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

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S TAT E P L A N N I N G O R G A N I Z AT I O N

D G F O R R E G I O N A L D E V E L O P M E N T A N D S T R U C T U R A L A D J U S T M E N T

W W O O R R K K I I N N G G P P A A P P E E R R

A N E C O N O M E T R I C A N A LY S I S O F S U RV E Y S T U D Y O N B I L K E N T C Y B E R PA R K A N D B AT I A K D E N I Z T E K N O PA R K

I N T U R K E Y

UNPUBLISHED STUDY

D e c e m b e r 1 4^{t h}, 2 0 0 9

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

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C o n t e n t s

1. INTRODUCTION ... 1

2. THEORETICAL FRAMEWORK ... 2

3. DATA SET ... 3

3.1. ANKARA CYBERPARK ... 3

3.2. BATI AKDENIZ TECHNOPARK ... 4

3.3. THE SCOPE OF THE SURVEY ... 4

4. ECONOMETRIC ANALYSIS ... 5

4.1. SCIENTIFIC GOALS... 5

4.2. ECONOMETRIC MODEL ... 5

4.3. BASIC ASSUMPTIONS FOR THE MODEL ... 8

4.3.1. Autocorrelation ... 9

4.3.2. Non-Normality ... 11

4.3.3. Hetoroskedasticity ... 12

4.3.4. Endogeneity Test ... 12

4.3.5. Multicollinearity ... 13

4.4. VALIDATING ASSUMPTIONS ... 14

4.4.1. Heteroskedasticity and Non-Normality ... 14

4.5. ANALYSIS OF RESULTS ... 15

4.5.1. General Evaluations of P-Values ... 16

4.5.2. Do Revenues Increase according to Year in these Technoparks? ... 16

4.5.3. Is there a Relationship between R&D Ratio and Added Value? ... 17

4.5.4. How do Education Level and R&D Ratio together affect the Added Value? ... 19

4.5.5. How Does Quality of Human Resources Influence Added Value? ... 21

4.5.6. Are the Software Exporter Companies the More Productive? ... 22

5. INTERPRETATION OF RESULTS ... 23

6. REFERENCES ... 26

APPENDIX 1: SURVEY FORMAT & SURVEYED FIRMS ... 27

APPENDIX 2: OTHER REGRESSIONS ... 30

APPENDIX 3: DATA ... 32

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T a b l e s

Table 1 : 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 ... 17

Table 6 : The Effects of R&D Ratio on Added Value (If Added Value > $75,000) ... 18

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 > $15,000)... 20

Table 9 : The Comparison of Quality of Human Resources in between Bilkent Cyberpark and BaTech Technopark ... 21

Table 10 : The Major Problems of Turkish Software Industry ... 24

Table 11 : Surveyed Firms ... 28

The report is prepared by Kamil TASCI & Mehmet Emin OZSAN

December14^th, 2009

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1 . 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 2001. It is anticipated that Turkey’s software market will have reached US$ 2.1 billion USD in 2006, up from 951 million for 2001. Turkish software industry’s annual growth rate is 17.5 % in the period of 2001-2006. 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 single- digit 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 120 software companies in CyberPark, and 12 out of 19 software companies in Batı Akdeniz Technopark have responded the survey. In total 58 out of 139 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 2100 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.

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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^1. 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 capital². 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 activities³.

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 14.9% in 2000 and 2005. Same figure for communication appliances producing companies had been 12.1%⁴.

Table 1 : R&D Intensity in ICTs by Sub-components (Percent)

Sub-Component 2000 2005

Telecommunication 1,8 1,4

Services 4,6 4,9

IT Equipments 6,1 5,4

Electronic Components 6,3 7,1

Communication Equipments 11,3 12,1

Software 14,9 14,9

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 #1 among world’s

1 Godin, 2004:685

2 Mitchell at al, 2003:26

3 OECD and European Commision, 2002:46-47

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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, 21% 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 . 1 . 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 108 companies in total, 94 out of which were software companies as of July 2005, in September 2006, the total number of established companies reached 164 and number of software companies reached to 120. In Cyberpark, number of people employed is

5 Software Magazine, 2006

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2011 in total, 1137 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 100 students are doing traineeships in the companies located at Cyberpark.

There are 120 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 19. 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 132 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 12 firms from Bati Akdeniz Technopark provided total 192 observations that belong to the period of 2002-2006. The data descriptions of the survey are the following:

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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 57 192

City (Ankara, Istanbul, Antalya, Other) 24 32 35 44 57 192

Total Revenue 24 32 35 44 57 192

The Number of Staff 24 32 35 44 57 192

The Number of Software Staff 23 29 32 42 55 181

Staff Education Level (Col., Bach., MS, PhD) 0 0 0 0 55 55

R&D Expenditure 17 23 28 38 51 157

Software Quality Consideration (Answer: Yes) 3 3 4 4 24 38

Export Experience (Answer: Yes) 1 1 2 5 24 33

Total Added Value (Revenue per Staff) 24 32 35 44 57 192

R&D Ratio (R&D Share in Total Revenue) 17 23 28 38 51 157

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 . 1 . 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.

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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 Number It is total added value and measured by (Total Revenue /Total Staff) in given year

rd_ratio Number (%)

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:

ij ij

batech tech

x bilkent

tech x

quality sw

x quality

sw x

r ex

x r

ex x ratio

rd x indx

ed x Y









1 5

0 5

1 4

0 4

1 3

0 3

2 1

0

) _

( )

_ (

) _

( )

_ (

) exp _ ( )

exp _ ( )

_ ( )

_ (

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”.

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Table 4 : Description of the Econometric Model

Component Description

Yij : 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.

0 Intercept parameter.

ed_indx

The independent variable denotes education index.

ed_indxij = 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 : 10 years for PhD degree after high-school

ed_indxij = (2 x ed_col + 4 x ed_bac + 6 x ed_mas + 10xed_phd)/(

ed_col+ed_bac+ed_mas+ed_phd)



1 The coefficient for ed_indx variable

rd_ratio The independent variable denotes R&D Expenditure Percentage Ratio of firm. It is calculated by

“R&D Expenditure / Annual Revenue”



2 The coefficient for rd_ratio variable

ex_expr Dummy variable denotes whether the company has export experience before nor not.

3 The coefficient for dummy ex_expr variable.

sw_quality Dummy variable denotes whether the company considers Software Quality Standards such as CMM or ISO 12207.

4 The coefficient for dummy sw_quality variable tech_bilkent –

tech_batech

Dummy variable denotes whether the company is located in Bilkent Cyberpark or BATech Technopark nor not.

5 The coefficient for dummy tech_bilkent and tech_batech variable

ij The random error.

According to the model, our null and alternative hypotheses are as follow:

1^st Hypothesis:

0 :

1 1







 Ha Ho

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:

0 :

2 2







 Ha Ho

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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

0 :

3 3







 Ha Ho

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

0 :

4 4







 Ha Ho

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

0 :

5 5







 Ha Ho

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,

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(iii) E(_i) = 0, X variable is non-stochastic and linearly independent, Properties

Unbiased, in other words E(



ˆ)



Assumptions: _i ~ N (0, σ² I) (normal, independent, homoskedastic) E(X, _i) = 0 (If X is random, then it is uncorrelated with the error) Properties:

1. Unbiased,

2. ˆ (,²(X^'X)^¹) ~ (β, σ2 (X’X)-1),

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 = 51 ---+--- F( 5, 45) = 1.61 Model | 8.9675e+09 5 1.7935e+09 Prob > F = 0.1756 Residual | 4.9985e+10 45 1.1108e+09 R-squared = 0.1521 ---+--- Adj R-squared = 0.0579 Total | 5.8953e+10 50 1.1791e+09 Root MSE = 33328 --- addedvalue | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---+--- ed_indx | 12638 6100.437 2.07 0.044 351.0934 24924.92 rd_ratio | -10812.67 6551.357 -1.65 0.106 -24007.78 2382.444 ex_expr | -7372.657 9752.303 -0.76 0.454 -27014.8 12269.49 sw_quality | 2197.486 10292.69 0.21 0.832 -18533.05 22928.02 tech_bilkent | 660.75 13082.06 0.05 0.960 -25687.88 27009.38 tech_batech | (dropped)

_cons | 3976.448 23712.61 0.17 0.868 -43783.2 51736.1 ---

4 . 3 . 1 . 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_₁) ≠ 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:

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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 191 delta: 1 unit . dwstat

Number of gaps in sample: 33

Durbin-Watson d-statistic( 6, 51) = .5193277

According to K=6 (degrees of freedom) and T=51 (the number of observation) values, we may gain dl and du values from Durbin-Watson Test tables as follows:

771 . 1

335 . 1



u l

d d

Since d value is between zero and lower d value (0d 0.519d_l 1.335), we can reject the null hypothesis (Ho: No Autocorrelation) may conclude that there is a positive (Ha1:Positive Autocorrelation 0<d<dl condition) autocorrelation problem. This problem should be solved GLS transformation.

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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

(141 missing values generated) . gen e2=e^2

(141 missing values generated) . egen m2 = mean(e2)

. egen m3 = mean(e3) . egen m4 = mean(e4) . gen S = m3/m2^1.5 . gen K = m4/m2^2

. gen jb = (_N/6)*(S^2+(K-3)^2/4) . display S

1.3469647 . display K 5.3052745 . di jb 100.57237

. display chiprob(5 ,jb ) 4.003e-20

In the “Right-Tail Critical Values for the

2Distribution” table, chisquare value is 11.0705 for df=5 at alpha level 0.05 and chisquare value is 9.23635 for df=5 at alpha level 0.1. Since our Jarque-Bera test result is 4.003 smaller than the mentioned values, we can not reject non- normality 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.

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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

---+--- ed_indx | 2.22e+08 3.45e+08 0.64 0.523 -4.73e+08 9.17e+08 rd_ratio | -1.67e+08 3.70e+08 -0.45 0.655 -9.13e+08 5.79e+08 ex_expr | -1.49e+09 5.51e+08 -2.70 0.010 -2.60e+09 -3.77e+08 sw_quality | -8.90e+08 5.82e+08 -1.53 0.133 -2.06e+09 2.82e+08 tech_bilkent | 1.28e+09 7.40e+08 1.73 0.090 -2.07e+08 2.77e+09 tech_batech | (dropped)

_cons | 2.39e+08 1.34e+09 0.18 0.859 -2.46e+09 2.94e+09 --- . predict v

(option xb assumed; fitted values) (141 missing values generated) . hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance

Variables: fitted values of e2 chi2(1) = 31.16 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.

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. ivreg addedvalue ed_indx rd_ratio ex_expr sw_quality tech_bilkent Instrumental variables (2SLS) regression

---+--- ed_indx | 12638 6100.437 2.07 0.044 351.0934 24924.92 rd_ratio | -10812.67 6551.357 -1.65 0.106 -24007.78 2382.444 ex_expr | -7372.657 9752.303 -0.76 0.454 -27014.8 12269.49 sw_quality | 2197.486 10292.69 0.21 0.832 -18533.05 22928.02 tech_bilkent | 660.75 13082.06 0.05 0.960 -25687.88 27009.38 _cons | 3976.448 23712.61 0.17 0.868 -43783.2 51736.1 --- (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:

(1) 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=51)

| addedv~e ed_indx rd_ratio ex_expr sw_qua~y tech_b~t ---+--- addedvalue | 1.0000

ed_indx | 0.2750 1.0000

rd_ratio | -0.1734 0.2607 1.0000

ex_expr | -0.1684 -0.0448 0.1961 1.0000

sw_quality | -0.0267 -0.0125 0.2108 0.0497 1.0000

tech_bilkent | 0.1033 0.4095 0.1490 0.1311 0.3169 1.0000

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 (1/VIF) is smaller than 0.05, there might be a multicollinearity problem.

. vif

Variable | VIF 1/VIF ---+--- tech_bilkent | 1.41 0.707295

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ex_expr | 1.08 0.924908 ---+--- Mean VIF | 1.25

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

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 N S

Since there are only heteroskedasticity and non-normality problem in our model, we would focus on these problems using some corrections.

4 . 4 . 1 . 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 = 1 Number of obs = 51 Estimated autocorrelations = 0 Number of groups = 1 Estimated coefficients = 6 Time periods = 51

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Wald chi2(5) = 8.07 Log likelihood = -600.2966 Prob > chi2 = 0.1522 --- addedvalue | Coef. Std. Err. z P>|z| [95% Conf. Interval]

---+--- ed_indx | 12638 6100.437 2.07 0.038 681.3674 24594.64 rd_ratio | -10812.67 6551.357 -1.65 0.099 -23653.09 2027.757 ex_expr | -7372.657 9752.303 -0.76 0.450 -26486.82 11741.51 sw_quality | 2197.486 10292.69 0.21 0.831 -17975.81 22370.78 tech_bilkent | 660.75 13082.06 0.05 0.960 -24979.62 26301.12 _cons | 3976.448 23712.61 0.17 0.867 -42499.41 50452.31 ---

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 51 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 = 1 Number of obs = 51 Estimated autocorrelations = 0 Number of groups = 1 Estimated coefficients = 6 Time periods = 51 Wald chi2(5) = 8.07 Log likelihood = -600.2966 Prob > chi2 = 0.1522 --- addedvalue | Coef. Std. Err. z P>|z| [95% Conf. Interval]

---+--- ed_indx | 12638 6100.437 2.07 0.038 681.3674 24594.64 rd_ratio | -10812.67 6551.357 -1.65 0.099 -23653.09 2027.757 ex_expr | -7372.657 9752.303 -0.76 0.450 -26486.82 11741.51 sw_quality | 2197.486 10292.69 0.21 0.831 -17975.81 22370.78 tech_bilkent | 660.75 13082.06 0.05 0.960 -24979.62 26301.12 _cons | 3976.448 23712.61 0.17 0.867 -42499.41 50452.31 ---

Estimated Model:

ij ed indx x rd ratio

Yˆ 3976.44812638*( _ )(10812.67 ( _ )

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 Intercept parameter⁰_=3976.448

 The slope parameter for edu_indx



¹_{= 12368}

 The slope parameter for rd_ratio



²= (-10812.67)

 The slope parameter for ex_expr (Export Experience)³

=(-7372.657)

 The slope parameter for sw_quality (Software Quality)⁴=(2197.586)

 The slope parameter for tech_bilkent (Bilkent Cyberpark)⁵_=(660.75)

4 . 5 . 1 . 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

1st Hypothesis: Regarding the 1st 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 0.10, 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=0.831 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.

ij i

i year

revenue 



₀



₁( ) 



Regarding the model, we would have the following table.

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Table 5 : The Growth of Revenues to Changing Year

Cumulative Bilkent CyberPark Bati Akdeniz Technopark

Number of observations 192 145 47

Wald chi2(2) 3.64 2.27 1.58

Prob > chi2 0.0565* 0.1317 0.2081

YEAR

Coefficient (



₁) 135676.1 140184.4 63444.84

SE 71148.5 92994.38 50400.64

p-value 0.057* 0.132 0.208

*** Significant at alpha level 0.01. ** Significant at alpha level 0.05. * Significant at alpha level 0.10.

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 0.10, we may conclude that year has significant effect on revenue. As the year increases 1 unit, the revenue of a company increases $135,676.

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.

ij ij

x rd ratio

AddedValue  

₀

 

₂

( _ )  

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

Estimated covariances = 1 Number of obs = 159 Estimated autocorrelations = 0 Number of groups = 5 Estimated coefficients = 2 Obs per group: min = 17 avg = 31.8 max = 51 Wald chi2(1) = 1.43 Log likelihood = -1876.278 Prob > chi2 = 0.2325 --- addedvalue | Coef. Std. Err. z P>|z| [95% Conf. Interval]

---+--- rd_ratio | -6127.973 5132.181 -1.19 0.232 -16186.86 3930.917 _cons | 45390.2 3133.558 14.49 0.000 39248.54 51531.86 ---

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

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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:

ij ij

ij

x ed indx

AddedValue  

₀

 

₁

( _ )  

. xtgls addedvalue ed_indx

---+--- ed_indx | 13963.05 5415.77 2.58 0.010 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(0.10) 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 $13,963 as the mean education level of company increase 1 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 16 16 No Observation

Wald chi2(2) 4.67 4.67 -

Prob > chi2 0.0306** 0.0306** -

R&D Ratio (rd_ratio)

Coefficient (



₂) 103722.7 103722.7 -

SE 47975.4 47975.4 -

p-value 0.031** 0.031** -

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

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effect on added value for companies that have added value more than $75,000. For these companies rd_Ratio increase by 1 unit, their revenues increase also $103,722. 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.

ij ij

x ed indx x rd ratio AddedValue  

₀

 

₁

( _ )  

₁

( _ )  

. xtgls addedvalue ed_indx rd_ratio

---+--- ed_indx | 13097.46 5127.68 2.55 0.011 3047.393 23147.53 rd_ratio | -11526.59 5894.502 -1.96 0.051 -23079.61 26.41816 _cons | 591.4507 20896.35 0.03 0.977 -40364.64 41547.54 ---

As seen from the above results, since Wald-test’s p-value (0.0157), p-value of ed_index (0.011) 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 (0.051) 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 0.10. 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.

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Table 7 : The Effects of Quality of Human Resources and R&D Ratio on Added Value

Wald chi2(2) 8.31 6.27 1.85

Prob > chi2 0.0157 0.0435 0.3975

Quality of Human Resources (ed_index)

Coefficient (



₁) 13097.46 14983.63 7445.728

SE 5127.68 7023.37 5955.079

p-value 0.011** 0.033** 0.211

Coefficient (



₂) -11526.59 -11660.24 -18856.33

SE 5894.502 6598.405 21113.58

p-value 0.051* 0.077* 0.372

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 $15,000?

When we use the $15,000 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 > $15,000)

Wald chi2(2) 9.58 8.22 0.72

Prob > chi2 0.0083 0.0164 0.6976

Coefficient (



₁) 14243.01 16748.72 23.19387

SE 5291.464 7055.984 6809.504

p-value 0.007*** 0.018** 0.997

Coefficient (



₂) -12989.54 -13657.36 19093.16

SE 5805.625 6431.383 28436.13

p-value 0.025** 0.034** 0.502

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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 | 12 3.428333 .8430662 2.29 5.5 FOR BATECH ed_indx | 45 4.323111 .8030731 3.18 6.44 FOR BILKENT

Then, regarding the question, we may shorten our model as follows:

ij ij

x ed indx x tech bilkent x tech bilkent

Y  

₀

 

₁

( _ )  

₅

( _ )

₀

 

₅

( _ )

₁

 

Null hypothesis is “location is not important for added value of firm.”

Batech Bilkent

Ha Ho

1 1

: :







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 12

Wald chi2(2) 5.03 0.98

Prob > chi2 0.0249** 0.3216

Coefficient (



₁) 16523.74 5793.806

SE 7365.168 5845.632

p-value 0.025** 0.322

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

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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:

ij

x ex r x ex r

Y  

₀

 

₃

( _ exp )

₀

 

₃

( _ exp )

₁

 

. xtgls addedvalue ex_expr

---+--- ex_expr | -4959.009 7889.051 -0.63 0.530 -20421.27 10503.25 _cons | 47699.25 5164.596 9.24 0.000 37576.83 57821.67 ---

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

ij ij x ex r x ex r x sw quality x sw quality Y ₀₃ ( _exp )₀₃ ( _exp )₁₄ ( _ )₀₄ ( _ )₁

. xtgls addedvalue ex_expr sw_quality Cross-sectional time-series FGLS regression Coefficients: generalized least squares Panels: homoskedastic

---+--- ex_expr | -4291.598 7952.464 -0.54 0.589 -19878.14 11294.94 sw_quality | -4636.749 7871.562 -0.59 0.556 -20064.73 10791.23 _cons | 49701.48 6173.1 8.05 0.000 37602.43 61800.54 ---