Entrepreneurs: A Study of Indian Biotechnology Industry
9.4 Methodology – Model and Data .1 Model
9.4.2 Data and Variables
9.4.2.1 Dependent Variable VC Support
VCF is denoted as a binary variable and it takes the value 1 if VC funding is supplied to a firm and 0 otherwise.
9 Venture Capitalist’s Role in Choosing Entrepreneurs 133 9.4.2.2 Independent Variables
Firm Size
Theoretical reasons why firm size should be related to the capital structure of the firm include economies of scale in lowering information asymmetries, transaction costs, market access, and risk exposure. First, smaller firms may find it relatively more costly to resolve informational asymmetries with lenders and financiers. As a result, they would be more inclined towards VCF. Such effects should be more prominent for start-ups as they are generally more information opaque than existing firms (Berger and Udell, 1998). In the present study, the size is calculated as the log of total assets.13
Asset Structure
The more tangible and generic the firms’ assets are, the greater the firms’ liquidation value, which reduces the financial loss incurred by financiers in the event of the firm defaultering (Harris and Raviv, 1991; Titman and Wessles, 1988). Firms with assets of greater liquidation value get easier access to finance and lower costs of financing, leading to these firms acquiring a higher level of debt or outside financing and less reliance on VC funding. A low liquidation but high intangibility (in terms of ideas) makes it ideal for VC funding (Gompers and Lerner, 2001).
The present study calculates the asset structure as current assets/total assets. Total assets and current assets have been taken till t-1 period for VC backed firms, where t is the year of VC funding. For non-VC backed firms, the average of 2002 and 2003 has been taken.
Firm Age
The biotech industry is characterized by network externalities and positive feedback.
Therefore, early entrants can assemble a large dedicated customer base, which gives them competitive advantage against new entrants. Thus firm age reflects a kind of first mover’s advantages and may have a direct influence on VCs funding. How- ever, for a start-up, this may not have any relevance as all the firms are new. It has been calculated as the difference between the year 2003 and the year of firm’s incorporation.
Firm Alliances
Firms’ alliances signal potentiality of the firm. Inter-firm alliances have the poten- tial to alter the opportunities and constraints that start-ups face in their early years.
Alliances provide myriad advantages primarily associated with the direct or indirect
134 V. Kathuria and V. Tewari access to complementary resources (Chung and Lee., 2000) and to knowledge and other assets for which arm’s-length ties are inadequate (Williamson, 1991).
Alliances may also confer legitimacy to a firm’s operations (Baum and Oliver, 1991, 1992; Miner et al., 1990), which in turn facilitates acquisition of other resources. Alliance advantages are particularly strong when timely access to knowl- edge or resources is essential (Teece, 1992). Faced with great uncertainty about the quality of firms (i.e., information asymmetry), VCs will rely heavily on the firms’
alliances to make judgments about their promise. Studies have shown that in the race for capital, firms capable of attracting alliance partners will outperform com- parable start-ups that lack such capabilities. The present study calculates this by considering the total number of alliances the firm has got with either other related company or institute or marketing agency.14For the VC backed firms, this data has been collected till t-1 period (i.e., a year before the period of funding). Regarding the non-VC backed firms data has been collected till 2003.15
Firm Diversification
Diversification of the firm in other sectors provides it with economies of scale and scope. This should positively affect the VCs’ decision to finance the firm as they may find the venture less risky to finance. Funds can be recovered from the other sector of business to maintain liquidity, hence facilitating easy exit. On the other hand, a diversified firm may also experience some interference from the management, thereby negatively affecting the choice. In the present case, if the firm is diversified into other sectors then it has been assigned a dummy 1 and 0 otherwise.
Management Differences
Management differences may also affect both—the likelihood of obtaining venture financing as well as the early rate of growth and survival prospects of a firm. It signifies if a firm is a subsidiary of a bigger group or a joint venture or a merged firm or has been promoted by some bigger group, its influence on VCF may be varied. This could affect both negatively and positively. VCs may not prefer others to interfere in their activities, which may be the case if the firm is under some other group. On the positive note, it can provide liquidity and alliances. A dummy has been assigned for a firm that is independent and 0 otherwise.
Region
The biotech activities in the country are not well spread across the country. The data shows that the activities are concentrated only in North, South and the West regions. Of the total market of Rs. 23,050 million in 2004, South-based companies accounted for nearly 39% of the business done, while West accounted for 32%, and
9 Venture Capitalist’s Role in Choosing Entrepreneurs 135 the North for 29%. The reasons for several companies to be based in a particular region include good support from associations, availability of research institutes16 for both alliances and for human capital and the presence of leading companies for alliances. This gets favor from the VCF, as a number of transaction and information asymmetries are taken care. In order to see the influence of location, each region is assigned a dummy; if a firm belongs to a particular region 1 and 0 otherwise.
Member of Park
Of late, many Southern states have set up biotech parks. A firm being its member makes it more likely to forge alliance with other firms and enhances knowledge exchange and spillovers. It can avail the opportunity of information flow at a lesser cost. VCs may be interested in such firms, which are in close network with other players. Apart from this, location of large number of firms in a park reduces trans- action cost for the VC (Gompers and Lerner, 2001). In the present study, the impact of influence of biotech park membership is seen using a dummy that takes the value 1 if the firm is a member and 0 otherwise.
Sales Turnover and Sales Growth
The turnover of the firm reflects its potential and capability. VCs want to be sure of the fact that they are investing in a firm, which is capable of standing among others and will offer high sales (and hence significant profits). Sales for the VC-backed firms have been collected till time period t (t being the period of VCF). For the non- VC backed firms, it is the average of 2002 and 2003. Many a time, it is the growth potential that may attract VCF. To see this, growth in sales has also been computed and used interchangeably with sales turnover.
Number of Awards
The recognition of a firm in the industry also counts when the question of funding arises. The award-winning firms may have better contacts and alliances with other companies and research institutions. VCs will have no apprehensions of complicated exit, as due to the recognition they may find many others to purchase their share.
Ideally one should have taken number of patents granted to check for their sig- nificance in VCs decision-making (Engel and Keilbach, 2007). Many scholars have noted the unique role of patents in biotechnology (e.g. Flingstein, 1996; Lerner, 1995; Powell and Brantley, 1992; Powell et al., 1996). A biotechnology firm with a patent is in a favorable position to obtain complementary assets and skills (Pisano, 1990) and is more likely to obtain VC financing and willing partners to support com- mercialization activities (Kenney, 1986; Lerner, 1994). Unfortunately, not all firms
136 V. Kathuria and V. Tewari release data for their patent applications and approvals. Since we could not collect data from the patent office, the total number of awards awarded till time period t (t being the year of VCF) has been used as a proxy. For the non-VC backed firms, data is collected till 2003. For a start-up, the variable may be inconsequential.
Number of Plants
A firm may have a big set up with many plants spread across the country. The larger the base they have, the more they will enjoy economies in production. VCs may look into this factor also for funding the firms. Again, for the VC-backed firms this is calculated till t (t being the period of VCF) and non-VC backed firm till 2003.
The variable, however, may be correlated with the size.
Organizational Characteristics
Organizational structure of a firm may also influence its current and future prospects.
For example, a public limited firm will have more liability and may not prefer taking risk compared to a private limited firm, whose liability is low, thus can take greater risk. On the other hand, a government owned firm may behave differently due to different objective function. These organizational differences have been captured in the present study by taking a dummy for a particular structure i.e., public, private or govt. firm 1 and 0 otherwise.
Profits
With respect to the profits, if a firm seems stable in terms of returns then it is likely to attract VC support. Profits for the VC-backed firms have been collected till time period t (t being the period of VCF). For the non-VC backed firms this period has been taken as average of 2002 and 2003. Alternatively, the growth in profits has been computed to see the rise in profit level. Firms having consistent profit (i.e., positive profit) have been assigned a dummy 1 and 0 otherwise.
Thus, the econometric (probit) model used for the study is:
VCF=α+β1Alliances+β2Member o f park+β3Other industries +β4Plants+β5Pro f its+β6Sales+β7Region
+β8Management di f f erences+β9Firm diversi f ication +β10Organizational characteristics+β11Asset structure +β12Awards+β13Firm age+μ
Where VCF = 1 if the VC funds are supplied; and 0 otherwise. It is to be noted that many of these variables, which have relevance for existing firms, become
9 Venture Capitalist’s Role in Choosing Entrepreneurs 137 meaningless in the case of a start-up. For example, all start-ups will be in the same age bracket, may not have any other plant and patenting is yet to take place. Simi- larly, it is too early to expect profits from the start-ups and so on. Thus for start-ups, the model needs to be modified accordingly.
VCF =α+β1sAlliances+β2sMember o f park+β3sOther industries +β4sSales+β5sRegion+β6sManagement Differences
+β7sFirm diversi f ication+β8sOrganizational characteristics +β9sAsset structure+μ
Where VCF = 1 if the VC funds are supplied to a start-up; and 0 otherwise.
Data Sources
Using different sources, a list of bio-technology firms is compiled. The list indicates that there are nearly 170 firms in the industry. Many of these firms are not only small in size but also in early stage. A number of data sources such as biotech park directories, personal meetings, company websites, Capitaline and other published materials are looked into to see whether the firm is VC funded or not. With respect to exogenous variables, despite searching a number of sources, we could collect information for only 91 firms (of the total 170) since most of the firms are private limited and are not listed anywhere. Of these 91 firms, 42 (≈45%) are VC financed and remaining 49 (i.e.,≈55%) are non-VC financed. Among the 42 VC-backed firms only 11 (≈24%) are start-ups, the rest 31 (≈76%) are late-stage firms. Of these 91 firms, 4 are government owned, hence need to be dropped and for two firms some of the variables were on extremes, indicating that these two firms may be outliers. Thus, the final analysis consists of 85 firms belonging to the sector.
These 85 firms belong to different segments of biotechnology sectors: agriculture, aquaculture, horticulture, human diagnostics, human therapeutics, human vaccines, forestry, engineering, environmental, food, beverage and fermentation, veterinary and energy.
The data for listed firms is obtained from Capitaline—a computer-based database from the Capital Market. The Capitaline data comprises the Bombay Stock Exchange listed companies in different sectors. It compiles annual report on a wide range of firm characteristics including sales, profits, R&D17activities, plants, patents, organizational characteristics, etc. As mentioned, the firms in biotech indus- try are mostly private firms; number of other sources were looked into, still the sales, profits and R&D data were not available for all the firms.
Information regarding the awards, diversification of firm has been collected using business magazines, newspapers and company websites. For membership of the park, the data sources are the ICICI knowledge park directory and Genome Valley Directory, company websites among others. Thus, the study looks into the factors affecting VCs decision for 85 biotechnology firms that began operations in India during the 28-year period between 1975 and 2003.
138 V. Kathuria and V. Tewari