Entrepreneurs: A Study of Indian Biotechnology Industry
9.5 Results and Interpretations
This section gives the empirical results obtained by using Probit framework. The analysis has been performed in two stages—in stage 1 for all the firms and in stage 2 for two categories of firms separately.
9.5.1 Result – All Firms
In the first stage, analysis is carried out on all the 85 firms of both categories—
start-ups and late-stage firms. Since data consists of firms of different size, heteroscedasticity cannot be ruled out. The test statistics show the presence of heteroscedasticity.Table 9.6gives the results for heteroscedasticity corrected model.
Column 3 gives the marginal effect. Sales figures show high correlation with profits.
Hence both could not be included together in the analysis.
Table 9.6 Factors affecting the probability of choosing a firm for VC funding No. Variables Coefficient value Marginal effects
1 Private −0.61 (0.62) −0.22
2 Independent −0.51 (0.46) −0.2
3 Age of firm 0.04*(0.02) 0.02
4 Alliances companies 1.46*(0.47) 0.53
5 Profits positive 0.77*(0.43) 0.3
6 Member of park 2.46*(0.64) 0.78
7 Asset Structure −0.2013 −0.24
8 Other Industries −0.6726 −0.44
9 Constant −0.58
LR 67
R square 0.56
Notes: Values in parenthesis are standard errors; *indicates variable is significant at minimum 10% level. N = 85.
140 V. Kathuria and V. Tewari Table 9.7 Contingency table(N=85)
Predicted values
0 1
Actual Values 0 38 (86%) 6 (13%) 1 7 (17%) 34 (83%)
It can be seen from the table that VCs don’t prefer a diversified firm, as indicated by the significance of the variable (row 8). One probable reason could be that this divides the funds leaving the firm with less liquidity but also increases chances of managerial interference. The results though not reported, the number of plants and awards don’t seem to have any affect on VC funding decision as indicated by their significance levels. Sign and significance level of Private variable (Row 1) indicates that VCs are not concerned with the fact that firm is public limited or private limited.
They look into ideas, and whichever suits them they finance that particular firm. The results indicate that assets structure has a significant but negative relationship with VC funding (row 7). Since high asset structure means firm is already liquid, it has less desire for VC funding, this is getting reflected in the sign and significance of the variable. Results show that the probability of receiving VC funding is positive if a firm is a member of a park (row 6) and has alliances with other companies (row 4).
This may be reducing information asymmetry. From the marginal effect, it can be inferred that for every 1% increase in biotech membership and forging an alliance, the chances of getting VC funding increases by 0.78 and 0.53% respectively. The model seems to have predicted quite well as indicated by the contingency table (Table 9.7). The table indicates that 86% of the predictions for non-VC backed firms have been made correctly, whereas 83% of the predictions are correct for the VC backed firms.
9.5.2 Late-Stage/Existing Firm Level
The above analysis is carried out on both categories of firms—start-ups and late- stage firms. However, as mentioned, some of the variables like profits, age may have less relevance in influencing VCs choice decisions for start-ups. Thus, in order to see how VCs choice decision is affected by type of firms, the analysis is repeated for both categories of firms separately. Since the sample had only 13 start-ups, a separate analysis could not be carried out for them. Thus the second stage analysis is conducted on 72 late-stage firms only.Table 9.8reports the results for the het- eroscedasticity corrected model. Here also sales figure show high correlation with profits. Hence, both together could not be introduced together.
Results indicate that VCs don’t prefer a firm when it is diversified into other sectors (row 6). Diversification may induce management interference and reduce
9 Venture Capitalist’s Role in Choosing Entrepreneurs 141 Table 9.8 Factors affecting the probability of choosing a late stage firm for VC funding
No. Variables Coefficient value Marginal effects
1 Independent −0.41 (0.52) −0.15
2 Alliances companies 1.55* (0.56) 0.52
3 Profits positive 1.02* (0.51) 0.33
4 Member of park 1.98* (0.63) 0.64
5 Asset structure avg. 2 −0.72* (0.43) −0.25 6 Other industries −1.29* (0.66) −0.44
7 Constant (0.58)
LR 50.04
R square 0.54
Note: Same asTable 9.6. Age was also there but it did not come out to be significant, hence not reported. N=72.
Table 9.9 Contingency table(N=72)
Predicted values
0 1
Actual Values 0 37 (90%) 4 (10%)
1 6 (19%) 25 (81%)
the liquidity in case the other sector is not profitable. The number of plants and awards, however, don’t show significant results for VC funding decision as found earlier.
For this analysis the study uses average of asset structure for 2 years. This could not be used in the previous analysis for all the firms, as the data for startups is not available for all the years. The variable shows a negative relationship with VC funding (row 5) indicating VCs preference for firm, which cannot tap other sources.
VCs decision is not influenced by the age of the firm. As after a certain threshold this age factor may not count. It is only in the initial years that this seems to have any relevance. Similarly, the insignificance of “Independent” variable (row 1) could be due to the fact that organization structure may not matter much if all other criteria perceived important have an impact.
Results also indicate that VC funding is directed towards a firm, which is a member of a park and has alliances with other companies (rows 2 and 4). The model seems to have predicted quite well as indicated by the contingency table (Table 9.9). Ninety per cent of the predictions for non-VC backed firms have been made correctly, whereas 81% of the predictions are correct for the VC backed firms.
A comparison ofTables 9.6 and 9.8indicates that there are factors such as firm type and age which are not relevant for VCs decision to fund an existing firm. How- ever, some of the factors like asset structure, member of the park, profitability etc.
are important factors considered by a VC when they intend to support an existing firm.
142 V. Kathuria and V. Tewari