion of the hunters surveyed does not have any belief that they consider that it brings good luck before or after the game. As can be seen in Table 14, 89% of the respondents answered "no" to this question. Similarly, a large portion of the hunters surveyed does not have any belief that they consider that it brings bad luck before or after the game. 93% of the respondents answered "no" to this question. Table 14: Superstitious Beliefs of the Hunters Superstition Do you have any beliefs that you consider that they bring good luck before or after the game? Do you have any beliefs that you consider that they bring bad luck before or after the game? Yes (%) No (%) 10,7 89,3 6,8 93,2 In Table 15 various thoughts of the hunters about hunting activity are included. Hunters stated their ideas on this subject by choosing alternatives on 1-9 scale. Looking at the importance scores in Table 14, it is seen that hunters enjoy talking about the things done in the previous game. Similarly, shooting related issues are also among the outstanding conversation pieces. Escape of the game animals, return home empty-handed, unsuccessful hunting activities are expressed as the situations which do not bother hunters. On the other hand, it was observed that the hunters do not have a certain feeling about envying the friends who realize a successful hunt. 11 �International Conference on Economic and Social Studies (ICESoS’13), 10-11 May, 2013, Sarajevo Table 15: Thoughts on the Hunting Activity Topics Importance Score To what extent do you talk about the things you did in the previous game in your conversations with the hunters? To what extent do you talk about the issues in relation to handing down to the next generations of the natural resources in your conversations with the hunters? To what extent do you talk about weapons, sighting and shooting techniques in your conversations with the hunters? To what extent do you question the negative hunts you did in the past? 7,5 How much does it upset you when the game animal escapes? 3,3 How much does it bother you to return home empty-handed after the game? 2,5 How much do you like to show the animals you shot to your relatives after the game? How much do you like to fill the legal limits at the end of the hunting? 3,3 What is your level of jealousy during hunting? 2,0 What is the priority of your passion for hunting in your social life? 6,2 To what extent do unsuccessful hunts affect you psychologically? 2,5 How much support does your spouse or family provide you for hunting? 4,9 How much do you like if your child or grandchild goes hunting? 7,3 How much will photo hunting, which is carried out by a camera instead of the rifle hunting, become widespread in future? 5,0 6,7 6,3 5,8 4,9 Hunters surveyed seemed to be willing about their children or grandchildren becoming hunters. However, it has clearly been determined that hunting prevents their social life from time to time and consequently the families do not support the hunters. Conclusion It is obvious that game and wildlife resources management in Turkey faces with a mission such as restoring the hunting activity with the permanent principles, norms and values. At present, the situation of the game and wildlife assets makes it a priority to propose this solution offer. Not only the hunters but also those who manage the resource are required to comply with the mentioned paradigm shifting. In other words, those who manage resources in question and those who carry out the activity of hunting must be integrated. The best way to accomplish this is to let hunters participate in game and wildlife management. Therefore, the hunters should primarily be recognized with their all value judgments. Without having information about social, economic and cultural structures of the hunters who want to take advantage of hunting and wildlife resources, it is not possible to plan hunting and to create effective strategies on a going forward basis. 12 �International Conference on Economic and Social Studies (ICESoS’13), 10-11 May, 2013, Sarajevo In this study, with the data obtained from questionnaire study carried out face to face in Marmara Region of Turkey distinguished in terms of industrialization and natural resources and in the most important province of Turkey, the cultural state of the territorial hunters has been revealed. The size of Istanbul in terms of population and industrialization necessitated the changes in the socio-cultural characteristics of the hunters. In comparison with the similar studies carried out in Anatolia it is seen that this study differs from the other ones. References Ay, Z., Bilgin, F., Şafak, İ., Akkaş, M., E. (2005). Ege Bölgesinde Avlanma Kartı Olan Avcıların Profilinin Belirlenmesi. Ege Ormancılık Araştırma Müdürlüğü, Teknik Bülten No: 27, Müdürlük Yayın No:36, İzmir, 39 s. Bora, M., E. (2001). Avcılığın Kısa Tarihi ve Özü, Sürdürülebilir Avcılık İçin Temel Eğitim Kitabı. Orman Bakanlığı Milli Parklar ve Av-Yaban Hayatı Genel Müdürlüğü Eğitim yayınları, Ankara. Bora, M. E., (Editör), (2002). Sürdürülebilir Av ve Yaban Hayatına Doğru. Orman Bakanlığı MilliParklar ve Av-Yaban Hayatı Genel Müdürlüğü, 384 s. Ankara. Çetinkaya, Ö. (2010). İslam Fıkhında Avcılık ve Av Hükümleri. Marmara Üniversitesi Sosyal Bilimler Enstitüsü Yüksek Lisans Tezi, 142s. Elbek, A. G., Uysal, İ., Saygı, H., (2002). Ege Bölgesi’ndeki Kara Avcı Profilinin Belirlenmesi Üzerine Bir Araştırma, 30 s. İzmir. Fidan, C., Orhan, C., Yalçın, Ö., Duran, C., Güldaş, N., Çelik, H. (2007). Doğu ve Güneydoğu Anadolu Bölgesi Kara Avcılarının Profilinin Saptanması. Güneydoğu Anadolu Ormancılık Araştırma Müdürlüğü, Yayın No :19, Teknik Bülten No: 11, Elazığ, 69s. Geray, A. U. (1999). Av ve Yaban Hayatı Yönetiminde Yeni Yaklaşımla İlgili Eğitim Workshop’u Raporu, Orman Mühendisliği Dergisi, Yıl 36, S. 6. Geray, A.U., Iğırcık, M. (2002). Türkiye’de Av Yaban Hayatı Eğitimi. Orman Mühendisliği Dergisi, Yıl 39, S. 8, 5-10. Iğırcık, M. (2001). Türkiye'nin Av Potansiyelinin Geliştirilmesine İlişkin Sosyo Ekonomik Çözümleme. İ.Ü. Fen Bilimleri Enstitüsü (Basılmamış Doktora Tezi), İstanbul. Iğırcık, M., Yadigar, S., Bekiroğlu, S., Okan, T., Akkaş, M. E. (2005). Marmara Bölgesi Avcı Profili. Ege Ormancılık Araştırma Müdürlüğü, Teknik Bülten No: 29, Müdürlük Yayın No:38, İzmir, 27 s. Mbaiwa, J.E. (2002). Past and Present Perspectives on the Sustainable Use of Wildlife Resources Among Basarwa Communities in Ngamiland District, Bostwana: The case of Khwai and Mabade. Bostwana Journal of African Studies, 16: 110-122. 13 �International Conference on Economic and Social Studies (ICESoS’13), 10-11 May, 2013, Sarajevo Oğurlu, İ. (2008). Yaban Hayatı Kaynaklarımızın Yönetimi Üzerine. SDÜ Orman Fakültesi Dergisi, Sayı:2, 35-88. Özbay, E. (2006). Elazığ Yöresi Avcılık Terimleri Sözlüğü. Fırat Üniversitesi Sosyal Bilimler Enstitüsü Yüksek Lisans Tezi, 143s. Şafak, İ. (2006a). Av ve Yaban Hayatı Yönetiminde Avcıların Kültürel Özellikleri, Ormancılıkta Sosyo-Ekonomik Sorunlar Kongresi (26-28 Mayıs 2006), Çankırı, s:127-133. Şafak, İ. (2006b). Avcılıkta Çatışma Yönetimi (İzmir İli Örneği), SDÜ Orman Fakültesi Dergisi,Seri: A, Sayı:1, 46-61. Şafak, İ. (2009). Avcı Derneklerine Üye Avcıların Kültürel Özellikleri (İzmir İli Örneği). Acta Turcica, Yıl 1, Sayı 1, 327-343. Acknowledgement In this study, intermediate results of the TUBİTAK (Scientific and Technological Research Council of Turkey) project, called "Determination of Cultural Characteristics and Profile of Hunters (Balıkesir, Bursa and Istanbul Provinces)” and numbered 111K519, were used. We would like to thank TUBİTAK for supporting this project. 14 � 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. 1597 Title A name given to the resource Determination of Cultural Characteristics of Hunters in İstanbul Province to Hunting and Wildlife Management (İstanbul Province Case) Author Author SAFAK, Ismail OKAN, Taner HIZAL, Erdem KECECIOGLU, Tamer ISIK, Caner ACAR, Sedat Abstract A summary of the resource. Turkey has a rather important potential from the point of view of wildlife resources which includes both species diversity and sheltering capacity. On the other hand, wildlife in Turkey has been endangered since wilderness ecosystem and habitats have been damaged and managed badly. There is a rather important role of hunters in this process. Hunting, which means catching of either living or lifeless animals that live freely in the nature, has attracted attention of mankind almost in every term. Hunting has been accepted as an important activity also today as well as in the past. It is not a realistic approach considering the hunting passion which comes to today with the traces from the past as nothing. Today, hunters have become a guaranty of biodiversity and wilderness by getting an environmentalist status. As increasing economic and social welfare, first of all demands of hunters and demands of stakeholders from wildlife sector are getting increased by varying. In this context, it is obligatory to develop and reach a rationalist management of wildlife potential that promises a great future of Turkey. In Turkey, lawful regulations were made in 2003 with act of 4915 to solve problems of wildlife management. Wildlife management in Turkey still live a transition period, problems are going on because new laws and its implementations have not yet established. Among these problems, illegal hunting, uneducated hunters, lack of hunter controlling have important place; basic problems are social, cultural, economic and lack of scientific structure. Associations, institutions and enterprises attach importance for knowing cultural elements and factors that affect them. It has been use an active instrument to convey cultural data and to reach aims. In this context, researches that determine human structure and cultural sensitiveness fields in the management of wildlife resources are very important. There have been seen some researches which related to determine of hunter profile since 2002. But, there are too few studies to determine cultural characteristics of hunters. In this context, it is needed some studies for hunter culture as related to management activities. This research project includes hunting clubs and their members in İstanbul. For this aim, according to sample size, there will be reached primary data using questionnaire method which is prepared for hunters. Some data based on literature research constitute secondary data of research. In this research, questionnaire, called the hunter form, will be developed. Hunter Form; cultural elements of hunters, profile of hunters and level of importance on the issue of hunters were determined. Nine-point Likert Scale was used in some questions to determine hunters’ thinking on the issue. The hunter form is designed in two parts. The first part is related to the demographic characteristics of hunters. The second section means detection levels of the components of the culture of hunters. In this research project, values forming culture of hunters were investigated in context of leaders and heroes, ceremony and symbols, story and legends, language, customs, norms and organizational socialization. Results of research project will be used as basic data in controlling and training of hunters, providing coordination and planning and sources. Data obtained from the hunters were evaluated using frequency, percentage, chi-square test, and Kruskal-Wallis one-way analysis of variance. In this context, there were benefited from SPSS program and others. Keywords: Hunting, Hunting Culture, Hunting and Wildlife Management, İstanbul. 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 2013-05-10 Keywords Keywords. Article PeerReviewed Identifier An unambiguous reference to the resource within a given context ISSN 978-9958-834-23-3