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M&Amp;Amp;A In Biotech Industry

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Determinants and consequences of M&A activity in the pharmaceutical – biotechnological industry

Alisher Saydalikhodjayev

March 30, 2008

Industrial Organization

Professor J. Likens

ABSTRACT

Traditionally, pharmaceutical firms with large R&D platforms dominated the space of drug innovation; however, the last 30 years produced a large number of smaller research-oriented biotech firms. Advances in the human genome project opened doors to the field of drug innovation to many new players. This event caused a change in the structure of the pharmaceutical-biotech industry, primarily through a hike in M&A activity during the 80’s and the 90’s. The examined study found that large pharmaceutical firms that had gaps in their drug development pipeline tended to engage in acquisitions, while small biotech firms that experienced financial trouble tended to participate in M&A as targets. At the same time, small biotech firms that had promising drug development pipelines and healthy financials chose to grow organically. No significant positive effects of the event of the merger on operations were found after controlling for company’s merger propensity score.

INTRODUCTION

“Formerly, when religion was strong and science weak, men mistook magic for medicine; now, when science is strong and religion weak, men mistake medicine for magic”

- Thomas Szasz

In the capitalist societies of the western world people have become increasingly aware of the importance of healthy life. Anecdotal evidence says that Americans spend most of their medical insurance funds during the last three months of their life. Thus, it’s easy to see why pharmaceutical and healthcare-related business would strive to deliver the best, most effective and reliable products to the consumers. Any proprietary or patented drug that battled cancer or any other feared disease could be sold at high markups in a state of inexhaustible demand. Focusing on life prolongation, healthcare-related industries boosted research in the field of medical science during the twentieth century. The germ theory of disease, which states that microorganisms are the causes of many diseases, was a highly controversial proposal at the beginning of the century but, eventually, it lead to the discovery of antibiotics and hygienic practices. Chemo-therapeutic revolution, which originated during WWII, has lead to major advances in cancer treatment. In the second half of the century, advances in synthetic organic chemistry allowed for controlled synthesis of complex molecular compounds and brought new opportunities to the field of pharmaceutical innovation. During each of these changes in the industry there were both losers and winners in the market, but generally, pharmaceutical firms with large R&D platforms dominated the space of drug innovation.

This status quo changed dramatically with the unraveling of the human genome research. The effort to identify all existing genes, along with their functionalities in human DNA, lead to many new research targets in drug development. Breakthrough in this field would allow scanning a patient’s genes in normal and damaged cells, delivering accurate diagnosis and prescribing customized medicines with a minimal amount of side-effects. Identification of these genes proved to be relatively easy; however, discovering their properties is much more difficult and costlier. Therefore, the immense breadth of possible research makes the investment in R&D a sizeable and very risky commitment. However, it also allows smaller firms to focus on one particular molecule or a set of molecules, which can be developed into a product (vaccine, treatment medication, etc.). Such firms have virtually no assets and consist of a very small management team along with a lab filled with the best researchers in the field. They start up with no sales force and no product. Their hope is to convert the research done in a lab into something marketable and producible at a larger scale. Because bigger pharmaceutical firms have established sales and advertising platforms, it seems reasonable to ask a question, whether some kind of a partnership between a small research-focused firm and a pharmaceutical power player would add value to both. Presumably, the bigger firm would benefit from the transfer of investment risk onto the research company and the latter would benefit from the growth opportunities through the established platforms of a bigger firm.

This reasoning seems to be supported by the consensus in the market: in general, bigger pharmaceutics yield lower valuation multiples than smaller research-oriented firms. This implies that the investors believe in stronger growth prospects of the latter and perhaps, factor a probability of acquisition by a pharmaceutical player in the price of a small firm. What other incentives may exist for small biotech firms to partner up with bigger pharmaceutical companies? Potential causes and effects of mergers and acquisitions in this developing sector are examined by Patricia Danzon, Andrew Epstein and Sean Nicholson (2004).

PAST LITERATURE

Danzon, Epstein and Nicholson argue in their paper that the determinants of M&A activity in the pharmaceutical-biotechnological industry are split between large and small firms. For the large ones, M&A is a primary answer to holes in the drug development pipeline; while for the small ones, it’s a primary exit strategy in situations of financial distress. The authors also criticize some common explanations for the M&A activity – economies of scale in research in development and desire to achieve higher market power. Moreover, as the authors analyze effects of mergers on future company profitability and operations, they find no significant difference in enterprise value, sales and employees among the firms that merged and the ones that haven’t. In fact, smaller firms have experienced a relatively slower growth in R&D expenses in the first year post-merger, which may suggest that the integration expenses due to the merger absorb the cash required to finance R&D among small firms.

As I go through a more detailed analysis of this work, I will also incorporate some case studies that were used by Louis Galambos and Jeffrey Sturchio in their study of strategic transition of pharmaceutical firms into biotechnology (1998). This paper demonstrates how the players in the pharmaceutical industry adjusted to the new setting, in which biotech research presented a promising opportunity to some and a threat to the market position of the others.

Only 20% of worldwide sales in the pharmaceutical and biotechnological industry was attributed to the ten largest firms in 1985. In 2002, this ten-firm concentration ratio rose to 48%. While the overall industry concentration remained relatively low, the value of the M&A activity in it exceeded $500 billion between 1988 and 2002. Common explanation for this hike in transactions was the existence of economies of scale in research and development (R&D) and in sales and marketing. However, the number of registered compounds by FDA has deteriorated since 1996 even though R&D spending has risen. Also, it is worth noting that the biggest pharmaceutical firms had very different R&D capabilities then many of the start up biotech companies.

The growth of biotech firms was spurred by the human genome research. In the 70’s, the discovery of recombinant DNA, which is simply a combination of DNA sequences that would not occur naturally, allowed scientists the means to produce any quantities of a specific molecular compounds within a body of bacteria. This innovation sparked growing interest in the field of genetic engineering but it also spread fears about the potential destructive consequences thereof. The uncertainty related to government controls and ability to patent genetic innovations seemed to cause the pharmaceutical executives to hold off their investment in this new sector. However, these risks did not discourage the venture capitalists and biotech scientists from continuing their research. On the other hand, large pharmaceutical companies continued their proven strategy of investing into the fields of microbial biochemistry, which studies the principles of bacterial growth, and enzyme inhibition, which studies the array of molecules capable of binding to pathogenic enzymes in order to decrease or kill their activity. These had a proven track record of being profitable, while the new investments were still too uncertain. Therefore, throughout the 70’s the biotech industry remained relatively small, but continued growing its R&D base in a direction, very different from that of the research done at the bigger pharmaceutical labs.

The first biotech company to go public, Genentech, by 1980 had developed several compounds that had very promising future. This reduced the uncertainty related to biotech, and many large companies began making investments in this field. The most often used strategies were either a quick acquisition of broad biotech research capabilities through a purchase of a biotech firm or more narrow approach, building on specific products that were already in the market or in the R&D stage. In either case there were transition costs in the existing R&D platforms because the existing experts in microbial chemistry or virology did not have the knowledge necessary to do genetic research. Due to this mismatch of skills, which was a result of almost a decade of no investments in biotech on the part of the large pharmaceutical firms, one could argue that the R&D capabilities of the pharmaceuticals were vastly divergent from those of the smaller biotech firms. This is probably one of the major reasons that points against the claim of large economies of scale in R&D of pharmaceutical and biotech firms.

However, this and other hypotheses may still be valid in answering a more general question: what precipitated the strong M&A activity in this sector during the last two decades? The empirical study conducted by Danzon, et al, attempts to answer this question.

The study conducted by the authors proceeds in two stages: examining the determinants of M&A in the pharmaceutical-biotechnological industry from 1988-2002, and analyzing the effect of this activity on their performance. In the first stage, several proposed reasons for merging will be examined: economies of scale and scope, specific assets or capacities (such as foreign companies or new complex technologies), imperfect agency controls (managers fulfilling their egos by attempting to run a bigger company), and a market for corporate control, in which an acquisition is a mechanism of transferring assets to a more efficient manager.

The analysis of the determinants of M&A is different for large pharmaceutical players and small biotech firms because they are organized differently and have different production functions. A fully integrated pharmaceutical firm has an R&D component and a production, marketing and sales components. R&D is a substantial investment (average of 18% of sales) and is highly risky because it does not necessarily produce any revenue. However, it is essential for the second component to function: newly discovered and/or patented drugs generate all of the revenue for these firms. In fact, patent protection is very often the major incentive for the firms to engage in R&D because after patent expiration (patent life lasts on average 12 years) entry of generic drug companies that reverse engineer the drugs with expiring patents undercuts the earnings of the pharmaceutical firm that developed the drug. The way to battle this “entry of generics” problem for the large pharmaceuticals is to generate and patent new compounds to replace the ones in the existing drug.

Small biotech firms, on the other hand, have no or very little production, marketing and sales component, and their biggest asset is their investment in R&D. While most firms start this way, examples of Amgen and Genentech demonstrate that if successful, these R&D focused laboratories grow very rapidly to produce and market their developed drugs. Patent protection is equally important for biotech firms; however, due to the complex nature of genetic drugs, the specificity of the assets of a biotech firm is in itself a barrier to entry of generic drug producers. In other words, some compounds are simply too complex to reverse engineer and this specific knowledge is an added barrier to entry that protects the innovation generated by a biotech company.

This reasoning demonstrates why the determinants of M&A may be inherently different for a big pharmaceutical company versus a small research-focused firm. One of the claims made by the authors is that pharmaceutical firms may face excess capacity in their second component of the production function (productions, sales, marketing) when they have gaps in their R&D pipeline for new drug developments. In order to maximize efficiency it may make sense for such a company to either reduce the scale of their second component, or to augment their R&D by acquiring another company that has a strong R&D pipeline. The first option may lack in efficiency due to the loss of firm-specific physical and human capital in the process of scaling down. For a small biotech firm, the authors hypothesize that mergers are an exit or a growth strategy.

Interestingly enough, when the pharmaceutical players started investing in the field of biotech, they did so in a variety of ways. Outright acquisitions have occurred, but most companies opted out for some sort of a partnership or ownership of a majority stake in a company. For instance, Eli Lilly, who had a dominant position in the US market for insulin, promptly reacted to a threat from Genentech that started producing human insulin synthetically, by signing a long-term $40 million production agreement with the biotech firm. Merck & Co., Inc., partnered with Chiron, a biotech firm based in San-Francisco, and some university research labs to develop the first genetically engineered vaccine for humans, a hepatitis B vaccine Recombivax. Other firms, like SmithKline, Johnson & Johnson, and Pfizer adopted similar strategies of forming partnerships with biotech firms to build up biotech capabilities of their own.

These actions may or may not be classified as M&A activity, but the authors choose to only look at “transforming mergers”, which represent “acquisitions exceeding $500 million or 20% of the market value of the buyer or the target”. While somewhat arbitrary, this definition is used to identify such transactions that required significant reorganization in order to integrate the acquired company into the parent. Over the period from 1988-2002 there were 202 recorded transforming mergers, 213 “large” and 170 “small” companies. “Large” firms have reached an enterprise value of $1 billion at least once during the period, and “small” ones never achieved that threshold but had sales of at least $20 million in at least one of the years.

The authors used a regression model in which the dependent variable is the probability that a firm will engage in a transforming merger activity in year t, and the independent variables are the following firm characteristics in years t-1, t-2, t-3:

Gaps in the drug development pipeline

To test the hypothesis that large firms engage in M&A activity in order to maximize the use of the production capacity, the authors choose a set of variables that reflect the expectation of the gaps in the drug development pipeline. The selected variables are: market to book value of assets, percent change in sales and percent change in operating expenses between year t-3 and t-1 and the percentage of firm’s drugs whose patents are close to expiration. Market to book value of companies with strong pipeline should be higher because the value of revenues from drugs in development is not reflected in the value of book assets, but is reflected in the market expectation about the firm. Therefore, the companies with low market to book ratio should be more likely to engage in an acquisition. Slow growth rate of sales should imply that the productivity of the production, sales and advertising factors are declining, which in turn implies an excess capacity in this component of a firm. So a firm with a slow growth rate of sales is also a likely acquirer. Slow growth rate of operating expenses may signal that the company is trying to reduce operating expenses in order to maintain revenue growth due to the expectation of weaker product development in the future. The last measure is the fraction of drugs in the company’s portfolio that were approved by the FDA between nine and 14 years ago, which measures the degree to which the company is �threatened’ by potential gaps in product development pipeline. While the average life of patents granted by the FDA is between 17 and 20 years, much of this time is used for regulatory approvals, hence the sales period is shortened to 9 to 14 years.

Economies of Scale

While the authors criticize the hypothesis that economies of scale are a significant determinant of M&A, they test it using the logarithm of enterprise value and the number of drugs that the company has in the market. The hypothesis is that the smaller firms are more likely to merge since they are at the lowest level of efficiency due to small scale.

Corporate Control

Firms with low market to book ratios are more likely to be targets according to the hypothesis that assets are transferred to more efficient managers. In addition to that, slower growth in revenues and high growth in operating expenses may signal that the company is run poorly, which makes it a more likely target. This market-based hypothesis is supported by the commentary on Wall Street, quoted from a research report of Donaldson, Lufkin & Jenrette (Mar 22, 1999): “The disparity in stock performance between large-cap and small-cap biotechs may facilitate consolidation within the industry as successful companies leverage their high stock prices to make product acquisitions or outright company acquisitions.”

Specific Asset Acquisition

Are foreign firms more likely to merge with the domestic ones in order to improve their access to the US market? In order to test this hypothesis, the authors include an indicator variable for foreign firms.

Agency Issues

If agency controls are imperfect and the manager has a lot of cash to spare, they may have an incentive to engage in M&A to run a larger company. A ratio of cash to sales is used with an expectation that a higher ratio correlates with a higher probability of merging.

RESULTS OF THE FIRST STAGE

In the sample of large firms, the probability of being an acquirer was shown to be negatively affected by the market to book ratio, number of marketed drugs, and the lagged percent change in sales. However, none of these coefficients were statistically significant. Percent of drugs approved 9-14 years ago, log of enterprise value, percent change in operating expenses, foreign firm indicator, and ratio of cash to sales were all positive. Only the fraction of old drugs and enterprise value were shown to be statistically significant though. This confirms the �gaps in the drug development pipeline’ hypothesis, since the firms with higher fraction of old drugs are more likely to be acquirers. The effect of the market to book ratio is not statistically significant, likely because of the mixed implications that this variable has on different hypotheses.

If the large firms were motivated by economies of scale, we would see that the large value of enterprise value is negatively correlated with the probability of engaging in a transaction, however, the regression tells us a different story. Enterprise value positively affects merging, which may suggest that the firms see economies of scale to be valuable even at already large companies. This outcome is consistent with the fact that the field of genetic engineering has provided such a vast array of research targets that even very large corporations cannot engage in enough R&D to span all of the possibilities. This also supports the observation of a duality in the market: the 10-firm concentration ratio has been increasing, but the number of small biotech startups has been growing as well, all due to the fact that the opportunities in this field are immense.

For the same sample of large firms, the probability of being a target is negatively correlated with the value of market to book ratio, number of marketed drugs, fraction of old drugs, percent change in sales, foreign firm and ratio of cash to sales. Coefficients of market to book variable and indication of a foreign firm are statistically significant. Positive correlations are observed with the log of enterprise value and percent change in operating expenses. Coefficient of the log of enterprise value is statistically significant. These results have interesting interpretations, primarily because coefficients of many variables have the same sign as in the regression for the acquirer. The effect of market to book value is a whole magnitude smaller than in the previous regression, but it’s still negative. Explanation for this could be that the targets are undervalued by the market and/or they confirm the hypothesis of corporate control and efficiency: poorly run companies are not valued highly in the market and present an opportunity for acquisition by more efficient managers. This proposition is also supported by a negative coefficient on the percent change in sales and a positive coefficient on the percent change in operating expenses.

Positive coefficient of the log of enterprise value is several magnitudes smaller than that of the first regression, but it still signifies that size of a company matters in the probability of its engaging in M&A. This could probably be attributed to the economies of scale argument, since within a large sample of companies most would be pharmaceuticals rather than biotech firms, which implies that returns to scale in the R&D sector may exist due to the fact that many of the pharmaceuticals must have retained their synthetic organic chemistry units, which clearly benefit from larger scale.

The choice of large firms to engage in absolutely no M&A activity had statistically significant negative correlations with the fraction of old drugs and the log of enterprise value. This outcome shows that firms with a strong drugs pipeline and relatively small size need not to worry about excess capacity and can benefit more from revenues from existing drugs than would bigger firms with the same amount of drugs in their pipeline. This notion draws criticism to the argument of economies of scale, since the smaller firms with a stronger drug pipeline are also the ones that tend to have more drugs on the market and stronger sales growth. And these firms are likely to not engage in any M&A activity because the value of organic growth is higher for them.

The sample of small firms generates very different results. In the regression for the probability of a firm serving as a target is negatively correlated with market to book ratio, number of marketed drugs, fraction of old drugs and ratio of cash to sales. It is positively correlated with the enterprise value. All of these coefficients are statistically significant and support the proposed hypotheses: transfer of assets to more efficient managers, and an exit strategy for firms that face financial trouble. Since they don’t have many drugs on the market, their fraction of old drugs is also smaller, but this metric does not affect small firms in the same way that it affects bigger ones. Small biotech companies do not experience excess capacity when their drug development pipeline is inactive, rather they are simply forced to sell. With very few drugs on the market and low market valuations, some of these firms have no other option but to sell their R&D platforms to another player. On the other hand, firms with a high market to book ratio, and high number of marketed drugs, as well as a high cash to sales ratio are less likely to become targets in an M&A transaction.

Small firms with high market to book value, high proportion of old drugs and high cash to sales ratio are more likely not to engage in any M&A activity at all. This is different from the large-firm sample in that the financial considerations do not affect smaller firms in the same way: attractive valuation and availability of cash does not make one a more probable acquirer. A higher number of marketed drugs also contributes to a firm’s decision not to engage in any M&A activity. This signifies that a small firm with robust valuation and strong market position prefers to grow organically rather than through M&A.

SECOND STAGE RESULTS

The causal effects of a merger on company’s subsequent performance are harder to follow because M&A activity in this industry has been shown not to be a random event. In other words, companies with higher probabilities of merging possessed certain characteristics pre-merger that affected their post-merger performance in a systematic way. For instance, if firms that expect poor earnings growth due to lacking drug development pipeline are more likely to merge, then the post-merger performance of these may be still worse than that of the firms that haven’t merged. In order to control for this potential bias in the regression model, the authors use a propensity score method to compare a group of merging firms with non-merging ones. In simple terms, if there are two firms with similar characteristics that yield the same propensity to merge for both, then the firm that did not merge can serve as a control for the firm that merged in that year.

After estimating the probabilities of merging in the first stage, the authors use these probabilities to sort all firm-years (singular observations) by their propensity to merge. All observations are then grouped into three separate groups: low, medium and high propensity score. This methodic should tackle the merger exogeneity problem by comparing firms with similar propensity scores; however it is not perfect because no two firms will have exactly the same score. The goal is to observe any significant differences in several operating statistics and market performance among the firms that merge and do not merge within the same group of propensity scores. The authors choose to look at percentage change for each firm between year t+1 and t+2 in five different performance measures: operating profit, enterprise value, sales, employees, R&D expense. The independent variable is now a merger indicator (1 if firm merged in year t, 0 otherwise) for the first set of regressions, merger indicator * merger propensity score for the second, and just the propensity score for the third.

The first set of regressions is intended to show what results would be achieved if mergers were taken as random events, and the second set shows the whether merger effects are still significant if one controls for merger propensity score of a firm in year t. Thus, unlike in many other regressions, the results of this regression are significant even if coefficients are not and r-squared is very low. In this case one can infer that mergers do not produce any significant positive outcomes for firms with similar merger propensities (i.e. with similar operating and financial characteristics). However, statistically significant results would demonstrate that mergers do indeed create value for the companies in operating or financial trouble.

The following results were obtained in the sample of large firms. Operating Profit, Sales, Number of Employees, and R&D expenditures were all negatively correlated with the event of a merger. Thus, assuming that mergers are exogenous, one can arrive at a conclusion that mergers slow down growth and disrupt R&D in the first year after the merger event. This may be true since integration effort can be a challenge and companies may choose to sacrifice some growth in order to complete the integration process as early as possible. Enterprise value had a positive but a statistically insignificant correlation, which is consistent with a rational investor hypothesis that says that investors are able to incorporate expectations into the price before and after the merger, yielding no abnormal returns to consolidation.

However, when the same variables were regressed against the added interaction term and the merger propensity score, the picture changed. Negative effect of merger on sales proved to be statistically insignificant, and the number of employees and R&D expenditures was negatively correlated to the firm’s propensity to merge. Thus, regardless of whether the firm merged in year t or not, the financial and operating characteristics that it possessed prior to year t determined its further decision about growing the number of employees and R&D expenditures. As discussed in the first stage, large firms exhibit higher propensity to merge when they see gaps in their drug development pipeline or face financial distress (low market to book value). This explains the reduction in the employee headcount, since a firm tries to remedy the excess capacity by reducing the employed staff. It also explains why R&D investment is disrupted since at low market to book ratios firm will face a more challenging market for raising funds in order to maintain its R&D platform running.

It’s important to note that none of the described second stage regressions yielded a large enough r-squared value to claim that mergers have much effect on the firm’s future performance. The largest value of r-squared for enterprise value regression was 0.13. This supports the argument that mergers or computed probability of merging have little to no effect on the company’s post-merger performance. This speaks, perhaps, to the inherently random nature of drug discovery: larger R&D platform does not yield a higher expected value of marketable drugs. Larger organization faces more pressure to renew its patented drug portfolio in order to maintain a flow of revenues. Perhaps this kind of pressure on the academicians working on the R&D side is detrimental to their effectiveness, which is why smaller labs have proliferated in the field of biotech.

In an attempt to analyze the effects of the merger over a different time horizon, the authors repeated the set of second stage regressions for percent changes in the five characteristics between years t+1, t+2, and t+2 and t+3. The results are largely similar to the results in the first year after the merger. However, controlling for a firm’s propensity to merge, operating profit three years post-merger is going to increase more for a firm with a higher propensity to merge. So, even though the overall effect of merging is negative with respect to growth in operating profit, the firms that have a larger probability of merging as determined in the first stage are more likely to benefit from a merger. The growth in Sales, Number of Employees, and R&D expenditures in years t+2 and t+3 is negatively correlated with the propensity to merge, which supports the hypothesis that firms that find themselves in financial or operating trouble will struggle regardless of whether they merge or not.

Recall that for the sample of smaller firms the authors found M&A activity to be an exit strategy for firms in financial trouble (low market to book ratio, number of marketed drugs), while strong firms with high market to book ratio and a strong pipeline of drugs are likely to not engage in any M&A at all. These results show up in the regressions on the effects of the merger on future performance. The firms that merge experience a slower growth in operating profit and R&D expense in the first year. However, the firms with higher propensities to merge experience a relatively higher growth in sales, number of employees and R&D expense. These statistically significant results are interesting because they signify that merging may be a more beneficial strategy for a small biotech firm than it is for a large pharmaceutical company, even if both find themselves in some kind of trouble. Merger may be the optimal solution to the lack of funding for the R&D platform of a small biotech firm in financial distress. Therefore, an increase in R&D spending should be expected after such a firm merges.

Just as in the sample of large firms, the effect of mergers on enterprise value is not significant, except for the third year after the merger. Growth in enterprise value in that year is lower for firms that had high propensities to merge and did merge. This may suggest that the market overvalued certain mergers originally, thinking that companies that were acquired provided added value for the buyer. However, the increase in the amount of R&D spending in the first two years for such companies may have been unfruitful, which lead investors to lower their expectations about the value of the firm. This is another clue that speaks to the inherently random nature of drug discovery: no firm can be certain that an investment in a financially cheap but promising research-focused lab will yield any tangible results.

Yet again, the firm’s merger propensity is the major determinant of the post-merger outcomes. Regardless of whether the firm engaged in any M&A activity, it will experience growth rates that are predicated upon its prior performance. That is, firms that were predicted in first stage to merge due to their financial or operating troubles will continue experiencing lower growth in sales, operating profit and won’t have sufficient financing for their R&D platform. On the other hand, firms with strong metrics will continue strong performance regardless of whether they merge or not. This result signifies once again the importance of controlling for firm’s merger propensity score in order to reduce the bias related to merger exogeneity.

CONCLUSION

Like many other industries, the pharmaceutical-biotech industry has experienced a high rate of M&A activity between the middle of 1980’s and year 2001. Companies like Glaxo SmithKline, Pfizer or Monsanto are a result of the sector’s largest mergers. The industry became more consolidated over time, even though the overall concentration in it remained relatively low – no single firm has over 12-13% of the market share. This can be partially attributed to the fact that some of the startup biotech firms decided to pursue organic growth, rather than engaging in any sort of M&A activity. The outcomes of the study examined in this paper show that among large firms (over $20 million in sales and $1 billion in market value), firms with a low market to book value (those with low expected growth), are more likely to acquire another firm. Another important variable in the equation is the fraction of “old” drugs in the product portfolio – the ones that are approaching patent expiration. When included in the system, it forces the market to book value variable to lose its statistical significance. This supports the hypothesis that mergers are a response strategy to tackle short-term excess capacity.

Many of the largest mergers have been thought to create value through savings due to economies of scale, both in R&D and selling, general and administrative expenses. While certain cost-cutting opportunities in the non-R&D sector may emerge, it is not a fact that a larger R&D platform necessarily generates a lower cost structure and thus, higher profits. Also, the authors find the firms with relatively high enterprise value to be more likely to engage in M&A activity, which shows that the perception of economies of scale is very high even at large organizations. It would be interesting to see whether the acquirers with high enterprise value in general tended to purchase small or large firms, since the value of the smaller ones lies mostly in their R&D platform, while at bigger firms other components of production may create value in merging.

Small firms, with enterprise value of less than $1 billion, merge mostly in order to exit the market when they find themselves undervalued or unsuccessful at developing their products. This result is quantified by low market to book ratios, few marketed products and low cash-sales ratio. Unlike the large companies, smaller firms tend to act as targets rather than acquirers, because financing is a bigger issue for the smaller firms than any excess capacity due to gaps in the drug development pipeline. This is also an expected outcome, since smaller firms engage mostly in R&D and have very little sales or production capacity. An interesting result was that small firms with a relatively high market to book ratio, strong number of marketed products and high cash to sales ratios are more likely not to engage in any M&A activity at all. This suggests that organic growth for companies with strong prospects is a better growth strategy than through M&A.

Authors find that M&A activity is not affected by principal-agent problem, in other words, managers do not seem to attempt to take advantage of easy availability of financing, measured by either cash or relatively high value of equity. A further look at the potential differences in manager incentive structure that is particular to the pharmaceutical-biotech industry would be an interesting extension of this portion of the analysis.

The analysis of the effects of M&A on further performance shows that in both the large- and small-firm samples, firms with relatively high merger propensity scores tend to have slower growth in sales, number of employees and R&D expenditures. The importance of controlling for the merger propensity before analyzing correlations between the effects of the merger and the event of the merger is crucial, because it avoids the problem of merger not being a random event. The authors find that for large firms mergers per se had “no effect on the change in enterprise value, sales, employees, and R&D expenses in the three years following a merger”. In other words, firms that experienced some operating or financial difficulties that precipitated their willingness to merge did not necessarily benefit from the mergers. In fact, the ones that merged showed slower growth in operating profit in the third year after merger. Therefore, merger is not necessarily a solution to the problems of large firms in this sector.

Small firms that combined had a relatively slower growth of R&D expenditures in the first year compared to firms with the same propensity score that did not merge. This could be attributed to substantial expenses that companies face during integration, which forces them to somewhat disrupt growth in R&D expenditures. It could also suggest that substantial synergies in R&D are achieved in the mergers of small firms. The enterprise value, which represents the market’s valuation of the company, remained largely unaffected by mergers and propensity scores. This result gives hope that market valuation incorporates most information available and does not over- or under-value mergers.

The examined study gives a cross-sectional analysis of the pharmaceutical-biotech industry using the period of 1988-2001. It challenges the commonly assumed reason for the mergers in pharmaceutical industry – existence of economies of scale in R&D platforms. This reason was quoted in many news articles as well as research reports, and while it could hold true for some mergers, it does not necessarily occur during an integration of any two R&D platforms. The specificity of any particular set of compound that one biotech firm may be researching prevents such economies of scale. The specialized knowledge inherent in the R&D process of biotech labs could even compromise the integration of two independent platforms.

Pharmaceutical-biotech industry is still in its growth stage and its evolving rapidly. Firms that started up as R&D-focused labs today have large production and selling capacities. Genentech and Amgen combined have a market capitalization of $140 billion – only $20 billion short of Pfizer’s market cap. These companies are no longer small players in the industry and can’t be described as purely research-oriented biotech enterprises. They now have their proprietary sales force, production capacity and access to capital markets. We can expect them to act like their pharmaceutical counterparties, rather than start-up biotech firms. Nevertheless, the lack of barriers to entry in this market as well as ample research opportunities will keep the doors to this industry open to start-ups for the foreseeable future. The industry’s largest players can consolidate but the industry as a whole will remain sparsely concentrated, giving a chance to the smallest of the firms to find the cure for AIDS, cancer, and other diseases, so feared by the world.

Bibliography

Agnew, Bruce. “When Pharma merges, R&D is the dowry.” Science, New Series, Vol. 287, No. 5460, pp. 1952-1953. Mar. 2000.

Danzon Patricia, Mark Epstein and Sean Nicholson. (2004) “Mergers and acquisitions in the pharmaceutical and biotech industries.” NBER Working Paper Series. http://www.nber.org/papers/w10536. Accessed on 11/03/07.

Galambos Loius, Jeffrey L. Sturchio. “Pharmaceutical Firms and the Transition to Biotechnology: A Study in Strategic Innovation.” The Business History Review, Vol. 72, No. 2, Gender and Business. pp. 250-278. Summer 1998.

Matraves, Catherine. “Market Structure, R&D and Advertising in the Pharmaceutical Industry.” The Journal of Industrial Economics, Vol. 47, No.2, pp. 169-194. Jun. 1999.

Various Biotech and Pharmaceutical Research Reports. Investext Database.



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