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

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.