S TAT E P LA N N IN G OR G A N IZAT IO N

S TAT E P LA N N IN G OR G A N IZAT IO N D G FOR REGIO N A L D E V E LO P MENT A N D STRUCTUR AL A DJ USTMENT W O RKING PA PER AN ECONOMETRIC ANALYSIS OF SURVEY STUDY ON BILKENT CYBERPARK AND BATI AKDENIZ TEKNO PARK IN TURKEY UNPUBLISHED STUDY D ecember 4 th, 2009 K A M I L TA S C I P l a n n i n g S p e c i a l i s t

C o n t e n t s. INTRODUCTION... 2. THEORETICAL FRAMEWORK... 2 3. DATA SET... 3 3.. ANKARA CYBERPARK... 3 3.2. BATI AKDENIZ TECHNOPARK... 4 3.3. THE SCOPE OF THE SURVEY... 4 4. ECONOMETRIC ANALYSIS... 5 4.. SCIENTIFIC GOALS... 5 4.2. ECONOMETRIC MODEL... 5 4.3. BASIC ASSUMPTIONS FOR THE MODEL... 8 4.3.. Autocorrelation... 9 4.3.2. Non-Normality... 4.3.3. Hetoroskedasticity... 2 4.3.4. Endogeneity Test... 2 4.3.5. Multicollinearity... 3 4.4. VALIDATING ASSUMPTIONS... 4 4.4.. Heteroskedasticity and Non-Normality... 4 4.5. ANALYSIS OF RESULTS... 5 4.5.. General Evaluations of P-Values... 6 4.5.2. Do Revenues Increase according to Year in these Technoparks?... 6 4.5.3. Is there a Relationship between R&D Ratio and Added Value?... 7 4.5.4. How do Education Level and R&D Ratio together affect the Added Value?... 9 4.5.5. How Does Quality of Human Resources Influence Added Value?... 2 4.5.6. Are the Software Exporter Companies the More Productive?... 22 5. INTERPRETATION OF RESULTS... 23 6. REFERENCES... 26 APPENDIX : SURVEY FORMAT & SURVEYED FIRMS... 27 APPENDIX 2: OTHER REGRESSIONS... 30 APPENDIX 3: DATA... 32

T a b l e s Table : R&D Intensity in ICTs by Sub-components (Percent)... 2 Table 2 : Inventory of Collected Data from Survey Study... 5 Table 3 : Explanatory Variables of the Econometric Model... 6 Table 4 : Description of the Econometric Model... 7 Table 5 : The Growth of Revenues to Changing Year... 7 Table 6 : The Effects of R&D Ratio on Added Value (If Added Value > $75,000)... 8 Table 7 : The Effects of Quality of Human Resources and R&D Ratio on Added Value... 20 Table 8 : The Effects of Quality of Human Resources and R&D Ratio on Added Value (If Added Value > $5,000)... 20 Table 9 : The Comparison of Quality of Human Resources in between Bilkent Cyberpark and BaTech Technopark... 2 Table 0 : The Major Problems of Turkish Software Industry... 24 Table : Surveyed Firms... 28 The report is prepared by Kamil TASCI & Mehmet Emin OZSAN December4 th, 2009

. I N T R O D U C T I O N Turkey s IT market is estimated to reach 5.3 billion USD by the end of 2006, up from 2.2 billion in 200. It is anticipated that Turkey s software market will have reached US$ 2. billion USD in 2006, up from 95 million for 200. Turkish software industry s annual growth rate is 7.5 % in the period of 200-2006. It is worth mentioning that the Turkish software market has experienced double-digit growth over the past five years, in contrast to the USA and EU s singledigit numbers. The Turkish software industry is dynamic and fast developing, but it is not big as other developing countries such as India, Ireland, Israel, and China. The recently enacted law allowing the establishment and operation of technoparks and technological development zones with participation and leadership of universities is likely to give a boost to the sector. The software houses with promising capabilities will now be able to complete their institutionalization and growth processes in these techno-parks, by benefiting from significant tax and investment incentives provided by the government. However, the field of software did not much more attract the interest of academicians in Turkey. Therefore, number of academic studies regarding this sector is limited. In order to contribute formulation process of software industry policy using real firm-level data and examine Turkish Software Industry s added value potential and technoparks performance, a survey study was carried out by Kamil TASCI, Planning Specialist (Science and Technology Policy) in the State Planning Organization at Prime Ministry of Turkey, in the period of July-August 2006 as well as providing an asset for efforts of exploring the current situation of the software industry. In the scope of the study, annual revenue, research and development budget, the number of employee and education level data were collected for each firm for 2002-2006 period. In addition, the problems of the Turkish software industry were questioned. 46 out of 20 software companies in CyberPark, and 2 out of 9 software companies in Batı Akdeniz Technopark have responded the survey. In total 58 out of 39 software companies have provided reply for the survey. These returns can be considered as representative to above expectations regarding the fact that there are more than 200 software companies doing business in Turkey. However, the results of the survey study were not analyzed in an econometrical or statistical approach. In this context, the aim of the study is to analyze the relationship between added value, research and development, export performance, and human capital in the two technoparks, and to contribute policy formulation process regarding software-sector-specific R&D and education policies.

2. T H E O R E T I C A L F R A M E W O R K Software industry is an industry which requires an advanced level of information and is a capability-intensive one. Research and Development (R&D) and innovative product creation constitute the basis of production in software. The key competition in information intensive industries like software is innovation supported by R&D activities. The fact that software is a general purpose technology, allows the use of techniques needed by each sectors in this technology and realization of multipurpose production and distribution systems. Along with being an R&D intensive sector in itself software technologies can affect mathematical, technological, economic, artistic and cultural creativity based on human capital 2. The main criteria to distinguish R&D from other activities is that there needs to be a visible identifiable measurable innovation element and a systematic solution toward removing a scientific ad/or technological ambiguity in R&D activities 3. Software is the most R&D intensive industry supported by well-educated human capital within information and communication technologies. According to OECD Outlook 2006 companies with the highest R&D intensity among world s leading 250 ICT companies are operating in the field of software. As it is seen from Table 2.5., given below, the share of R&D allocated by software companies in company revenues had been 4.9% in 2000 and 2005. Same figure for communication appliances producing companies had been 2.% 4. Table : R&D Intensity in ICTs by Sub-components (Percent) Sub-Component 2000 2005 Telecommunication,8,4 Services 4,6 4,9 IT Equipments 6, 5,4 Electronic Components 6,3 7, Communication Equipments,3 2, Software 4,9 4,9 Source: OECD IT Outlook 2006 In the field of software, the cost of production development changes according to the product type and competitive position of company. R&D expenditures in companies, which produce package software, software procedures, component and tools and attain its revenue from licensing, are higher compared to that of companies in other sectors. For instance, IBM, attaining 60% of its revenues from software and related services, is ranked # among world s Godin, 2004:685 2 Mitchell at al, 2003:26 3 OECD and European Commision, 2002:46-47 4 OECD; 2006:4 2

software companies. IBM spent 5.8 billion dollars, 6% of its total revenues, for R&D activities. Microsoft, #2 in the rank, spent 7.7 billion dollars, 2% of its company revenues, for R&D activities in the same period5. Because IBM chooses service oriented software value chain, R&D share in its turnover is low compared to that of Microsoft, which attains its revenues mainly from package software and produces keystone technology. Information used in software production remains in the hands of producer, so no information loss takes place and this fact contributes the creation of new products. If they possess qualified human resources, developing countries in advanced technology production capability in a shorter period. Main common features of developing countries whose software industry and export activities have been developed like Brazil, India, Ireland, and Israel, is their qualified human resources6. One of the features sought by multinational technology companies for the R&D or operation center to be opened is the number of qualified labor force in that country. Developed and developing countries face with scarcity in human resources specialized in software7. Companies in industries based on intellectual capital seek to recruit highly qualified but low cost human resources. Companies sometimes directly import these human resources from abroad and sometimes they build laboratory and R&D centers in locations of human resources sought and they try to lower labor costs and benefit from knowledge accumulation in these regions8. To sum up, since software industry is knowledge based industry and its production activities mostly are based on R&D, quality of human capital directly influences firm-performance. 3. D A T A S E T Ankara CyberPark, the first one of the two selected technology development zones studied under the survey, is chosen because of its leading status among others in sheltering more software industry firms, and Batı Akdeniz Teknokent, the second one, is chosen because it demonstrates the most rapidly growth in the field of software industry. 3..A N K A R A C Y B E R P A R K Ankara CyberPark is founded in Bilkent University campus by Bilkent Holding. The CyberPark, founded in 2002, has become the most rapidly developing TDZ of Turkey in a short time. While there were 08 companies in total, 94 out of which were software companies as of July 2005, in September 2006, the total number of established companies reached 64 and number of software companies reached to 20. In Cyberpark, number of people employed is 5 Software Magazine, 2006 6 Heeks and Nicholson, 2002 7 Barr and Reilly, 2004:7 8 Aneesh, 2002 3

20 in total, 37 of them are working in R&D, and 874 of them are support personnel. Turkey s first private incubator center has been established in CyberPark. 20 companies are getting support from incubator center. In addition, more than 00 students are doing traineeships in the companies located at Cyberpark. There are 20 software firms located in the zone and this number equals to 73.2% of total number of companies in the zone. During the period of 2002-2005 420 technological products are developed in the zone. The total turnover of R&D companies located in the zone is expected to reach the amount of 600 million dollars as of 2006. 3.2.B A T I A K D E N I Z T E C H N O P A R K Batı Akdeniz Teknokent (Technopark) is founded in Akdeniz University campus in the city of Antalya in 2004. As of July 2005 there were 5 companies having business in the zone and 4 of them were software companies. In September 2006 number of total companies having business in the zone reached to 22 and number of software companies reached to 9. 90.9% of companies having an R&D branch in the Batı Akdeniz Teknopark are doing business in the field of software industry. It is expected that total amount of turnover of software companies located in the zone will get ahead of 8 million dollars as of the end of 2006, according to the survey results. 3.3.T H E S C O P E O F T H E S U R V E Y A one-to-one communication is conducted with managers of 32 software companies located at Cyberpark and Batı Akdeniz technoparks, and a survey questionnaire given in the annex is sent them via electronic means. The information pursued in the questionnaire were their 5 year development performances, their employment profile, educational profile of their employees, annual wage paid for their software personnel, their relation with R&D and innovation, quality level of the company, their exports level and destinations. Within the frame of survey it is also requested from companies to list the 5 most important problems of the software industry. 46 firms from Bilkent Cyberpark and 2 firms from Bati Akdeniz Technopark provided total 92 observations that belong to the period of 2002-2006. The data descriptions of the survey are the following: 4

Table 2 : Inventory of Collected Data from Survey Study (the number of observations) 2002 2003 2004 2005 2006 Total The number of Companies 24 32 35 44 57 92 City (Ankara, Istanbul, Antalya, Other) 24 32 35 44 57 92 Total Revenue 24 32 35 44 57 92 The Number of Staff 24 32 35 44 57 92 The Number of Software Staff 23 29 32 42 55 8 Staff Education Level (Col., Bach., MS, PhD) 0 0 0 0 55 55 R&D Expenditure 7 23 28 38 5 57 Software Quality Consideration (Answer: Yes) 3 3 4 4 24 38 Export Experience (Answer: Yes) 2 5 24 33 Total Added Value (Revenue per Staff) 24 32 35 44 57 92 R&D Ratio (R&D Share in Total Revenue) 7 23 28 38 5 57 Education Index (The mean education level among staff) 0 0 0 0 55 55 4. E C O N O M E T R I C A N A L Y S I S 4..S C I E N T I F I C G O A L S The scientific goals of the study are to research the answer of the following questions in Turkish s Technoparks case: Do Revenues Increase according to Year in these Technoparks? Is there a Relationship between R&D Ratio and Added Value? How do Education Level and R&D Ratio together affect the Added Value? How Does Quality of Human Resources Influence Added Value? Are the Software Exporter Companies More Productive than others? 4.2.E C O N O M E T R I C M O D E L According to collected data, the possible variables that might be used are described in the following table. 5

Table 3 : Explanatory Variables of the Econometric Model Variable Type Explanation company Text Compay Name tech_bilkent Dummy It measures If the company has R&D branch in Cyberpark tech_batech Dummy It measures If the company has R&D branch in BaTech city_ank Dummy It measures If the company is located in Ankara city_ist Dummy It measures If the company is located in Istanbul city_ant Dummy It measures If the company is located in Antalya city_oth Dummy It measures the company is located in other city frgn_share Dummy It measures If the company has foreign shareholder year Year Year revenue Number Annual revenue in given year staff Number Total staff in given year sw_staff Number Total software staff in given year rd_exp Number Research and Development expenditure in given year ed_col Number The number of staff who has college degree (2-year) ed_bac Number The number of staff who has Bachelor degree (4-year) ed_mas Number The number of staff who has Master degree ed_phd Number The number of staff who has PhD degree sw_quality Dummy It measures If the company has considered Software Quality models or standards ex_expr Dummy It measures If the company has Software Export experience addedvalue rd_ratio Number Number (%) It is total added value and measured by (Total Revenue /Total Staff) in given year It is R&D ratio and measured by (Total R&D Expenditure-Budget /Total Revenue) in given year ed_indx Number It is mean of education year of all staff in a company. Regarding the mentioned questions in the prior section, the general econometric model could be developed as follows: MODEL: Y ij x( ed _ indx) x( rd _ ratio) x( ex _ exp r) x( sw_ quality ) 4 x( tech _ bilkent ) 5 0 0 x( sw_ quality ) 0 4 x( tech _ batech ) 5 2 3 ij 0 x( ex _ exp r) 3 In the econometric model, there are five explanatory variables to predict the observations of dependent variable, Added Value of Firm in the given year. It is quantitative outcome and measured by $US dollar. 6

Table 4 : Description of the Econometric Model Component Yij : 0 ed_indx rd_ratio 2 ex_expr 3 sw_quality 4 tech_bilkent tech_batech 5 ij Description It is Added Value of Firm in the given year and dependent variable. It denotes added value of firm that is calculated by Annual Revenue/ the Number of Staff for software firm i th in year j. Intercept parameter. The independent variable denotes education index. ed_indx ij = Mean of Firm s Staff s Formal Education Year. ed_col : 2 years for college degree after high-school ed_bac : 4 years for bachelor degree after high-school ed_mas : 6 years for masters degree after high-school ed_phd : 0 years for PhD degree after high-school ed_indx ij = (2 x ed_col + 4 x ed_bac + 6 x ed_mas + 0xed_phd)/( ed_col+ed_bac+ed_mas+ed_phd) The coefficient for ed_indx variable The independent variable denotes R&D Expenditure Percentage Ratio of firm. It is calculated by R&D Expenditure / Annual Revenue The coefficient for rd_ratio variable Dummy variable denotes whether the company has export experience before nor not. The coefficient for dummy ex_expr variable. Dummy variable denotes whether the company considers Software Quality Standards such as CMM or ISO 2207. The coefficient for dummy sw_quality variable Dummy variable denotes whether the company is located in Bilkent Cyberpark or BATech Technopark nor not. The coefficient for dummy tech_bilkent and tech_batech variable The random error. According to the model, our null and alternative hypotheses are as follow: st Hypothesis: Ho: 0 Ha: 0 If p-value is greater than significance level of.05, then we accept the null hypothesis and may conclude that there is no linear relationship between Added Value (Yij) and (ed_indx), Education Level (Quality of Human Capital). 2 nd Hypothesis: Ho: 0 2 Ha: 0 2 7

If p-value is greater than significance level of.05, then we accept the null hypothesis and may conclude that there is no linear relationship between Added Value (Yij) and (rd_ratio), R&D expenditure. 3 rd Hypothesis Ho: 0 3 Ha: 0 3 if p-value is greater than significance level of.05, then we accept the null hypothesis and may conclude that there is no linear relationship between Added Value (Yij) and (ex_expr), Export Experience. Alternative hypothesis assumes that software-exporter companies are more productive because of intense competition in the global market. 4 th Hypothesis Ho: 0 4 Ha: 0 4 If p-value is greater than significance level of.05, then we accept the null hypothesis and may conclude that there is no linear relationship between Added Value (Yij) and (sw_quality), Software Quality consideration. Alternative hypothesis assumes that companies which are considering software quality models and standards are more productive. 5 th Hypothesis Ho: 0 5 Ha: 0 5 If p-value is greater than significance level of.05, then we accept the null hypothesis and may conclude that there is no linear relationship between Added Value (Yij) and (tech_bilkent) existing in the larger technopark or cluster. Alternative hypothesis assumes that companies which are located on larger technoparks are more productive because of benefitting from other advantages of that techno-cluster. 4.3.B A S I C A S S U M P T I O N S F O R T H E M O D E L It has been assumed that the regression model satisfies the assumptions of Ordinary Least Squares Estimators (OLS). These are; (i) The functional form is correct, (ii) There are no pertinent variables that have been omitted from the regression, 8

(iii) E( i ) = 0, X variable is non-stochastic and linearly independent, Properties Unbiased, in other words E( ˆ) Assumptions: i ~ N (0, σ 2 I) (normal, independent, homoskedastic) E(X, i ) = 0 (If X is random, then it is uncorrelated with the error) Properties:. Unbiased, ˆ ' 2. (, 2 ( X X ) ) ~ (β, σ2 (X X)-), 3. β is BLUE. Proof (known as Gauss-Markov Theorem): (i) The estimator is clearly linear in y. In other words, it can be written as Ay, (ii) Unbiased estimator, (iii)it should be illustrated that β has the least variance of any linear unbiased estimator.. reg addedvalue ed_indx rd_ratio ex_expr sw_quality tech_bilkent tech_batech Source SS df MS Number of obs = 5 -------------+------------------------------ F( 5, 45) =.6 Model 8.9675e+09 5.7935e+09 Prob > F = 0.756 Residual 4.9985e+0 45.08e+09 R-squared = 0.52 -------------+------------------------------ Adj R-squared = 0.0579 Total 5.8953e+0 50.79e+09 Root MSE = 33328 addedvalue Coef. Std. Err. t P> t [95% Conf. Interval] -------------+---------------------------------------------------------------- ed_indx 2638 600.437 2.07 0.044 35.0934 24924.92 rd_ratio -082.67 655.357 -.65 0.06-24007.78 2382.444 ex_expr -7372.657 9752.303-0.76 0.454-2704.8 2269.49 sw_quality 297.486 0292.69 0.2 0.832-8533.05 22928.02 tech_bilkent 660.75 3082.06 0.05 0.960-25687.88 27009.38 tech_batech (dropped) _cons 3976.448 2372.6 0.7 0.868-43783.2 5736. 4.3.. A u t o c o r r e l a t i o n This assumption asserts that error terms should be independent from each other. If COV( t t ) 0, then error terms are correlated and not independent. Durbin Watson test should be applied to search for the autocorrelation in the model. Regarding the test, null, and alternative hypotheses are given below: 9

Source: Gujarati (2004): pp.469-470 In order to calculate d-statistic, we may use dwstat command after setting time with tsset.. tsset t time variable: t, 0 to 9 delta: unit. dwstat Number of gaps in sample: 33 Durbin-Watson d-statistic( 6, 5) =.593277 According to K=6 (degrees of freedom) and T=5 (the number of observation) values, we may gain dl and du values from Durbin-Watson Test tables as follows: d d l u.335.77 Since d value is between zero and lower d value ( 0 d 0.59 d. 335 ), we can reject the null hypothesis (Ho: No Autocorrelation) may conclude that there is a positive (Ha:Positive Autocorrelation 0<d<dl condition) autocorrelation problem. This problem should be solved GLS transformation. l 0

4.3.2. N o n - N o r m a l i t y This assumption asserts that error terms are normally distributed. Jarque-Bera test should be used to detect normality assumption. According to above ordinary least square regression model, we may use the Jarque-Bera test. The formula of Jarque-Bera statistic is as follows. In the above formula n = sample size, S = skewness coefficient, and K = kurtosis coefficient. For a normally distributed variable, the Skewness and Kurtosis values should be S = 0 and K = 3. Hence, the JB test of normality is a test of the joint hypothesis that S and K are 0 and 3, respectively. In that case the value of the JB statistic is expected to be 0. At first, we need to create a variable for keeping residuals, then we need to take its power respectively 2,3,4 and to store in 3 other variables: e2, e3, and e4 in order to calculate Skewness (S) and Kurtosis (K) values. The STATA steps are shown as follows. JARQUE-BERA TEST. predict e, re (4 missing values generated). gen e2=e^2 (4 missing values generated). gen e3=e^3 (4 missing values generated). gen e4=e^4 (4 missing values generated). egen m2 = mean(e2). egen m3 = mean(e3). egen m4 = mean(e4). gen S = m3/m2^.5. gen K = m4/m2^2. gen jb = (_N/6)*(S^2+(K-3)^2/4). display S.3469647. display K 5.3052745. di jb 00.57237. display chiprob(5,jb ) 4.003e-20 In the Right-Tail Critical Values for the 2 Distribution table, chisquare value is.0705 for df=5 at alpha level 0.05 and chisquare value is 9.23635 for df=5 at alpha level 0.. Since our Jarque-Bera test result is 4.003 smaller than the mentioned values, we can not reject nonnormality null hypothesis (Ho: Non-Normality, Ha: Normal). Therefore we may conclude that there is non-normality problem in our model. We will solve this problem later using GLS method with nmk command.

4.3.3. H e t o r o s k e d a s t i c i t y In order to test whether variance of error terms are homoskedastic, we should use Breusch- Pagan test. Our null hypothesis is that the variance of error terms is homoskedastic if p-value for chi2 is greater than 0.05. If it is lower than 0.05, we can reject null hypothesis and conclude that the variance is heteroskedastic.. reg e2 ed_indx rd_ratio ex_expr sw_quality tech_bilkent tech_batech Source SS df MS Number of obs = 5 -------------+------------------------------ F( 5, 45) = 2.88 Model 5.092e+9 5.028e+9 Prob > F = 0.0245 Residual.5983e+20 45 3.557e+8 R-squared = 0.2422 -------------+------------------------------ Adj R-squared = 0.580 Total 2.092e+20 50 4.284e+8 Root MSE =.9e+09 e2 Coef. Std. Err. t P> t [95% Conf. Interval] -------------+---------------------------------------------------------------- ed_indx 2.22e+08 3.45e+08 0.64 0.523-4.73e+08 9.7e+08 rd_ratio -.67e+08 3.70e+08-0.45 0.655-9.3e+08 5.79e+08 ex_expr -.49e+09 5.5e+08-2.70 0.00-2.60e+09-3.77e+08 sw_quality -8.90e+08 5.82e+08 -.53 0.33-2.06e+09 2.82e+08 tech_bilkent.28e+09 7.40e+08.73 0.090-2.07e+08 2.77e+09 tech_batech (dropped) _cons 2.39e+08.34e+09 0.8 0.859-2.46e+09 2.94e+09. predict v (option xb assumed; fitted values) (4 missing values generated). hettest Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of e2 chi2() = 3.6 Prob > chi2 = 0.0000 As seen the above results, since p-value=0.00 is smaller than significance alpha level 0.05, we may reject to null hypothesis (Ho: Constant Variance). Hence, we may conclude that there is a heteroskesdasticity problem. 4.3.4. E n d o g e n e i t y T e s t If variables are jointly determined by other variables in the model, then we face endogeneity problem. Durbin-Wu-Hausman test is used to determine endogeneity. Null hypothesis is that there is no endogeneity in the model. In order the check if there is endogeneity problem in our model, we may use ivreg command in STATA. 2

. ivreg addedvalue ed_indx rd_ratio ex_expr sw_quality tech_bilkent Instrumental variables (2SLS) regression Source SS df MS Number of obs = 5 -------------+------------------------------ F( 5, 45) =.6 Model 8.9675e+09 5.7935e+09 Prob > F = 0.756 Residual 4.9985e+0 45.08e+09 R-squared = 0.52 -------------+------------------------------ Adj R-squared = 0.0579 Total 5.8953e+0 50.79e+09 Root MSE = 33328 addedvalue Coef. Std. Err. t P> t [95% Conf. Interval] -------------+---------------------------------------------------------------- ed_indx 2638 600.437 2.07 0.044 35.0934 24924.92 rd_ratio -082.67 655.357 -.65 0.06-24007.78 2382.444 ex_expr -7372.657 9752.303-0.76 0.454-2704.8 2269.49 sw_quality 297.486 0292.69 0.2 0.832-8533.05 22928.02 tech_bilkent 660.75 3082.06 0.05 0.960-25687.88 27009.38 _cons 3976.448 2372.6 0.7 0.868-43783.2 5736. (no endogenous regressors) According to the above results, we may conclude that there is no endogeneity problem. 4.3.5. M u l t i c o l l i n e a r i t y If two independent variables are highly correlated, then there is the problem of multicollinearity. Except for pair carrying out regressions between explanatory variable, this problem cannot be detected by three ways: () Check the correlation between variables: If two independent variables have a correlation coefficient greater than 0.8, then there may be a problem.. corr addedvalue ed_indx rd_ratio ex_expr sw_quality tech_bilkent (obs=5) addedv~e ed_indx rd_ratio ex_expr sw_qua~y tech_b~t -------------+------------------------------------------------------ addedvalue.0000 ed_indx 0.2750.0000 rd_ratio -0.734 0.2607.0000 ex_expr -0.684-0.0448 0.96.0000 sw_quality -0.0267-0.025 0.208 0.0497.0000 tech_bilkent 0.033 0.4095 0.490 0.3 0.369.0000 Since the each of correlation test results is lower than 0.8, we may conclude that there is no multicollinearity problem among the variables. (2) Variance Inflation Factors (VIF) method: After regression, we may use vif command to test possible multicollinearity problem. If a VIF value is larger than 20 or a tolerance value (/VIF) is smaller than 0.05, there might be a multicollinearity problem.. vif Variable VIF /VIF -------------+---------------------- tech_bilkent.4 0.707295 ed_indx.36 0.735840 sw_quality.20 0.830339 rd_ratio.9 0.84332 3

ex_expr.08 0.924908 -------------+---------------------- Mean VIF.25 According to above result, we can say that there is no multicollinearity problem in our model. (3) VCE, corr command: After regression, we may also use vce command to test possible multicollinearity problem. If a vce correlation is larger than 0.8, there might be a multicollinearity problem.. vce, corr Correlation matrix of coefficients of regress model e(v) ed_indx rd_ratio ex_expr sw_qua~y tech_b~t _cons -------------+------------------------------------------------------------ ed_indx.0000 rd_ratio -0.2836.0000 ex_expr 0.638-0.280.0000 sw_quality 0.278-0.2263 0.0583.0000 tech_bilkent -0.4453 0.0686-0.678-0.3558.0000 _cons -0.903 0.2296-0.272-0.2573 0.385.0000 Since the each of correlation test results is lower than 0.8, we may conclude that there is no multicollinearity problem among the variables. 4.4.V A L I D A T I N G A S S U M P T I O NS Since there are only heteroskedasticity and non-normality problem in our model, we would focus on these problems using some corrections. 4.4.. H e t e r o s k e d a s t i c i t y, A u t o c o r r e l a t i o n a n d N o n - N o r m a l i t y To overcome heteroskedasticity and autocorrelation problem in our model, we would use feasible GLS method using xtgls command. While using the method, we need to use nmk option to provide normality assumption as well. nmk defines that standard errors are to be normalized by N-k, where k is the number of parameters estimated, rather than N, the number of observations.. xtgls addedvalue ed_indx rd_ratio ex_expr ex_expr sw_quality sw_quality tech_bilkent tech_batech, nmk note: ex_expr dropped because of collinearity note: sw_quality dropped because of collinearity note: tech_batech dropped because of collinearity Cross-sectional time-series FGLS regression Coefficients: generalized least squares Panels: homoskedastic Correlation: no autocorrelation Estimated covariances = Number of obs = 5 Estimated autocorrelations = 0 Number of groups = Estimated coefficients = 6 Time periods = 5 4

Wald chi2(5) = 8.07 Log likelihood = -600.2966 Prob > chi2 = 0.522 addedvalue Coef. Std. Err. z P> z [95% Conf. Interval] -------------+---------------------------------------------------------------- ed_indx 2638 600.437 2.07 0.038 68.3674 24594.64 rd_ratio -082.67 655.357 -.65 0.099-23653.09 2027.757 ex_expr -7372.657 9752.303-0.76 0.450-26486.82 74.5 sw_quality 297.486 0292.69 0.2 0.83-7975.8 22370.78 tech_bilkent 660.75 3082.06 0.05 0.960-24979.62 2630.2 _cons 3976.448 2372.6 0.7 0.867-42499.4 50452.3 As seen from the above results, since our model has completed transformation using feasible GLS method, Homeskedasticity, No Autocorrelation and Normality assumptions have been satisfied. 4.5.A N A L Y S I S O F R E S U L T S When we run our model, we can the following result. Since education index parameters belong to only 2006, there 5 observations are taken into account, 5 of them are for explanatory variables.. xtgls addedvalue ed_indx rd_ratio ex_expr ex_expr sw_quality sw_quality tech_bilkent tech_batech, nmk note: ex_expr dropped because of collinearity note: sw_quality dropped because of collinearity note: tech_batech dropped because of collinearity Cross-sectional time-series FGLS regression Coefficients: generalized least squares Panels: homoskedastic Correlation: no autocorrelation Estimated covariances = Number of obs = 5 Estimated autocorrelations = 0 Number of groups = Estimated coefficients = 6 Time periods = 5 Wald chi2(5) = 8.07 Log likelihood = -600.2966 Prob > chi2 = 0.522 addedvalue Coef. Std. Err. z P> z [95% Conf. Interval] -------------+---------------------------------------------------------------- ed_indx 2638 600.437 2.07 0.038 68.3674 24594.64 rd_ratio -082.67 655.357 -.65 0.099-23653.09 2027.757 ex_expr -7372.657 9752.303-0.76 0.450-26486.82 74.5 sw_quality 297.486 0292.69 0.2 0.83-7975.8 22370.78 tech_bilkent 660.75 3082.06 0.05 0.960-24979.62 2630.2 _cons 3976.448 2372.6 0.7 0.867-42499.4 50452.3 Estimated Model: Yˆ ij 3976.448 2638*( ed _ indx) ( 082.67x( rd _ ratio) ( 7372.657) x( ex _ exp r) 297.486x( sw_ quality ) 660.75x( tech _ bilkent ) ij 5

Intercept parameter 0 =3976.448 The slope parameter for edu_indx = 2368 The slope parameter for rd_ratio 2 = (-082.67) The slope parameter for ex_expr (Export Experience) 3 =(-7372.657) The slope parameter for sw_quality (Software Quality) 4 =(297.586) The slope parameter for tech_bilkent (Bilkent Cyberpark) 5 =(660.75) 4.5.. G e n e r a l E v a l u a t i o n s o f P - V a l u e s st Hypothesis: Regarding the st hypothesis, p-value=0.038 is smaller than.05 significance level, we reject the null hypothesis and may conclude that there is a linear relationship between Added Value (Yij) and (ed_indx), Education Level (Quality of Human Capital). 2nd Hypothesis : Regarding the 2nd hypothesis, p-value=0.099 greater than.05 significance level, we accept the null hypothesis and may conclude that there is no a linear relationship between Added Value (Yij) and (rd_ratio), R&D_Expenditure. However, If we would use the significance alpha level 0.0, we reject the null hypothesis and may conclude that there is no a linear relationship between Added Value (Yij) and (rd_ratio), R&D_Expenditure. 3rd Hypothesis: Regarding the 3rd hypothesis, p-value=0.450 is greater than.05 significance level, we accept the null hypothesis and may conclude that there is no a linear relationship between Added Value (Yij) and (ex_expr), Export Experience. 4th Hypothesis: Regarding the 3rd hypothesis, p-value=0.83 is greater than.05 significance level, we accept the null hypothesis and may conclude that there is no a linear relationship between Added Value (Yij) and (sw_quality), Software Quality consideration. 5th Hypothesis: Regarding the 3rd hypothesis, p-value=0.960 is greater than.05 significance level, we accept the null hypothesis and may conclude that there is no a linear relationship between Added Value (Yij) and and (tech_bilkent) existing in the larger technopark or cluster. 4.5.2. D o R e v e n u e s I n c r e a s e a c c o r d i n g t o Y e a r i n t h e s e T e c h n o p a r k s? In order to find the answer of the question, we would use the following model. revenue i year ) 0 ( i ij Regarding the model, we would have the following table. 6

Table 5 : The Growth of Revenues to Changing Year Cumulative Bilkent CyberPark Bati Akdeniz Technopark Number of observations 92 45 47 Wald chi2(2) 3.64 2.27.58 Prob > chi2 0.0565* 0.37 0.208 YEAR Coefficient ( ) 35676. 4084.4 63444.84 SE 748.5 92994.38 50400.64 p-value 0.057* 0.32 0.208 *** Significant at alpha level 0.0. ** Significant at alpha level 0.05. * Significant at alpha level 0.0. As seen the above table, cumulatively the growth of revenue regarding the model increases as year increases. Since p-value of Wald-test (0.565) and p-value of year (0.057) are larger than significance alpha level 0.0, we may conclude that year has significant effect on revenue. As the year increases unit, the revenue of a company increases $35,676. 4.5.3. I s t h e r e a R e l a t i o n s h i p b e t w e e n R & D R a t i o a n d A d d e d V a l u e? In order to find the answer of the question, we would modify our model as follows. AddedValue ij x( rd _ ratio) 0 2 ij Regarding the model, we would have the following STATA results.. xtgls addedvalue rd_ratio Cross-sectional time-series FGLS regression Coefficients: generalized least squares Panels: homoskedastic Correlation: no autocorrelation Estimated covariances = Number of obs = 59 Estimated autocorrelations = 0 Number of groups = 5 Estimated coefficients = 2 Obs per group: min = 7 avg = 3.8 max = 5 Wald chi2() =.43 Log likelihood = -876.278 Prob > chi2 = 0.2325 addedvalue Coef. Std. Err. z P> z [95% Conf. Interval] -------------+---------------------------------------------------------------- rd_ratio -627.973 532.8 -.9 0.232-686.86 3930.97 _cons 45390.2 333.558 4.49 0.000 39248.54 553.86 As seen from the above table, since Wald-test s p-value (0.2325) and p-value of rd_ratio (0.232) are larger than significance alpha level 0.05, we may conclude that research and 7

development ratio has not effect on added value alone. Its coefficient is also negative direction. We may the follow the same step for education index variable: AddedValue ij x( ed _ indx) 0 ij ij. xtgls addedvalue ed_indx Cross-sectional time-series FGLS regression Coefficients: generalized least squares Panels: homoskedastic Correlation: no autocorrelation Estimated covariances = Number of obs = 57 Estimated autocorrelations = 0 Number of groups = Estimated coefficients = 2 Time periods = 57 Wald chi2() = 6.65 Log likelihood = -678.69 Prob > chi2 = 0.0099 addedvalue Coef. Std. Err. z P> z [95% Conf. Interval] -------------+---------------------------------------------------------------- ed_indx 3963.05 545.77 2.58 0.00 3348.338 24577.77 _cons -6089.628 22890.28-0.27 0.790-50953.74 38774.49 Since Wald-test s p-value (0.0099) and p-value of ed_indx(0.0) are smaller than significance alpha level 0.05, we may conclude that mean education level of company has positive effect on added value. It means that the added value could increase $3,963 as the mean education level of company increase year. What if added value is larger than $75,000? When we use the $75,000 limitation to discard startups that have newly established and not have enough financial resources, we would have the following table. Table 6 : The Effects of R&D Ratio on Added Value (If Added Value > $75,000) All Bilkent CyberPark Bati Akdeniz Technopark Number of observations 6 6 No Observation Wald chi2(2) 4.67 4.67 - Prob > chi2 0.0306** 0.0306** - R&D Ratio (rd_ratio) Coefficient ( 2 ) 03722.7 03722.7 - SE 47975.4 47975.4 - p-value 0.03** 0.03** - *** Significant at alpha level 0.0. ** Significant at alpha level 0.05. * Significant at alpha level 0.0. As seen the above table, since p-values of Wald-test and p-value of R&D ratio are smaller than significance alpha level 0.05, we may conclude that R&D ratio has positive and significant 8

effect on added value for companies that have added value more than $75,000. For these companies rd_ratio increase by unit, their revenues increase also $03,722. Second interesting point is that all the firms are located in Bilkent Cyberpark. It shows that Bilkent CypberPark companies are more mature, higher skilled, and they can R&D as a productivity increasing factor. 4.5.4. H o w d o E d u c a t i o n L e v e l a n d R & D R a t i o t o g e t h e r a f f e c t t h e A d d e d V a l u e? R&D ratio variable might have significant effect with education index variable together on added value. In order to test this, we may use the following equation. AddedValue ij x ed _ indx) x( rd _ ratio ) 0 ( ij. xtgls addedvalue ed_indx rd_ratio Cross-sectional time-series FGLS regression Coefficients: generalized least squares Panels: homoskedastic Correlation: no autocorrelation Estimated covariances = Number of obs = 5 Estimated autocorrelations = 0 Number of groups = Estimated coefficients = 3 Time periods = 5 Wald chi2(2) = 8.3 Log likelihood = -600.6558 Prob > chi2 = 0.057 addedvalue Coef. Std. Err. z P> z [95% Conf. Interval] -------------+---------------------------------------------------------------- ed_indx 3097.46 527.68 2.55 0.0 3047.393 2347.53 rd_ratio -526.59 5894.502 -.96 0.05-23079.6 26.486 _cons 59.4507 20896.35 0.03 0.977-40364.64 4547.54 As seen from the above results, since Wald-test s p-value (0.057), p-value of ed_index (0.0) are smaller than significance alpha level 0.05, we may conclude that education index has significant effect on added value. Then, p-value of rd_ratio (0.05) is smaller than significance alpha level 0.05, we may conclude that R&D ratio has not significant effect on added value at alpha level 0.05, but it has significant effect at alpha level 0.0. It is interesting that coefficient of rd_ratio has negative direction. If we follow the prior steps for Cyberpark and BaTech technopark separately, we would have the following table. 9

Table 7 : The Effects of Quality of Human Resources and R&D Ratio on Added Value All Bilkent CyberPark Bati Akdeniz Technopark Number of observations 5 39 2 Wald chi2(2) 8.3 6.27.85 Prob > chi2 0.057 0.0435 0.3975 Quality of Human Resources (ed_index) R&D Ratio (rd_ratio) Coefficient ( ) 3097.46 4983.63 7445.728 SE 527.68 7023.37 5955.079 p-value 0.0** 0.033** 0.2 Coefficient ( 2 ) -526.59-660.24-8856.33 SE 5894.502 6598.405 23.58 p-value 0.05* 0.077* 0.372 *** Significant at alpha level 0.0. ** Significant at alpha level 0.05. * Significant at alpha level 0.0. We may conclude that education level (quality of human capital) and R&D expenditure may be considered as productivity influencing factors for firms that are located in Bilkent Cyberpark. As R&D ratio has negative effect on added value since these firms are startups and suffer from lack of adequate financial sources, quality of human resource (ed_index) can be considered productivity increasing factors for software firms. What if added value is larger than $5,000? When we use the $5,000 limitation to discard startups that have newly established and not have enough financial resources, we would have the following table. Table 8 : The Effects of Quality of Human Resources and R&D Ratio on Added Value (If Added Value > $5,000) All Bilkent CyberPark Bati Akdeniz Technopark Number of observations 47 36 Wald chi2(2) 9.58 8.22 0.72 Prob > chi2 0.0083 0.064 0.6976 Quality of Human Resources (ed_index) R&D Ratio (rd_ratio) Coefficient ( ) 4243.0 6748.72 23.9387 SE 529.464 7055.984 6809.504 p-value 0.007*** 0.08** 0.997 Coefficient ( 2 ) -2989.54-3657.36 9093.6 SE 5805.625 643.383 28436.3 p-value 0.025** 0.034** 0.502 *** Significant at alpha level 0.0. ** Significant at alpha level 0.05. * Significant at alpha level 0.0. 20

As seen from the above table, for all companies that are located in these two technoparks, since p-values of ed_indx (0.007) and r_ratio (0.025) are smaller than significance alpha level 0.05, we may conclude that quality of human resources (ed_indx) and R&D ratio have significant effect on added value. However, as we look at the p-values at technopark level, we may see that these two variables are not significant for Bati Akdeniz Technopark firms but significant for those of Bilkent Cyberpark. 4.5.5. H o w D o e s Q u a l i t y o f H u m a n R e s o u r c e s I n f l u e n c e A d d e d V a l u e? As seen the below table, the mean of quality of human capital of each technopark is quite different. Variable Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- ed_indx 2 3.428333.8430662 2.29 5.5 FOR BATECH ed_indx 45 4.323.803073 3.8 6.44 FOR BILKENT Then, regarding the question, we may shorten our model as follows: Y ij 0 x( ed _ indx) 5x( tech _ bilkent ) 0 5x( tech _ bilkent ) ij Null hypothesis is location is not important for added value of firm. Ho: Ha: Bilkent Bilkent Batech Batech We would have the following table after carrying out required regressions. Table 9 : The Comparison of Quality of Human Resources in between Bilkent Cyberpark and BaTech Technopark Bilkent CyberPark Bati Akdeniz Technopark Number of observations 45 2 Wald chi2(2) 5.03 0.98 Prob > chi2 0.0249** 0.326 Quality of Human Resources (ed_index) Coefficient ( ) 6523.74 5793.806 SE 7365.68 5845.632 p-value 0.025** 0.322 *** Significant at alpha level 0.0. ** Significant at alpha level 0.05. * Significant at alpha level 0.0. Regarding the above tables, p-value=0.024 of ed_index(bilkent)is smaller than significance level 0.05, education level can be considered as productivity increasing factor for Bilkent Cyberpark firms. However, p-value=0.387 of ed_index (Bati Akdeniz Technopark) is greater than significance level 0.05, education level can NOT be considered as productivity increasing 2

factor for the technopark firms. Since the p-values of ed_indx for each technopark are quite different, we may reject to the null hypothesis. 4.5.6. A r e t h e S o f t w a r e E x p o r t e r C o m p a n i e s t h e M o r e P r o d u c t i v e? Regarding the question, we may change our model as follows: Y ij 0 3x( ex _ exp r) 0 3x( ex _ exp r) ij. xtgls addedvalue ex_expr Cross-sectional time-series FGLS regression Coefficients: generalized least squares Panels: homoskedastic Correlation: no autocorrelation Estimated covariances = Number of obs = 77 Estimated autocorrelations = 0 Number of groups = 5 Estimated coefficients = 2 Obs per group: min = 5 avg = 5.4 max = 57 Wald chi2() = 0.40 Log likelihood = -93.2674 Prob > chi2 = 0.5296 addedvalue Coef. Std. Err. z P> z [95% Conf. Interval] -------------+---------------------------------------------------------------- ex_expr -4959.009 7889.05-0.63 0.530-2042.27 0503.25 _cons 47699.25 564.596 9.24 0.000 37576.83 5782.67 According to above result, since p-value is smaller than.05 alpha level, we may conclude that export experience can not be considered a factor that increases added value of firm in these technoparks. What if we evaluate export experience and software quality consideration together? In the line with the new situation, we may change our model as follows Y ij 0 3x( ex _ exp r) 0 3x( ex _ exp r) 4x( sw_ quality ) 0 4x( sw_ quality ). xtgls addedvalue ex_expr sw_quality Cross-sectional time-series FGLS regression Coefficients: generalized least squares Panels: homoskedastic Correlation: no autocorrelation Estimated covariances = Number of obs = 77 Estimated autocorrelations = 0 Number of groups = 5 Estimated coefficients = 3 Obs per group: min = 5 avg = 5.4 max = 57 Wald chi2(2) = 0.74 Log likelihood = -93.0943 Prob > chi2 = 0.6894 addedvalue Coef. Std. Err. z P> z [95% Conf. Interval] -------------+---------------------------------------------------------------- ex_expr -429.598 7952.464-0.54 0.589-9878.4 294.94 sw_quality -4636.749 787.562-0.59 0.556-20064.73 079.23 _cons 4970.48 673. 8.05 0.000 37602.43 6800.54 ij 22

According to above result, since p-value is smaller than.05 alpha level, we may conclude that export experience and software quality can not be considered a factor that increase added value of firm in these technoparks. 5. I N T E R P R E T A T I O N O F R E S U L T S According to econometrical assessment of the survey results regarding the Turkish Software Industry, the major conclusions achieved by the survey are as follows: Cumulatively the growth of revenue regarding the model increases as year increases. As the year increases unit, the revenue of a company increases $35,676. The mean the number of employee is 2.35 and the mean added value is $44,495 in these technoparks. If we look at these facts at technopark level, we can see that Bilkent Cyberpark has $47,44 mean added value with the mean 23.93 employee and BaTech Technopark has $36,324 mean added value with the mean 3.4 employee. They are separating their 32.5 % of annual budgets for R&D activities. As BaTech companies s average R&D expenditure is %24., the ratio is % 38.6 for Bilkent CyberPark companies. In Turkey, perhaps Bilkent Cyberpark firms are the best software companies in terms of their innovative capabilities and high qualified human resources due to be located in one of the best technology cluster of Turkey and Middle East region. In the general model we found that there is not a linear relationship between research and development ratio and revenue per employee among the software firms in the two technoparks. Why? The companies in two technoparks are mostly start-up companies and their mean of firm-age is less than 3. They are still R&D and st product development phase. Hence, most of them have not completed their final product yet. When we change the model determining minimum level added value, we found that education level is important factors to increase added value of firms in the two technoparks. But, as R&D expenditure increases, the added value decreases according to our results. Why? Since these companies are newly established startups, they have not yielded their works yet and they are still at product development phase. There is a linear relationship between the education level of a software firm and its added value. As the education level increases among the software firms located in technoparks, the revenue per employee also increases. Education level is always effective on the added value of a software firm. As mean year of higher education among staff (quality of human capital) increases unit, the firm s added value per staff increase more than $3000. Hence, education level (quality of human capital) and R&D expenditure may be considered as productivity influencing factors for firms that located on the technoparks. Another issue regarding the R&D, we assumed that if the company is new established, it has not revenue for given year, then its added value would be lower with insignificant p-value. Contrary, if firm has relatively completed 23

its institutionalism, and embarked on selling its products or services, its Added Value will be higher. In the light of these assumptions, when we implement our model for firms which more than $75,000 added value per year, we get the result R&D expenditure is significant productivity increasing factors for the firms whose added values are larger than $75,000 for firms in these technoparks. R&D ratio has positive and significant effect on added value for companies that have added value more than $75,000. For these companies rd_ratio increase by unit, their revenues increase also $03,722. Second, all the firms are located in Bilkent Cyberpark. It shows that Bilkent CypberPark companies are more mature, higher skilled, and they can benefit from R&D as a productivity increasing factor. Export experience and software quality consideration can not be considered a factor that increase added value of firm in these technoparks. Why? Because these firms are startup firms, we can not measure this at this time. It is required more detailed studies and large size datasets. High quality personnel are needed to take more shares in global market and to make contribution to global value chain. All people working for the sector graduated from at least high school. Predominantly, employers of the Bati Akdeniz Technoparks have two-year college degree or undergraduate degree from famous universities. But in Bilkent Cyberpark the ratio is higher than Bati Akdeniz: mean education level of Cyberpark is more than 4.3 years. It means that most of employees have at least Bachelor degree. Moreover, since the added value of a software firm is directly related to human resources capacity, the demand for well-educated software worker is very high in Turkey. For this reason, software workers who have graduate or upper level of education degree are able to earn more than others due to the lack of skilled ones. As we look at the other part of the study, what are the most important problems of Turkish Software industry question, the result is also supported by the question s answers. As indicated in the following table, the problem of lack of qualified software staff is the first rank among the problems of Turkish software industry. Table 0 : The Major Problems of Turkish Software Industry Problems Score Ratio (%) Lack of Qualified Software Staff 23 9.2 Lack of Venture Capital 4.7 Expansion to Foreign Markets 9.2 Software Piracy 9.2 High Tax Rates 8 6.7 There is a linear relationship between the research and development (R&D) ratio and revenue per employee. Regarding the R&D expenditures, the survey results have also showed that investing to research and development activities is so crucial to be more productive and profitable business in software industry. According to results of the analysis, the software firms have allocated 32.5 % of their annual revenues for the R&D operations. The ratio is 24% for 24

BaTech and %38.6 for Bilkent Cyberpark companies. These ratios, which are above OECD average of 4.9 percent, are promising for future development of the Turkish software industry 9. To sum up, according to survey results, the software companies are needed high-qualified employees who have at least bachelor and up degrees. College and high school degree staff have not significant effect on added value of firms. Firms that want to be more competitive and innovative consider the fact day by day. Turkey needs to increase high qualified human resources capacity in software industry focusing on more graduate programs such as software engineering instead of college and high school ones immediately. 9 OECD, 2006:p.4 25

6. R E F E R E N C E S ANEESH, A., Rethinking Migration: High-Skilled Labor Flows from India to the United States, The Center for Comparative Immigration Studies Working Paper No. 8, University of California San Diego, August 2002, (online) http://www.ccis-ucsd.org/publications/wrkg8.pdf BARR, Avron and Patrick REILLY, Developing a National Software Strategy: Some IP Considerations International Conference on Strategies for Building Software Industries in Developing Countries IIPI, Hawaii, May 9-2, 2004, (online) http://www.iipi.org/conferences/hawaii_sw_conference/barr%20paper.pdf HEEKS, Richard and Brian NICHOLSON, Software Export Success Factors and Strategies in Developing And Transitional Economies, Institute for Development Policy and Management, University of Manchester, UK, 2002, (online) http://www.sed.manchester.ac.uk/idpm/publications/wp/di/di_wp2.pdf GEWEKE, John, Contemporary Bayesian Econometrics and Statistics, Departments of Economics and Statistics, University of Iowa, July, 2003GODIN, Benoit, The New Economy: What The Concept Owes To The OECD, Research Policy, Vol. 33, 2004, pp. 679-690 GUAJARATI, Damodar N., Basic Econometrics, 4 th Edition, McGraw-Hill Companies, 2004 MITCHELL, William J., Alan S. INOUYE and Marjory S. BLUMENTHAL, (editors), Beyond Productivity, Information Technology, Innovation and Creativity, ISBN 0-309-0886-82, The National Academies Press, Washington DC, June 2003 OECD and European Commision, Frascati Manual: Proposed Standard Practice For Surveys Research and Experimental Development, ISBN 92-64-9903-9, Paris, 2002, (online) http://www.oecd.org/publications/e-book/920208e.pdf OECD, Information Technology Outlook 2006, ISBN 92-64-02643-6, Paris, 2006 Software Magazine, Software 500 List, (online) http://www.softwaremagazine.com/ WOOLDRIGE, Jeffrey M., Introductory Econometrics A Modern Approach, 2005 26

A P P E N D I X : S U R V E Y F O R M A T & S U R V E Y E D F I R M S A) General Information A- Company Name A-2 Foundation Year BILKENT CYBERPARK & BATI AKDENIZ TECHNOPARK Technology Development Zones Software Firms Survey A-3 Origin of Corporation(City-Country) A-4 Ownership (Local-Foreign) A-5 Address A-6 Contact Person/Position A-7 Phone A-8 Fax A-9 Web Address A-0 e-mail A- Major Action Fields B) Corporation Profile (US Dollar) 2002 2003 2004 2005 2006 B- Total Revenue B-2 The Number of Employee B-3 The Number of Software Staff B-4 The Number of R&D Employee B-5 Total Salary for Software Staff B-6 Total R&D Expenditure B-7 The Number of Patent B-8 Total Export Revenue C) Employee Education Profile C- The Number of Employee D) Quality Level College Bachelor Master PhD Total D- D-2 D-3 Do you implement a software development model such as SW-CMM, CMMi, IEEE or ISO based? Do you have CMMi or SW-CMMi certificate? If you implement the CMM model, which level has been implemented by your firm? What is your basic problem to get CMMI or SW-CMM certificate? - Acquisition Cost 2- We don t need 3- We don t know what CMMI is 4- Other? (Please clarify) 27

E) Export Destinations (Please sign with + ) E- West Europe (Germany-UK-France-...) E-2 East Europe (Bulgaria-Romania- Russia-..) E-3 Asia-Pacific (Japan-Korea-China-..) E-4 North America (USA-Canada-Mexico) E-5 Central Asia Turkic Countries (Azerbaycan-Turkmenistan- Kazakhstan-Uzbekstan- ) E-6 Middle East-Africa (Iran-Iraq-Lebanon-Syria-Israel-Egypt- ) E-7 Other(Please clarify) F) The Problems of Software Industry What are major problems of the Turkish Software Industry? Do you have solution offers related to these problems? Problems Offered Solution Table : Surveyed FirmsBIC } : Surveyed Firms # Company Name Location Alpdata Bilgisayar Yazılım Donanım Sistemleri ve Medikal Paz. Tic. Ltd. Şti. Bilkent CyberPark 2 AmonRa Bati Akdeniz Technopark 3 ARGESET Bilkent CyberPark 4 ANT-BİM BİLGİSAYAR Bati Akdeniz Technopark 5 AVIAN Yazılım Danışmanlık Eğitim İç Ve Dış Ticaret Limited Şirketi Bilkent CyberPark 6 ATHENA BİLGİSAYAR Bati Akdeniz Technopark 7 Başkent Bilişim Bilkent CyberPark 8 Dem Yazılım Bati Akdeniz Technopark 9 BİLBEST Bilişim Bilkent CyberPark 0 Doruk Otomasyon ve Yazılım Bati Akdeniz Technopark Bilişim Sanayi Bilkent CyberPark 2 Kod Yazılım Bati Akdeniz Technopark 3 Bilsoft Bilgi Eğitim teknolojileri Yazılım Bilkent CyberPark 4 MİKRO KURUMSAL DAN.YAZILIM Bati Akdeniz Technopark 5 CBKSOFT Yazılım Bilkent CyberPark 6 NETWORK Computer Bati Akdeniz Technopark 7 Egecom Yazılım Bilişim Bilkent CyberPark 8 PRODEM YAZILIM Bati Akdeniz Technopark 9 ESC Bilgi işlem yazılım Bilkent CyberPark 20 RMOS Bati Akdeniz Technopark 2 ESPOR Dijital Bilkent CyberPark 22 SAN BİLGİSAYAR Bati Akdeniz Technopark 23 GDY Piksel Yazılım Bilkent CyberPark 28

24 SENTEZ BİLGİSAYAR YAZILIM Bati Akdeniz Technopark 25 İDEAL TEknoloji Bilkent CyberPark İkisoft Yazılım Bilgi ve İletişim Eğitim Teknolojileri ve Hizm. Elektronik San. ve Tic. Ltd. 26 Şti. Bilkent CyberPark 27 INOVIS Bilkent CyberPark 28 Interlog Lojistik Sistemler Yazılım Bilkent CyberPark 29 KAREL ArGe A.Ş. Bilkent CyberPark 30 Knidos Teknoloji Bilkent CyberPark 3 LST Yazılım Bilkent CyberPark 32 Mescioğlu Mühendislik Bilkent CyberPark 33 MHT ELEKTRONIK Bilkent CyberPark 34 MikroBiz Ltd. Şit. Bilkent CyberPark 35 Mikrokom Yazılım Bilkent CyberPark 36 MİRON Bilgisayar Bilkent CyberPark 37 Molkay Yazilim Bilkent CyberPark 38 Most Yazılım Bilkent CyberPark 39 NARYAZ SOFTWARE Bilkent CyberPark 40 NATEK Bilişim Bilkent CyberPark 4 Neo Grup Mühendislik Bilkent CyberPark 42 Netsis Yazılım Sanayi ve Ticaret A.Ş Bilkent CyberPark 43 Optima Elektronik Bilkent CyberPark 44 PLEKSUS BİLİŞİM TEKN. A.Ş Bilkent CyberPark 45 Promed Bilgi yönetim sist. Bilkent CyberPark 46 PROSET YAZILIM Bilkent CyberPark 47 SDT Bilkent CyberPark 48 SESAM YAZILIM Bilkent CyberPark 49 SIMTERNET Bilkent CyberPark 50 Synplicity Araştırma Geliştirme Bilkent CyberPark 5 TEKNO TASARIM Bilkent CyberPark 52 Tepe Teknoojik Servisler A.Ş. Bilkent CyberPark 53 Tridem Yazılım Bilkent CyberPark 54 TTG Uluslararası LTD Bilkent CyberPark 55 UTARİT Bilişim Bilkent CyberPark 56 VENTURA YAZILIM Bilkent CyberPark 57 Yöntem Bilgi Teknolojiler Bilkent CyberPark 58 Zirve Bilgi Teknolojileri Bilkent CyberPark 29

A P P E N D I X 2 : O T H E R R E G R E S S I O N S Revenue - R&D Expenditure. xtgls revenue rd_exp Cross-sectional time-series FGLS regression Coefficients: generalized least squares Panels: homoskedastic Correlation: no autocorrelation Estimated covariances = Number of obs = 57 Estimated autocorrelations = 0 Number of groups = 5 Estimated coefficients = 2 Obs per group: min = 7 avg = 3.4 max = 5 Wald chi2() = 2.3 Log likelihood = -240.363 Prob > chi2 = 0.0000 revenue Coef. Std. Err. z P> z [95% Conf. Interval] -------------+---------------------------------------------------------------- rd_exp 2.48063.95025.0 0.000.76582 2.530306 _cons 392849.8 02975 3.82 0.000 9022.5 594677.2 Revenue Staff and R&D Expenditure. xtgls revenue rd_exp staff Cross-sectional time-series FGLS regression Coefficients: generalized least squares Panels: homoskedastic Correlation: no autocorrelation Estimated covariances = Number of obs = 57 Estimated autocorrelations = 0 Number of groups = 5 Estimated coefficients = 3 Obs per group: min = 7 avg = 3.4 max = 5 Wald chi2(2) = 529.7 Log likelihood = -2339.526 Prob > chi2 = 0.0000 revenue Coef. Std. Err. z P> z [95% Conf. Interval] -------------+---------------------------------------------------------------- rd_exp.823493.795037.02 0.30 -.69474.53470 staff 45083.63 2972.228 5.7 0.000 39258.7 50909.09 _cons -7466.24 72449.48 -.02 0.306-2664.6 67832.4 Revenue Staff and R&D Expenditure. xtgls addedvalue staff rd_exp Cross-sectional time-series FGLS regression Coefficients: generalized least squares Panels: homoskedastic Correlation: no autocorrelation Estimated covariances = Number of obs = 57 Estimated autocorrelations = 0 Number of groups = 5 30

Staff Year Estimated coefficients = 3 Obs per group: min = 7 avg = 3.4 max = 5 Wald chi2(2) = 4.80 Log likelihood = -85.72 Prob > chi2 = 0.0909 addedvalue Coef. Std. Err. z P> z [95% Conf. Interval] -------------+---------------------------------------------------------------- staff -88.8295 32.9576 -.42 0.56-449.426 7.76248 rd_exp.074966.0080298 2.8 0.029.007585.0332347 _cons 42667.52 3240.904 3.7 0.000 3635.46 4909.57. xtgls staff year Cross-sectional time-series FGLS regression Coefficients: generalized least squares Panels: homoskedastic Correlation: no autocorrelation Estimated covariances = Number of obs = 92 Estimated autocorrelations = 0 Number of groups = 5 Estimated coefficients = 2 Obs per group: min = 24 avg = 38.4 max = 57 Wald chi2() = 2.28 Log likelihood = -900.7923 Prob > chi2 = 0.3 staff Coef. Std. Err. z P> z [95% Conf. Interval] -------------+---------------------------------------------------------------- year 2.075344.374682.5 0.3 -.689833 4.76967 _cons -438.478 2755.422 -.50 0.33-9539.005 262.049 Software Quality - Staff and Year (Since sw_quality is dummy variable, it is used Probit method). probit sw_quality staff year Iteration 0: log likelihood = -53.365839 Iteration : log likelihood = -49.04395 Iteration 2: log likelihood = -48.93996 Iteration 3: log likelihood = -48.93228 Probit regression Number of obs = 77 LR chi2(2) = 8.9 Prob > chi2 = 0.06 Log likelihood = -48.93228 Pseudo R2 = 0.0834 sw_quality Coef. Std. Err. z P> z [95% Conf. Interval] -------------+---------------------------------------------------------------- staff.046424.00649 2.26 0.024.009203.0273646 year -.235365.22388 -.93 0.054 -.474704.00407 _cons 47.5742 244.906.93 0.054-8.44729 95.5902 3

A P P E N D I X 3 : D A T A 32