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

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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
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