The Sample and Data

The biotechnology industry of 225 publicly held companies provides the population of firms for this investigation (Burrill & Lee, 1993). The sample from this population was limited to firms which went public since 1982. Thus, the initial sample was limited to 218 firms. These firms were then contacted by phone with a request for a copy of the prospectus from their IPO. A total of 106 companies were willing to provide a prospectus representing a response rate of 48%. However, 15 of these companies were excluded from the sample due to missing data and 2 were excluded because warrants for shares in their parent company were included in the IPO. Thus, our final sample consisted of 89 firms.

To test for potential biases in this sample we compared the average total assets and average total liabilities of the firms in our sample in 1992 to the average total assets and liabilities reported by Burill & Lee (1993) for all 225 public firms. Our sample averaged $11,123,000 in total assets and $3,515,000 in total liabilities. Burill & Lee (1993) reported the average total assets and total liabilities of the 225 public biotechnology firms in 1992 as $11,377,000 and $3,313,000 respectively. In addition, the percentage of non-pharmaceutical health care companies in our sample was 15% and the industry wide percentage, as reported by Burill & Lee (1993) was 17%. Based on these comparisons and the size of our sample, we believe we have a fairly representative sample of the publicly held biotechnology companies.

The data used in our analysis was gathered from (1) the prospectus for each of the initial public offerings by the firms in our sample, (2) Ernst and Young's industry annual reports on the biotechnology industry and (3) the CRSP data tapes.

Dependent Variable

Market Value Added. Market value added is the difference between the company's market value at the time of it's IPO and the capital employed by the company. MVA is a cumulative measure of all of the stock market's assessment at a particular time of the net present value of all a company's past and planned capital projects (Stewart, 1991). It measures how successful the company has been up to that time at investing capital and how successful the market believes it is likely to be in the future.

MVA is calculated from the difference between two figures - an approximation of the fair market value of all the companies debt and equity capitalization and capital employed by the company. The market value is the actual market value of the company's common equity plus the book value of preferred stock, minority interests, long-term non-interest bearing liabilities, all interest bearing liabilities and the present value of all non-capitalized leases. The capital employed by the company is essentially the company's assets less non-interest bearing current liabilities plus certain equity equivalent accounting reserves (Bad debt, LIFO, Goodwill amortization, R&D, Unusual losses). In the case of biotechnology companies the important adjustments are the addition of the accumulated deficit during the startup phase, which we considered and unusual loss, and depreciated R&D. These are both added into total capital because in economic sense they represent investments by the organization and therefore should be considered as part of the capital employed by the firm.

The market value of the firm's common equity was gathered from the CRSP data tapes at the end of the 1st day of trading. The accounting information was gathered from the most current financial statements included in the prospectus for the initial public offering.


Independent Variables

Location. Based on the location of the firm's headquarters, firms were coded into geographic territories based on zip code and MSA (Metropolitan Statistical Areas). These locations were then compared to the eight areas identified by Burill & Lee as concentrations of biotechnology activity. In order to capture the variance in the concentration of these eight areas the location variable is the percentage of the nation's total biotechnology firms located in the firm's specific MSA. A "0" was recorded for firms not in one of the eight geographical areas.

Citation Data. In this study we are using citation analysis as an indication of the quality of the scientific personnel of the biotechnology firm. The names of the top scientists employed by each firm were gathered from the prospectus of the firm's initial public offering. Only full time employees were included in the list in order to control for biases created by firms attempting to increase their visibility/legitimacy by hiring a long list of scientific advisors or consultants. Names of all scientific personnel listed in the prospectus as well as top executives were compiled. We then used the Science Citation Index to gather the total number of citations for each scientist in the firm during his/her career. These citations were then totaled to create a measure of the quality of the scientific team employed by the biotechnology firm.

Patents. From the offering firm's prospectus, a count of the total number of patents held by that firm was obtained. This includes both patents granted directly to the firm and patents in which the firm is the sole licensee.

Rate of Product Development. In the business section of each prospectus the companies report the number of products under development or which have reached the market. Only products which had reached the pre-clinical stage of development or beyond were included. Multiple applications of the same product were counted as a single product. The total number of products was then divided by the age of the firm to create a measure of the firm's rate of new product development.

Relative R&D Intensity. R&D intensity was measured as the total R&D expenditures reported by the firm in the prior year divided by the total expenditures of the firm. The traditional measure of R&D intensity has been R&D as a percentage of sales, but given their early development stage most of these companies have little or no revenue, therefore dividing through by total expenditures was the logical choice to measure the firm's focus on R&D.


Control Variables

Age. MVA is a cumulative measure of the wealth created by the firm. Therefore, the age of the firm in years was entered into the model as a control variable.

# Employees. To control for any possible effects of size on a firm's ability to generate wealth we entered the total number of employees into the model as a control. The number of employees was chosen as the control for size because total assets is being used in the calculation of the dependent variable.



The data was analyzed using ordinary least squares regression. Descriptive statistics of the variables are presented in Table 1. The average MVA of the firms in our sample was $65.7 million. The average firm developed 0.65 products per year and had control of 3.35 patents. With respect to location, the average firm was located in a metropolitan area with 7.4% of the total national biotechnology firms. The average firm spent 60% of its money on R&D. The research of the key scientists of the average firm had been cited 126 times. The average firm was 5.5 years old and employed 66 people. The correlation matrix is presented in Table 2.




Standard Deviation

Market Value Added






Firm Citations






Rate of New Product Development



R&D Intensity






# Employees






Variable 1 2 3 4 5 6 7 8
1. MVA 1.00 0.341 0.430 -0.013 0.354 0.421 0.152 0.066
2. Location 0.341 1.00 0.134 0.004 0.172 0.200 0.106 0.133
3. Citations 0.430 0.134 1.00 0.070 0.219 0.256 0.188 0.001
4. Patents -0.013 0.004 0.070 1.00 0.183 0.096 0.183 0.039
5. Rate of New Product Development 0.354 0.172 0.219 0.183 1.00 0.359 -0.082 0.036
6. R&D Intensity 0.421 0.200 0.256 0.096 0.359 1.00 0.084 -0.177
7. Age 0.152 0.106 0.188 0.183 -0.082 0.084 1.00 0.008
8.Employees 0.066 0.133 0.001 0.039 0.036 -0.177 0.008 1.00

n = 89

Table 3 presents the results of the regression analysis with MVA as the dependent variable. The adjusted R2 for the model is 0.334. The F-statistic is 7.30 and is significant beyond the 0.0001 level. These results indicate that the model is explaining a significant amount of the variation in the MVA and that the model is a good fit for the data.

The geographic concentration variable was significantly positively related to MVA at the 0.05 level. These results provide support for Hypothesis #1. Firms which are located in a cluster of firms appear to create significantly more shareholder wealth.

The firm citation measure was significantly positively related to MVA at the 0.01 level. These results provide strong support for Hypothesis #2. A strong research team, as measured by the citation of their previous works, creates significantly more wealth.

The number of patents controlled by the firm was not significantly related to MVA. No support was found for Hypothesis #3.

The rate of new product development was positively related to MVA at the 0.05 level. These results provide support for Hypothesis #4. Firm's which have a higher rate of new product development appear to create significantly more wealth.

The measure of relative R&D intensity was significantly positively related to MVA at the 0.01 level. These results provide strong support for Hypothesis #5. Firm's which pursue a strategy of intense R&D investments create significantly more wealth.




Variable B Std. Error B Beta T Sig. T
Location 1,811,677 834,960 0.198 2.17 0.03
Firm Citations 103,239 33,430 0.287 3.09 0.003
Patents -1,124,963 900,595 -0.113 -1.25 0.22
Rate of New Product Development 11,442,783 5,910,175 0.190 1.94 0.05
R&D Intensity 54,636,634 20,954,659 0.258 2.607 0.01
Age 1,072,336 1,089,502 0.090 0.984 0.38
# Employees 54,589 60,250 0.082 0.906 0.37

n=89 R2 Adj. = 0.334
F=7.3 Significance of F = 0.0000

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Last Updated 1/15/97 by Geoff Goldman & Dennis Valencia

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