The Impact of Innovative Activity in Economic Performance: An ...

International Conference on Applied Economics – ICOAE 2008 595
The Impact of Innovative Activity in Economic Performance: An Empirical Analysis on
Pharmaceutical Sector
Ilias A. Makris and Stavros I. Stavroyiannis 1
Abstract
In this paper we examine the effect of innovation in pharmaceutical firms operating in Greek economy. We recorded some basic economic and non-economic
data between 1996 and 2005, trying to reveal the impact of innovative activity in sales’ and employment’s growth, comparing to other factors that are believed
to be important (such as investment activity and firm’s size). Through a rather differentiated statistical analysis, we managed to compare strictly financial data
(such as sales, investment etc) along with some non-financial (such as employment and innovative activity) in a common model. The process followed,
included a correlation matrix, a non parametric test and finally an econometric model (logistic regression), in order to verify our findings and reveal the exact
effect of each parameter. Findings strongly indicate that the development of real in-house innovative activity seem to be of major importance, with firms
following that strategy to present higher rates of growth.
Key Words: Innovation, Pharmaceuticals, Firm’s Growth
JEL codes:C25, G30, L65, O31, O34
1. Introduction-Theoretical Background
Pharmaceutical industry is one of the most fast growing sectors of the economy globally, including some of the largest and most powerful
multinational companies. Its economic strength and ability to deliver innovative products, makes it a major industrial asset to the economy. It
is one of Europe’s best performing high technology sectors, presenting high rates of investment activities, in both, tangible and intangible
assets, and of new, innovative pharmaceutical products, generating high economic results. European Commission (2007), underlines that
Pharmaceuticals are also one of the top R&D investing sectors, and according to EFPIA [2007], it present the highest ratio of R&D
investment to net sales at worldwide level and in European Union accounts for approximately 3.5% of the total manufacturing value added,
17.5% of the total EU business R&D expenditure and 5,4% of the total manufacturing exports [p.7]. Very important is its contribution to the
employment too, as it generates three to four times more employment indirectly - upstream and downstream - than it does directly, with a
significant proportion being high value added jobs (e.g. clinical sciences, universities, etc) [p.9].
As regard to Greece, recent data present that the pharmaceutical industry follows high rates of growth (8.5%) along with high
profitability, even in periods that the economy faced economic distress. Lately, the competition became more intensive, and the prospects for
the following years (because of the ageing of the population, and the improvement of the quality of life) seem to be even better [Statbank:
2004]. According to official data [FEIR: 2003] 2 pharmaceutical expenses seem to rise faster than the increase of GDP in the beginning of the
present decade, and the production rise continuously, along with the investments in the sector. Very important is also the contribution to the
employment, with 11,350 employees to work in the area and many more in dependents sectors, like the commercial part of the
pharmaceutical products (wholesale and retail). Other published indices, show that nine (9) pharmaceutical industrial firms are in the top 100
companies in terms of sales (turnover) in the whole economy, with the vast majority of them (7), to be subsidiary companies of multinational
enterprises [ICAP: 2008].
The latter demonstrates one of the basic characteristics of the last decade in the sector: the limited presence (and activity) of domestic
companies, which confront a continuous shrinking of their share. The bigger part of the market seems to be controlled from subsidiary
companies that produce in third party facilities and have developed a large network of health centres and retailing companies, or from
companies that produce using foreign know-how, through the acquisition of intellectual property rights (IPR). It is indicative that Greek
pharmaceutical trade-balance for 2005 was -1.800m € (with more than 60% of market share to concern imported medicines), and the real
R&D activity was the lower in EU-15, calculating in 36m € [EFPIA: 2007]. So, have domestic firms any chance to survive and prosper in that
fast growing sector? Disclosing some of the factors that contribute to firm’s growth, we wish to shed light on that issue, suggesting directions
that entrepreneurs may follow when developing their strategy.
Research in the field of economic development of pharmaceutical firms is rather poor, presenting mainly descriptive statistics concerning
the basic economic results, without thorough examination of the factors affecting them. Companies’ (and sector’s) value and size however
make such an analysis very interesting. In the present survey we focused on the impact of R&D activity (Research and Development and
Intellectual Property Rights-IRPs) in Greek pharmaceutical industry, concerning firms’ rates of growth (in terms of sales and generation of
employment). We also tried to segregate real in-house R&D from imported know-how (IPRs), in order to locate any possible differences in
theirs effect in firm performance.
For the purpose of the analysis, we examined and compared the effects of innovative active in firms’ growth, with that of other factors
that are believed to contribute also to the overall performance, such as investments in tangible assets (Fixed Assets), the size of a firm, and its
ownership status. The selection of the above variables was in accordance with what economic theory underlines, as all seem to result in
higher rates of growth. More specific, investment activity is considered to be a source of motive power for firms, as claimed by various
surveys, while firm’s size seem to play major role in growth. Smaller enterprises are believed to be a major part of the economies, with major
contribution to the diffusion of products, innovations, and specialized personnel, along with the generation of employment. As for Greece,
Small and Medium Enterprises (SMEs) are dominant in the whole industry. Finally, concerning ownership status, we diversified independent
1 Ilias Makris is Assistant Professor in Technological Education Institute of Kalamata, Dept. of Finance and Auditing, and Visiting Professor in Un. of Patras,
Dept. of Business Administration, Greece, e-mail: elimak99@yahoo.com, elimak@upatras.gr
Stavros Stavroyiannis is Associate Professor in Technological Education Institute of Kalamata, Dept. of Finance and Auditing, Greece, e-mail:
stavroyian@teikal.gr
2 Foundation for Economic and Industrial Research: A study of Kousoulakou and Fragoulakis

596 International Conference on Applied Economics – ICOAE 2008
firms from subsidiary companies, in order to reveal any possible differences in economic performance (as the latter are believed to have more
chances to grow, being supported from the parent company).
2. Research and Development
Research and Development (and innovative activity in general), is considered to be a crucial factor for technological and economic
development and focused policies are established in order to encourage and support firms to develop such activities (especially Small and
Medium Enterprises), as they seem to contribute not only to economic welfare (in terms of turnover, profitability, etc.), but also to noneconomic
too (generation of employment, establishment of new technologies etc.), and generally, to high and fast rates of growth 3 . To a
recent paper, Kafouros [2005], found that R&D process is correlated positively with productivity growth and sales, especially in high-tech
sectors [p.492], such as tend to be pharmaceuticals as already mentioned, while Delapierre et al [1998], claimed that it results in increase of
the level of sectoral and national economic performance, diffusing innovative products [p.990]. The contribution of innovative firms (mainly
SMEs) to the overall economic growth has been underlined also from Ferguson and Olofsson [2004, p.5], while a survey of Autio and
Parhankangas [1998], concluded that innovative firms, even during economic recessions, seem to increase their employment and sales
generation [p.120]. Surveys for Greece [Tselekidis, et al 2003 and Makris 2007], also found that firms performing innovative activities, enjoy
the last decade higher rate of growth in turnover and employment, than their sector’s average.
As regard to pharmaceutical sector, literature (although limited), concluded to similar results. Indicatively, Jefferson et al (2006), in a
survey concerning China, claimed that one of the sectors that present the highest incidence of R&D performers is pharmaceutical industry,
enjoying significant returns (from increased sales). Also, Penner-Hahn and Shaver (2005), in their study for Japan, showed that international
R&D activities (enhanced with existing research capabilities) increase the performance and the expansion of the pharmaceutical firms.
3. Statistical Analysis
In order to create our sample, we used micro-level data from the database of ICAP’s Greek Financial Directory 4 , for the last decade
(1996-2005). We first located all manufacturing firms activating in Greek economy that produce pharmaceutical products, using the NACE
industry classification (Classification of Economic Activities in the European Community), and choosing the firms that have the code 2442
(that is: main activity, production of pharmaceutical products). We finally collected data from 74 firms. We then excluded all firms that were
established after 1990, because we wanted to avoid possible bias in data set, as most firms in the first years of their operation present usually,
higher rates of expenditure in order to create the necessary infrastructure, with very poor however, economic results. Through the above
process, 51 firms were finally located and the next step was to collect economic data from their balance-sheets for the period examined,
concerning investment in tangible assets (Fixed Assets), Research and Development (expenses for Research and Development and
Intellectual Property Rights 5 ) and turnover (sales). Furthermore, we diversified our sample to completely Greek-owned firms and to
subsidiaries, in order to examine the overall effect of ownership status in firm’s performance, as many companies (with high market share)
produce mainly in third parties facilities importing and using foreign Know-how and do not perform any in-house R&D activity. Finally, a
segregation of the firms was followed, according to their size (number of personnel), as SMEs are considered to achieve higher rates of
growth.
The next step was to make our data compatible for statistical analysis. Thus, we calculated the change in turnover through the period
examined, along with sample’s average rate of change, following the same process as regard to the non-economic factor (Employment). Then,
we gave the value one (1) to the firms that exceed the average rate of turnover’s growth (of the sample), and zero (0) to those that are below
the average. We followed the same process for employment, creating finally two discrete dichotomous variables that we used as dependents
in our analysis.
We then discriminate innovative from non-innovative firms, giving the value two (2) to the firms that present expenses concerning R&D
and IPRs, and one (1) to those that do not perform such activities, creating another discrete variable that we used later as explanatory factor.
As already underlined, such activities disclose the existence of any type of innovative process in the firm. Our next variable (of the same
form) was developed from the rate of investment in fixed assets in the period examined, by calculating the rate of growth in investment in
fixed assets from 1996 to 2005 for each firm, and subtracting that (for each one) from the average sectoral’s growth. Through the above
process, we created a continuous variable taking any value (positive or negative). With that variable we wanted to test the hypothesis that the
intense of the investment activities in fixed assets, is related (positively) to higher growth of sales. Finally, two more variables were
developed, diversifying the firms as regard to:
i) their size:
• 1-50 employees (very small firms) taking the value (1)
• 51-100 employees (small firms) taking the value (2)
• 101-250 (medium firms) employees taking the value (3)
• more than 250 employees (large firms) taking the value (4)
ii) their’s ownership status: to those that are completely Greek-owned (taking the value 2) and those that are subsidiaries of
multinational corporations (taking the value 1). Our scope is to test whether the growth of a company is depended on its status (implicating
that maybe the subsidiary companies are in a better competitive position).
All the variables described, are apposed below (along with their interpretation and codification):
• Dependent variables (discrete):
3 See indicatively findings of various surveys, supported that theory: Reed and Moreno: 1986, Storey and Tether: 1998, LIFT: 1998, Giudici and Paleari: 2000,
Bartjokas: 2001.
4 A Greek private company, considered as the most inclusive database containing financial information of firms of all sectors. The reader may find more
details, in www.icap.gr
5 In that account, are included all the intangible assets that are acquired from the firm, either through buying in or by producing them, with the prospect, to
enjoy monopolistic advantage (through patents-Royalties etc.), and as a result, higher economic performance than its competitors.

International Conference on Applied Economics – ICOAE 2008 597
o Turnover’s growth (DS96_05: change in sales, higher or lower than sample’s average)
o Employment’s growth (DE96_05: change in employment, higher or lower than sample’s average)
• Independent variable
o Innovative activities (Innovation=RandD and IPRs)
o Fixed assets’ change (DF96_05: difference/variance from the average sample’s change - continuous)
o The size of the firm (Size: number of personnel)
o The ownership status (Ownership: whether the firm is completely Greek-owned or is a subsidiary company of a large
multinational company - discrete)
4. Empirical Results
4.1. Correlation and Non-Parametric Test
Our overall scope was to test whether such activation generates higher rates of growth for the firms (in terms of sales and employment),
as generally considered. For that purpose, we firstly tried to locate some possible correlations between the sales’ growth and the established
variables 6 .
Table 1: Correlation matrix
Ownershi
DS96_05 Innovation DE96_05 DF96_05 p Size
DS96_05 Pearson
Correlation
1 ,349(*) ,402(**) ,276(*) ,100 ,090
Sig. (2-tailed) ,012 ,003 ,050 ,487 ,530
Sum of Squares
and Crossproducts
12,627 3,863 4,980
7966,77
1
1,039 2,725
Covariance ,253 ,077 ,100 159,335 ,021 ,055
DE96_05 Pearson
Correlation
,402(**) ,378(**) 1 -,048 ,067 ,309(*)
Sig. (2-tailed)
Sum of Squares
,003 ,006 ,740 ,640 ,027
and
productsCross-
4,980 4,098 12,157 -1349,348 ,686 9,196
Covariance ,100 ,082 ,243 -26,987 ,014 ,184
* Correlation is significant at the 0.05 level (2-tailed).
** Correlation is significant at the 0.01 level (2-tailed).
As we can see, there seems to be strong correlation between our dependent variables and innovative activity, a pre-finding indicative to
the importance of such activities in firm’s performance and growth. Because we wanted to test our findings, we used also the non-parametric
test, Kolmogorov-Smirnov (Z-test), the results of which are apposed to the following tables, considering sales as the variable of interest (table
2) and employment (table 3).
Table 2: Non-Parametric Z-test (sales)
Test Statistics(a) 1
DF96_05 Ownership Size Innovation
Most Extreme Differences Absolute ,356 ,082 ,169 ,306
Positive ,356 ,082 ,169 ,306
Negative ,000 ,000 -,006 ,000
Kolmogorov-Smirnov Z 1,264 ,292 ,601 1,087
Asymp. Sig. (2-tailed) ,082 1,000 ,862 ,188
Exact Sig. (2-tailed) ,059 ,514 ,408 ,022
Point Probability
a Grouping Variable: DS96_05
,001 ,208 ,069 ,011
6 For the overall analysis that will follow, we used the statistical software SPSS 14.

598 International Conference on Applied Economics – ICOAE 2008
Table 3: Non-Parametric Z-test (employment)
a Grouping Variable: DE96_05
Test Statistics(a) 2
DF96_05 Ownership Size Innovation
Most Extreme Differences Absolute ,248 ,056 ,302 ,337
Positive ,248 ,056 ,302 ,337
Negative -,065 ,000 ,000 ,000
Kolmogorov-Smirnov Z ,866 ,197 1,052 1,175
Asymp. Sig. (2-tailed) ,441 1,000 ,219 ,126
Exact Sig. (2-tailed) ,371 ,732 ,060 ,008
Point Probability ,011 ,240 ,016 ,006
The results from the above statistical analysis seem to confirm our thesis, that innovative activity consist a ‘motive power’ for the firms,
contributing to fast growth and economic progress. As it is clear, innovation, seems to be the most significant (Exact Sig.) variable in the
interpretation of sales’ growth (in a 5% ratio), with investment in tangible assets (fixed asset) to follow, however, in a 10% ratio. The results
are the same concerning the employment, with innovative activity to be significant interpreter of employment’s growth, and size of the firm to
follow. According to the latter however, results showed that adversely to the dominant belief, higher rates of growth seem to follow larger
firms and not the SMEs (positive relation between size and increase in employment). Another interesting finding is also that the critical factor
for the generation of jobs seems to be investment in intangible assets (R&D and IPRs) and not investment in tangible ones, while the last
variable (ownership), does not seems to be a significant predictor for growth.
4.2. The Econometric Model
In order to analyse further and verify the results, as they consists very important outcomes, we proceeded to the development of an
econometric model, in order to disclose the exact effects of each one of the factors selected in firm’s growth. The difficulty that we
confronted from the beginning was the fact that we had to manage a rather differentiated data set, in quantitative, qualitative and noneconomic
(employment) form. The already described transformation, allowed us however, to develop an econometric model using Binomial
(or Binary) logistic regression, including all of them in the same analysis. Such models are useful for predictions of the presence or absence of
a characteristic or outcome based on values of a set of predictor variables, when dependent variable is discrete (and dichotomous) and
explanatory variables of any type. That is, the dependent variable can take the value 1 with a probability of success p, or the value 0 with
probability failure 1-p. Logistic regression is a conditional probability approach used to estimate the probability of occurrence of an outcome
or choice. The probability of occurrence of a dichotomous dependent variable is predicted using the coefficients of the independent variables.
Probability models have the advantage that significant coefficients can be interpreted in terms of the relationship with the dependent variable.
Using the Logistic regression coefficients we will be able to estimate odds ratios for each of the significant independent variables in the
model. It is suitable for our survey, as we want to examine the effect of innovative activity (relative to other factors-variables) in firms’
growth.
The use of logistic regression is a common method of analysis in various fields such as psychological, medical surveys etc. However, it is
not met very often, in surveys enabling financial issues. The reason for that is the dichotomous dependent variable, which is easier to be
developed in the former but difficult as regard to financial aspects, where most of the data are quantitative. The last years, research efforts in
financial economics have tried to incorporate logistic regression in the analysis, of financial and non-financial data. Indicatively we refer the
survey of Tucker [2006] who makes a methodological comparison between logistic regression and neural networks in financial modelling,
considering them as useful tools in decision making process in corporate finance, including a variety of aspects. In another recent study, Lowe
and Parvar [2004], used logistic regression (and factor analysis) in order to manage to include qualitative criteria (such as opportunities,
relationships, risks etc.), in firm’s decision making process to bid or no bid for a project, whether, the same process have followed Tselekidis
et al [2001] examining the effects of investment activities in employment’s growth in firms from various industrial sectors. Also, in a
previous work of Makris [2006], a similar model was developed successfully, in order to locate the factors determining the provision of
finance from commercial banks to New Technology-Based Firms, with non-financial criteria to be also included in the analysis.
Our model appears the following form [(through the use of the logit of p. Logit(p) is the log (to base e) of the odds or likelihood ratio that
the dependent variable is 1 (event)]. The dependent variable can take the value 1 with a probability of success (event) p, or the value 0 with
probability failure (no event) 1-p.:
⎛ p ⎞
logit( p)
= log⎜
⎟ = logit(
p)
= a + bi * Xi + ε i
⎝1−
p ⎠
a=the constant,
bi =the predictor variable coefficient
X i =the observed predictor variable values
ε = any random factor external to the model
(1), where
Analytically, (P) is the probability of a firm to belong to those that perform higher rates of growth in turnover (and employment) than the
average of the sample, which is depended on some characteristics (X i), with some parameters (β). Finally, (ε ι) includes any other random
factor, not related to the model.

International Conference on Applied Economics – ICOAE 2008 599
4.3. The Econometric Model
Through the logistic regression analysis, we tried to explore the relationship between firm’s performance (increase in the rate of sales and
the employment) and the explanatory variables (size, innovation, ownership and investment). Both factors seem so far to be strongly
correlated (positively) with the existed R&D process, as the pre-mentioned results underlined.
As we can see to the following tables, the predictability of the model seem to quite good, which validates our analysis, the results of
which are apposed to table 3.
Tables 4-6: Result of the regression (sales)
Model Summary
-2 Log Cox & Snell Nagelkerke
Step likelihood R Square R Square
1 50,747 a a Estimation terminated at iteration number 7 because
parameter estimates changed by less than ,001.
,317 ,424
Observed
Classification Table(a)
Predicted
DS96_05 Percentage Correct
,00 1,00
Step 1 DS96_05 ,00 24 4 85,7
1,00 10 13 56,5
a The cut value is ,500
Overall Percentage 72,5
Variables in the Equation
B S.E. Wald Df Sig. Exp(B)
Step 1(a) DF96_05 ,003 ,002 3,507 1 ,061 1,003
Ownership 1,348 1,024 1,734 1 ,188 3,851
Size -,197 ,357 ,305 1 ,581 ,821
Innovation 2,975 1,274 5,454 1 ,020 19,586
Constant -6,781 2,672 6,442 1 ,011 ,001
a Variable(s) entered on step 1: DF96_05, Ownership, Size, Innovation
As it is clear, innovation is the most important factor in the interpretation of sales’ growth (in a 5% ratio), with investment in fixed assets
to follow (sig. in a 10% ratio). As for the remainder two variables Size and Ownership Status, they proved to be not significant in the
interpretation of the dependent variable. Logistic regression offers us also the opportunity to calculate the exact impact of each explanatory
variable to the dependent one, examining the odds ratio (Exp(B)), which represents the ratio-change in the odds of the event (of interest) for
an one-unit change in the predictor. It is the exponentiation of B coefficients, and is apposed to the last column of table 6. It is clear from the
results, that the impact of the development of innovative activity in turnover’s growth, is almost twenty times the impact of the increase in
fixed assets’ investment, meaning that firm that present such activities, is 20 times more possible to present higher than sector’s average, rate
of sales’ growth.
Similar are the results concerning employment as the dependent variable as apposed below, with innovation to be very important in the
generation of employment (and as a result to firm’s growth), along with size. The latter, although seem to be logical as larger firms need more
personnel, however, is indicative of the minor role of Greek pharmaceutical SMEs in the increase in employment in the sector.
Tables 7-9: Results of the regression (employment)
Model Summary
-2 Log Cox & Snell Nagelkerke
Step likelihood R Square R Square
1 56,624 a a Estimation terminated at iteration number 5 because
parameter estimates changed by less than ,001.
,235 ,319

600 International Conference on Applied Economics – ICOAE 2008
a The cut value is ,500
Observed
Classification Table(a)
Predicted
DE96_05 Percentage Correct
,00 1,00
Step 1 DE96_05 ,00 23 8 74,2
1,00 7 13 65,0
Overall Percentage 70,6
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 1(a) Innovation 2,575 1,150 5,011 1 ,025 13,125
Size ,736 ,357 4,253 1 ,039 2,087
Ownership -,599 ,924 ,420 1 ,517 ,550
DF96_05 ,000 ,000 ,064 1 ,800 1,000
Constant -6,341 2,557 6,149 1 ,013 ,002
a Variable(s) entered on step 1: Innovation, Size, Ownership, DF96_05.
In order to avoid any possible bias in the analysis by considering both IPRs and R&D activity as innovative activity for the firms, and
attribute to them as one variable, we chose to proceed further to the analysis, segregating and include them in the model as separate variables.
The reason was that the latter is real in-house R&D process, while the former includes the acquisitions of intellectual Property Rights or the
‘third parties production’ the so-called as «FASON». As we can see from the results apposed in the following summarised table, only R&D is
really a critical factor concerning higher rates of growth, not IPRs. That is a truly interesting finding, confirming that in-house innovative
process is what firms (especially smaller and independent) should focus on, in order to survive and grow.
5. Conclusions
Table 10: Summarised Process’ Results
Dependent: Sales Dependent: Employment
Nagelkerke ,339 Nagelkerke ,283
R Square
R Square
Percentage 76,5 (0: 82,1
Percentage 76,5 (0: 80,6
Correct
1: 69,6)
Correct
1: 70,0)
DF96_05 ,083 (Exp(B):1,003) DF96_05 ,861 (Exp(B):1,000)
Ownership ,331 (Exp(B):2,747) Ownership ,352 (Exp(B): ,399
Size ,476 (Exp(B): ,773) Size ,086 (Exp(B): 1,804)
RandD ,033 (Exp(B): 4,577) RandD ,019 (Exp(B):5,254)
IPRs ,773 (Exp(B): 1,245) IPRs ,909 (Exp(B): ,918)
In the present survey, we tried to approach a very important sector of the economy, the pharmaceutical industry, which affects not only
the overall economic indices, but also the quality of life. Through the proper transformation of our data set and the development of an
econometric model (logistic regression), we manage to include economic and non-economic factors in the same analysis, trying to cover a
broader research field. The use of such methodology, although not common in economic analysis, proved to be very useful for data sets
consisting of both economic and non-economic data, with dichotomous dependent variables.
Our scope was to examine the role of innovative activity in firms’ growth (in terms of sales and employment), among other factors such
as investment in tangible assets, size of the firms and ownership status (subsidiary company or independent). Our findings seem to confirm
that innovative activity proves to be (in pharmaceutical sector), the most important factor for firms’ better performance.
Furthermore, segregating R&D and IPRs and attributing them as two different explanatory variables, we isolated the effect of active inhouse
innovative process from the acquisition of foreign know-how, and find that only the former can interpret the achievement of high rates
of growth. As subsidiary companies control the major part of the market-share in Greek pharmaceutical industry and achieve higher net
profits’ ratio, the only factor that seems to be able to help the other firms in the sector to survive, is active innovative process. The latter
should be considered thoroughly, from Greek firms which seem to present low rates in that kind of expenses.
References
Autio, E., and Parhankangas, A., (1998), ‘Employment Generation Potential of New, Technology-Based Firms During a Reccessionary
Period: The Case of Finland’, Small Business Economics, 11:113-123.

International Conference on Applied Economics – ICOAE 2008 601
Bartjokas, A., (2001), Financial Markets and Technological Change: Patterns of Technological and Financial Decisions by
Manufacturing Firms in Southern Europe, The United Nations University, Institute for New Technologies (INTECH): Discussion Paper
Series, No. 2001-4, August.
Chirinko, S. R., (1993), ‘Business Fixed Investment Spending: Modeling Strategies, Empirical Results, and Policy Implications’, Journal
of Economic Literature, XXXI (December):1875-1911.
Delapierre, M., Madeuf, B., and Savoy, A., (1998), ‘NTBFs-The French case’, Research Policy, 26:989-1003.
European Commission (2007), EU Industrial R&D Investment Scoreboard (2007): http://iri.jrc.ec.europa.eu
European Federation of Pharmaceutical Industries and Associations-EFPIA (2007), The Pharmaceutical Industry in Figures,
www.efpia.org.
Ferguson, R., and Olofsson, Ch., (2004), ‘Science Parks and the Development of NTBFs-Location, Survival and Growth’, Journal of
Technology Transfer, 29:5-17.
Giudici, G., and Paleari, S., (2000), ‘The Provision of Finance to Innovation: A Survey Conducted among Italian Technology - based
Small Firms’, Small Business Economics, 14:37-53.
Hahn, J, P., and Shaver, M, J., (2005), ‘Does International Research and Development Increase Patent Output? An Analysis Of Japanese
Pharmaceutical Firms’, Strategic Management Journal, 26:121-140.
ICAP (2008), Greek Financial Directory, ICAP, Greek economic database, www.icap.gr
Jefferson, G., Huamao, B., Xiaojing, G., and Xiaoyun, Y., (2006), ‘R&D Performance in Chinese Industry’, Economics of Innovation and
New Technology, 15(4/5) June/July:345–366.
Kafouros, M., (2005), ‘R&D and Productivity Growth: Evidence from the UK’, Economics of Innovation and New Technology, 14(6)
(September):479-497.
Kousoulakou, X., and Fragkoylakis, B., (2003), The market of Medicine, 2003: Foundation for Economic and Industrial Research, (FEIR-
IOBE), published survey.
LIFT (1998), Financing Innovation, A Guide (Innovation and participation of SMEs’ programme, European Commissions Fifth Research
Framework Programme), Luxembourg.
Makris, I., (2007), ‘Innovative process and Firm’s Performance: A survey in traditional and high-tech sectors of Greek Economy’, Global
Business and Economics Anthology, II, December:117-124.
Makris, I., (2006), ‘How the Greek Banks manage risky projects’, Operational Research-An International Journal (ORIJ), Special issue
“Modelling and Decision Support in Financial Markets”, 6 (August):183-195.
Lowe. J.D., and Parvar, J., (2004), ‘A logistic regression approach to modeling the contractor’s decision to bid.’, Construction
Management and Economics, 22:643-653.
Reed, J., and Moreno, G., (1986), ‘The Role of Large Banks in Financing Innovation’. In Landau, R. and Rosenberg, N.(eds), The positive
sum strategy: Harnessing technology for economic growth, Washington Academy Press: Washington:453-465
Statbank (2004), A survey for Medical and Cosmetics, Statbank: Greek economic database, www.statbank.gr,
Storey, D.J., and Tether, B.S., (1998), ‘New technology-based firms in the European Union: an introduction’, Research Policy, 26:933-
946.
Tucker, J., (2006), Neural Networks Versus Logistic Regression In Financial Modelling: A Methodological Comparison,
Discussion Paper, http://citeseer.ist.psu.edu/.
Voulgaris, F., Papadogonas, T., and Agiomirgianakis, G., (2005), ‘Job Creation and Job Destruction in Greek Manufacturing’, Review of
Development Economics, 9(2):289–301.