The effect of collaborations on performance in pharmaceutical and biotech industries - Evidence from U.S.

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The effect of collaborations on performance in

pharmaceutical and biotech industries

- Evidence from U.S.

Master Thesis Mirela Predescu University of Groningen: s2528843 Email: predescu_mirela987@yahoo.com Supervisor: Dr. H.W.J. Vrolijk Co-assessor: Dr. H. Gonenc 20.06.2014 University of Groningen

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Abstract

This paper investigates how collaborations between pharmaceutical and biotech companies influence performance. Theory suggests that collaborations have a positive impact on performance for both categories of companies. For pharmaceutical companies, collaborations can reduce risks and costs; they can acquire rights for products which have a high commercialization potential. Biotech firms can obtain funds to continue their research activities. This study presents both the pharmaceutical companies’ perspective and the biotech entities’ situation. Here, two different analyses are conducted which lead to unequivocal results for the relationship between collaborations and performance. This study fills a gap by adding some empirical evidence to theoretical theories; thus offers a good starting point of further research.

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Acknowledgement

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Tables

Table 1 - Current model and future model for financing ... 12

Table 2 - Sample size for financial purpose ... 15

Table 3 - Sample size for collaborations... 15

Table 4 - Area of the research ... 16

Table 5 - Description of groups ... 16

Table 6 - Results on average before deleting outliers ... 23

Table 7 - Results on average after deleting outliers ... 23

Table 8 - Hypotheses Population ... 26

Table 9 - Results of t-test for U.S. pharmaceutical companies - ROA and ROE ... 26

Table 10 - Results of t-test for U.S. biotech companies - ROA and ROE ... 27

Table 11 - Sample Descriptive Statistics ... 28

Table 12 - Results of regression for U.S. pharmaceutical companies for 2011-2013 ... 32

Table 13 - Results of regression for U.S. biotech companies for 2011-2013 ... 33

Table 14 - Sample size for collaborations in details ... 40

Table 15 - Explanation of variables ... 41

Table 16 - Correlation matrices for U.S. pharmaceutical companies between independent variables ... 42

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Figures

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1 Introduction

It is well known that the 20st century brought several innovations which have revolutionized modern medicine and contributed to improvements in patient well-being. The introduction of some vaccines, contraceptive pill, antibiotics, AIDS medications and other important medications for different illnesses have radically changed human destiny. Today, human beings can expect to live longer than they did a century ago due to some important findings in pharmaceutical and biotech research, followed by many other small steps.

Additionally to human health and life style, this industry has been playing a very important role in world economy. It has been argued that the U.S. pharmaceutical industry is one of the most profitable industries in the U.S. (Public Citizen, 2002; CBO 2006). The International Federation of Pharmaceutical Manufacturers & Associations (IFPMA, 2012) even asserts in its report that in the United States every job in biopharmaceutical supports five jobs outside of pharmaceutical sector like: manufacturing, high-tech, childcare, retail, accounting and more.

Many researchers in this domain acknowledge the importance of the pharmaceutical industry all over the world and the trend of investments in emerging countries. But, the U.S. market is still the leader market in terms of revenues, which overcomes the total of the five largest European markets (Kyle, 2005). Furthermore, approvals of drugs in U.S. are a good signal for the other markets. In other words, if a drug was approved by U.S. authority, there are many chances for that particular drug to be approved in other countries as well. Therefore, taking into account the reasons presented above I consider the U.S. pharmaceutical industry as the best choice for a good representation of this field.

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This study attempts to identify if both biotechnological and pharmaceutical companies having deals reach a better performance than both aforementioned types of companies having no deals. Therefore the analysis is designed in a way that it considers the perspective of the U.S. pharmaceutical companies as well as the one of U.S. biotech companies.

The remainder of the paper is structured as follows: in the second part of the study the theoretical background is presented. In the third part the underlying methodology will be detailed. The results and discussion are presented in the fourth part. Finally, the fifth section concludes and presents the limitations of this study.

2 Background information

2.1 Industry characteristics

Definition

U.S. Census Bureau (2012) defines pharmaceutical industry as a sector where companies are engaged in one or more of the following: manufacturing biological and medicinal products, processing drugs and herbs, isolating active medicinal principals from botanical drugs and herbs and manufacturing pharmaceutical products.

Products

The pharmaceutical industry incorporates two categories of products.

- Chemical based products, which are produced in forms as: pills, tablets, capsules, powders and solution. They have a well know structure and can be easily verified. - Biologicals are derived from living materials (animals, human, plants,

microorganisms) and are more complex.

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2.2 The pharmaceutical industry

The U.S. pharmaceutical industry is a globalized industry which played a very important role in human history and its evolution. In the last century there were many innovations which have changed people lives all over the world by saving lives, treating incurable diseases and decreasing sufferings.

The Industry’s economical role

Furthermore, it is argued that the U.S. pharmaceutical industry is one of the most profitable industries in the U.S. (Public Citizen, 2002; CBO, 2006). Pharmaceutical Research and Manufacturers of America (PhRMA, 2013a) assert in their report that the biopharmaceutical industry is making major contributions to the U.S. economy and this sector provides high quality jobs and strong economic output for the U.S. economy. More than 810 000 people are working in US biopharmaceutical sector and this sector contributes nearly $790 billion in economic output on an annual basis.

The R & D importance

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2.3 The U.S. Market

IMS Institute for Healthcare Informatics estimates in their press release (2010) that the U.S. pharmaceutical market, the world’s largest market, has reached $300 billion in 2009. However, the growth has slowed in recent years because of many drugs which have lost patent protection and that have been replaced by generic equivalents. The impact of the economic recession has contributed to this slowed growth, as well.

IMS Health (2013) found in their study that total dollars spent on medications in the U.S. was $329.2 billion in 2013. Total dollars spending on medications, after a -1.0% decline in 2012 is now up 3.2 percent on a nominal basis. There are some factors which contributed to this increase such as less patent expirations (expiration of patents in 2013 contributed to a lower medicine spending with the amount of $19 billion, compared with $29 billion the previous year), the increase of prices, and more spending on innovative new medicines (36 New Molecular Entities launched in 2013, FDA report, 2014).

Biological medicines are one of the faster growing segments of the pharmaceutical market. United States represents the largest market of biotechnology products in the world. There are more than 1,300 firms which activate in this industry. The U.S. bioscience sector has grown by 6.4 percent, from 2001 to 2010. More than 96,000 jobs were added while 3 million jobs were lost in other industries. (Battelle/BIO 2012)

2.4 Pharmaceutical industry criticism, challenges and opportunities

Pattikawa (2007) presents in her study some criticism which researchers have regarding to pharmaceutical industry

- Suboptimal Allocation of Resources; she argues that the high prices of products in this field are less explained by R & D investments but more by marketing and administration expenses. - Innovation is slow. In the market are more incrementally modified drugs than innovative ones. Despite the continuing increase of R&D expenditure, pharmaceutical companies have less innovative products compared to other high tech industries.

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which have already been approved before, are less costly and risky than investing in a new active ingredient which present a high risk because not too many are known.

Many papers discuss big changes which pharmaceutical and biotechnology industries have to face in the future in order to increase their power market and innovation.

 Christensen et al. (2009), argue in their book that even though pharmaceutical companies have spent lots of money on R&D the industry has been introducing fewer innovative drugs into the market. They predict that the market will be fragmented and it will not be dominated by multi-billion-dollar blockbuster products any more. Industry’s revenues will come from a much wider variety of products, with much lower revenues per products. Furthermore, they believe that small companies can compete with big ones in the discovery and development of biotech products.

 In 2008 Price Waterhouse Coopers (PwC) asserts in its publication that the pharmaceutical industry is at a critical point in their research and development strategies. Many medicines which were launched in the 1990s will lose their patent in the next years. Therefore many companies will be very exposed. The audit company states that only four out of ten companies have enough products in their pipelines to fulfill their sales gap. Furthermore, PwC argues that there will be a new model in the pharmaceutical industry’s sales and marketing workforce, as the industry shifts from a mass-market to a target-market. In this case, there will barriers for smaller entrants because they do not have economic power to sustain a highly marketing process.

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2.5 New models of financing in pharmaceutical and biotech industry

In order to overcome all challenges that companies have been facing, researches have been discussing for many years the need of a new model of financing in this industry.

To optimize R&D processes, JSB Intelligence (2005) presents in its report, the new Pharma model which is based on the network approach that works as a licensing mechanism, and a network of research companies that works on a contract basis. The authors of the report believe that licensing processes would lead to a major growth and companies can take advantages of technology and benefit from cost savings and faster put-on-market time. They also recognize the importance of biotech companies in this field. They assert that biotech companies can focus on narrow portfolios and are more complex than conventional pharmaceuticals. Table 1 contains the old and new model in JSB Intelligence’s vision.

Table 1 - Current model and future model for financing

Current Model Future Model

Lower Success Rates in R&D Higher Success Rates in R&D

No Modifying diseases Disease Modifying

Sequential Integrated Production Product Life Cycle Management

Insourcing Outsourcing

10 to 12 years 3 to 4 years

In house Integrated R&D Network R&D

Low Productivity High Productivity

Source, JSB Intelligence(2005), ‘Emerging Business Models in the Pharmaceutical Industries

2.6 Pros and Cons of collaborations

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companies are characterized as innovative specialized companies. Finally, Tyebjee and Hardin (2004) argue that a more efficient industry will be possible if biotech and pharmaceutical companies are able to continue to negotiate financial deals that will be beneficial for both parties and those that utilize their products.

Agres (2011) presents advantages which pharmaceutical and biotech companies can have from licensing and partnering arrangements if they pursue their respective goals. For Big Pharma this means reducing risk, costs and receiving rights for products with a high commercialization potential which may help on countering sales decrease from generics and filling poor development pipelines. For biotech it means obtaining funds to continue research activities.

A recent article presents biotech companies perspective about alliances with other companies in medical field. Veilleux (2014) admits in her paper that alliances may bring many advantages for biotech companies such as new knowledge and financial resources for R&D, and clinical trials. Big pharmaceutical companies can build bridges with foreign capital and markets. However, as other authors noticed that alliances are not always beneficial or relevant, results of her study show that biotech companies use R&D alliances as a second-best option and they prefer to conduct in-house R&D which are financed by venture capital and capital market funds.

Howells et al. (2008) asserts that R&D outsourcing has specific issues. For instance, the outcome of R & D outsourcing is associated with high levels of risk and uncertainty and information asymmetry. Collaborations in terms of R&D outsourcing are unique events which are not similar with other transactions. R&D outsourcings that prove unsuccessful can have a negative impact on the long term future of the firm. But most importantly, very often both companies are involved to some extent in building and producing new medicines which leads to intellectual property rent sharing issues.

2.7 Research questions

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H0: There is no relationship between performance and collaboration with other firms for pharmaceuticals and biotechnology companies.

H1: There is a positive relationship between performance and collaboration with other firms for pharmaceuticals and biotechnology companies

If H1 is rejected, this would point out that intellectual property rent sharing might slow down the performance.

If H1 cannot be rejected, this would confirm the predictions of the literature regarding the advantages of collaborations between pharmaceutical and biotech entities.

These hypotheses are analyzed from two perspectives: the pharmaceutical companies’ perspective and the biotech entities’ view.

3 Methodology

3.1 Data collection

For the purpose of this research, I used Bureau Van Dijk’s Orbis Database and the Bioworld website. Bureau Van Dijk’s Orbis Database provides annual report data for financial analysis. Information about collaborations between pharmaceutical and biotechnology companies was gathered from Bioworld website. Bioworld is an online database that contains several publications about biotech industry. This study only considers publications entitled ‘Collaborations Between Biotech And Pharmaceutical Companies’.

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Table 2 - Sample size for financial purpose

Sample Database Initial sample

size Final sample size Initial Sample Bureau Van Dijk’s Orbis 730 companies 496 companies

Table 3 presents information about the sample size for collaborations between pharmaceutical and biotech companies over a period from 2011 to 2013. These collaborations have worked as a parameter for differentiation of the groups presented in Table 5. The collaborations which were made between subsidiaries of companies were attributed to the principal companies. For more details about dataset I refer Appendix 1.

Table 3 - Sample size for collaborations

Year Raw data Final Data

2011 279 deals 204 deals

Total 570 deals

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Table 4 - Area of the research

Biowold Database - deals

Bureau Van Dijk’s Orbis Database -

financial data

U.S. pharma and U.S. biotech companies with

deals (A)

U.S. pharma and U.S. biotech companies

without deals (B)

U.S. pharma and U.S. biotech companies with deals , but they are not in

financial database (C)

U.S. pharma and U.S. biotech companies without deals, and they

are not in financial

database (D)

Table 5 - Description of groups

Groups Details

Group 1 U.S .pharmaceutical companies with collaborations(deals) Group 2 U.S. biotech companies with collaborations(deals)

Group 3 U.S. pharmaceutical companies without collaborations(deals) Group 4 U.S. biotech companies without collaborations(deals)

3.2 An initial assessment and t – test analysis

In order to present a first general overview, each group was analyzed on average separately and a t-test was conducted in order to identify if there are significant differences between performance means of companies which have deals and entities which do not have, for both categories: U.S. pharmaceutical and U.S. biotech companies.

3.3 Main and control variable

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Following the paper of Lucius et al. (2006) in order to assess the performance of companies in the study two ratios are used: Return on Assets (ROA) and Return on Equity (ROE).

Return on Assets (ROA) is measured by Net income before taxes divided by Total Assets.

The ratio reveals how well a company utilizes its assets to generate earnings.

Return on Equity (ROE) is measured by Net income before taxes divided by Total Equity

and indicates how much profit a company generates with the shareholders investments.

According to H1, the main independent variable is represented by collaborations between pharmaceutical and biotech companies. In order to test this effect on companies’ performance I introduced a dummy: Deals_D, which takes the value of ‘1’ if U.S. pharmaceutical companies have collaborations with biotech companies and ‘0’ if U.S. pharmaceutical companies prefer to be stand-alone.

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In the following paragraphs the control variables and the expected effects on performance are presented.

Scherer (2001) asserts that profitability can be linked with R&D in three different ways. First, successful R&D projects can lead to new products which may add profits to the company. Second, the incomes of the company can serve as a source of funding for R&D. Third, the expectation of profit in certain therapeutic areas can increase R&D expenses for those particular departments. Pattikawa (2007) proves that R&D intensity is positive and significant in relation with performance for a sample of 27 U.S. pharmaceutical companies over a period from 1971 to 2005. Lucius et al.’s (2006) findings in U.S. arrive at the same conclusion. Due to the fact that these authors measure R&D intensity differently, in this study both alternatives are used. First, R&D intensity is calculated as R&D expenditure divided by Total Assets as Pattikawa (2007) proceeds in her paper and second, R&D intensity is measured as R&D expenditure divided by

Sale, as Lucius et al. (2006) calculate in their paper.

A large body of literature and research has proved the relationship between size and firm performance. Beard and Dess (1981) affirm in their paper that size is one of the most important determinants of a company’s profitability in terms of competitive market power in a certain industry. Lucius et al. (2006) and Pattikawa (2007) found that size is positively and significantly correlated with performance. In this study, size is measured in two ways. First, it is calculated as

Log (Total Assets) as Pattikawa (2007) presents it in her paper and second, as Log (Sales), as

measured by Lucius et al., (2006) in their paper.

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included in this model in order to capture capital structure is Total Debt to Total Equity as Pattikawa (2007) uses in her paper.

In order to identify the efficiency of cash flow of companies, this model includes Cash

Flow divided by Total Assets. Pattikawa (2007) proves in her paper that this variable has a

positive and significant impact on performance.

Finally, in order to capture the possible influence of future growth of pharmaceutical companies a Growth variable is included in the model. Pattikawa (2007) does not find significant results, whereas other papers such as that of Shubita and Maroof alsawalhah (2012) found a positive result for growth and performance. Here Growth is measured as Current year’s Sales minus Previous year’s Sales divided by Previous year’s Sales.

3.4 Regressions

The following model is conducted using ordinary least squared regressions. For pharmaceutical companies the following models are presented:

The following models are used for biotech companies:

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Where

= dependent variable, performance = dependent variable, performance

= intercept

= dummy = dummy = dummy = dummy

= control variable, R&D intensity using Total Assets = control variable, R&D intensity using Total Sales control variable, size using Total Assets

control variable, size using Sales = control variable, capital Structure = control variable, capital Structure = control variable, capital Structure control variable, Cash Flow efficiency = control variable, growth

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4 Results

This chapter discusses the difference between U.S. companies having collaborations and U.S. companies preferring to develop as stand-alone, for both pharmaceutical and biotech categories. As a result U.S. pharmaceuticals and U.S. biotech companies with collaborations should have a better performance than the others. In this section I analyze the extent of these differences and if they are significant.

4.1 Initial analysis

An initial assessment on average is made for all four groups. Variables are verified for normal distribution in Eviews (statistical package) and possible outliers are removed. Table 6 presents results for each group before deleting outliers and Table 7 shows the values for all groups after deleting outliers.

Analyzing performance ratios, there can be seen that the only group with positive values is Group 1: U.S. pharmaceuticals with collaborations. Therefore, I can assert that pharmaceuticals companies which have deals have a better performance on average than those that do not have. U.S. biotech companies display negative values on average for performance ratios in both cases. However, the results show that U.S. biotech companies that prefer collaborations have a less negative value on average comparing with U.S. biotech companies that do not have deals with pharmaceuticals companies.

Looking at R&D1, which is related to Total Assets, there can be seen that U.S. pharmaceutical entities have lower levels than for U.S. biotech companies, if they have deals or not. This implies that U.S. biotech companies still prefer to have in-house investments in R&D, even though they have collaborations. The R&D2 variable, which is related to Sales, is highly affected by outliers. For instance, deleting outliers from group 3 and group 4, leads to a much lower level for R&D2, on average.

Size variables show that companies from group 1 are bigger than other groups. Groups 2, 3 and 4 have a similar size.

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concordance with the paper of Custódio et al. (2011) showing that the pharmaceutical industry is one of the industries where short term debt is preferred to long term debt. Furthermore, they assert that large companies as entities from group 1 are keener to have long term debts. TDE is highly affected by outliers. For the analysis purpose in this case, the results after deleting outliers are taken in considerations. The TDE variable show that U.S. companies including both pharmaceutical and biotech with deals have a higher level of debt to ratio than firms which do not have deals. This result does not verify the findings of Zambuto et al. (2011) that the use of collaboration leads to a lower financial leverage. However, this outcome can be explained using Baxamusa et al.’s (2011) findings that display companies prefer equity instead of debt when they have to finance projects with greater information asymmetry regarding to exposed risk such as R&D and acquisitions. U.S. companies from group 3 and 4, which do not have collaborations, have to finance their own projects that imply a high level information asymmetry in terms of risks. Therefore, they may prefer equity instead of debts and group 1 and 2 would choose debts because they already share risks.

CFA levels show that only pharmaceutical companies with collaborations are using efficiently its assets to collect cash flow. The other three groups have a negative value for this ratio.

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Table 6 - Results on average before deleting outliers

ROA1 ROE R&D1 R&D2 Size1 Size2 LDA SDA TDE CFA Growth

Group 1 0.083 0.196 0.067 0.129 7.487 7.203 0.201 0.197 1.352 0.116 -0.02 Group 2 -0.24 -0.664 0.415 7.988 4.978 4.36 0.14 0.349 -0.275 -0.496 8.308 Group 3 -0.219 -0.664 0.363 16.153 4.638 4.345 0.191 0.964 0.833 -0.54 0.989 Group 4 -0.379 -1.05 0.654 67.817 4.363 3.855 0.163 1.876 0.434 -4.256 2.344

Table 7 - Results on average after deleting outliers

ROA ROE R&D1 R&D2 Size1 Size2 LDA SDA TDE CFA Growth

Group 1 0.083 0.196 0.067 0.129 7.487 7.203 0.201 0.197 1.263 0.116 0.013 Group 2 -0.24 -0.664 0.415 4.402 4.978 4.36 0.14 0.308 0.476 -0.454 1.623 Group 3 -0.219 -0.664 0.363 3.184 4.638 4.345 0.191 0.44 0.426 -0.503 0.613 Group 4 -0.379 -1.05 0.654 7.18 4.363 3.855 0.163 0.729 0.118 -0.736 1.067 1

Note: Variables included are: ROA: Return on Assets - Net income before taxes divided by Total assets, ROE: Return on Equity - Net income before taxes divided by Total equity , R&D1 – R&D expenditure divided by Total Assets, R&D2 – R&D expenditure divided by Sales, Size1 – Log(Total Assets), Size2 – Log(Sales), LDA – Long Term Debt divided by Total Assets, SDA - Short Term Debt divided by Total Assets, TDE – Total Debt divided by Total Equity, CFA – Cash Flow divided by Total Assets and Growth - Current year’s Sales minus Previous year’s Sales divided by Previous year’s Sales

1

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Figure 1 presents the relation between collaborations and performance for U.S. pharmaceutical firms. Here, collaboration is measured as the Number of Deals which each company has divided by the Total of Deals of all companies from group 1. Performance is calculated using two ratios as ROA and ROE. Considering that more deals may lead to a better performance, here, results show that this is not the case. For instance J&J, Pfizer and Merck have more deals than Bristol-Myers, Eli Lilly or Baxter International, but their ratios of performance have a lower level than Bristol-Myers, Eli Lilly or Baxter International. The same situation applies for Abbott Laboratories that has a higher number of deals comparing with Forest Laboratories, Becton, Dickinson or Mylan, but a worse performance. Hospira has a lower level for deals and performance, comparing with the other companies. However, this is a small sample which may not be representative. For U.S. biotech companies this analysis is not relevant because many of the U.S. biotech companies which have deals and are included in this sample have one, two or three deals

Figure 1 - US pharmaceutical companies - ROA, ROE and Nr deals/Total deals

Figure 2 displays a cross-border analysis for U.S. biotech companies which have collaborations. This figure consists of 94 U.S. biotech companies which have collaborations with pharmaceutical entities. In Figure 2 it can be seen that the number of U.S. biotech companies which have deals only outside of U.S. is higher than U.S. biotech companies which have deals

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only in the U.S., or both, in and outside of U.S. These results are not consistent with Veilleux’s (2014) findings which show that the level of internationalization of biotechnology firms, defined by the number of international strategic alliances, depends on country-specific features: the bigger the home country of biotech entities is, the smaller the number of international strategic alliances they have. However, my outcomes can be interpreted as a result of globalization where drivers of cross-border alliances are expected to grow even in difficult economic conditions what actually 2011, 2012 and 2013 have offered (Jackson and Schuler 2003).

Figure 2 - Biotech companies - cross-borders analysis for collaborations

4.2 U.S. pharmaceutical and U.S. biotech companies’ performance, a t - test analysis

In order to test whether the performance differs between U.S. companies which have collaborations and those which do not have a t-test was conducted. In this study, this test is used to identify if there is a significant difference between the average of ROA and ROE values of the companies which have deals and entities which do not have, for both pharmaceutical and biotech entities. The average means are compared to see if they are different. Each group is considered as one sample. The null hypothesis is that the differences between companies which have deals and entities which do not collaborate for both categories; pharmaceutical and biotech are zero. Because former studies suggest that collaborations between pharmaceutical and biotech

0 5 10 15 20 25 30 2011 2012 2013

Biotech c. with deals only in U.S. Biotech c. with deals only out of U.S.

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companies might affect performance positively for both types of companies, the alternative hypothesis states that there are significant differences within groups.

Table 8 - Hypotheses Population

U.S. pharmaceutical companies U.S. biotech companies

_{with deals} _{without deals} _{with deals} _{without deals}

0

Where: represents the mean of differences of the samples.

Table 9 displays the results of the t-test for U.S. pharmaceutical companies which have collaborations with biotech companies and U.S. pharmaceuticals companies which do not have deals. Table 9 reports the outcome of the t-test for U.S. biotech entities which have collaborations with pharmaceutical companies and U.S. biotech firms which are stand – alone.

Table 9 - Results of t-test for U.S. pharmaceutical companies - ROA and ROE

ROA ROE Gr1 U.S. pharmaceutical companies with deals Gr3 U.S. pharmaceutical companies without deals Gr1 U.S. pharmaceutical companies with deals Gr3 U.S. pharmaceutical companies without deals Sample 1 33 1 626 1 33 1 626 Included observations 33 626 33 626

Test of hypothesis mean 0 0 0 0

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Table 10 - Results of t-test for U.S. biotech companies - ROA and ROE

ROA ROE Gr2 U.S. biotech companies with deals Gr4 U.S. biotech companies without deals Gr2 U.S. biotech companies with deals Gr4 U.S. biotech companies without deals Sample 1 242 1 286 1 238 1 300 Included observations 242 286 238 300

Test of hypothesis mean 0 0 0 0

Sample Mean -0.24 -0.379 -0.664 -1.05 Sample Variance 0.118 0.127 1.725 2.241 Method t-statistic 4.583 3.18 P(T<=t) one-tail 0.000 0,001 t Critical one-tail 1.648 1.648 P(T<=t) two-tail 0.000 0,002 t Critical two-tail 1.965 1.964

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4.3 Descriptive Statistics

Next, descriptive statistics are presented for variables included in regression models.

Table 11 - Sample Descriptive Statistics: ROA: Return on Assets - Net income before taxes divided by Total Assets, ROE: Return on Equity - Net income before taxes divided by Total Equity , R&D1 – R&D expenditure divided by Total Assets, R&D2 – R&D expenditure divided by Sales, Size1 – Log(Total Assets), Size2 – Log(Sales), LDA – Long Term Debt divided by Total Assets, SDA - Short Term Debt divided by Total Assets, TDE – Total Debt divided by Total Equity, CFA – Cash Flow divided by Total Assets and Growth - Current year’s Sales minus Previous year’s Sales divided by Previous year’s Sales. All variables except for firm size are winsorized at the 1st and 99th percentiles.

ROA ROE

Mean Med Max Min SD Nr. Obs. Mean Med Max Min SD Nr. Obs.

U.S. pharma c. -0.206 -0.140 0.405 -0.972 0.343 659 -0.612 -0.255 1.009 -5.862 1.193 659

U.S. biotech c. -0.316 -0.324 0.599 -0.991 0.354 528 -0.886 -0.533 1.066 -7.202 1.347 538

R&D1 R&D2

U.S. pharma c. 0.301 0.144 3.210 0.000 0.479 698 2.829 0.162 59.740 0.000 9.038 594

U.S. biotech c. 0.519 0.340 5.018 0.015 0.697 635 5.653 1.249 82.082 0.016 13.086 458

SIZE1 SIZE2

U.S. pharma c. 4.762 4.688 8.274 1.052 1.152 762 4.487 4.399 8.089 0.243 1.321 662

U.S. biotech c. 4.617 4.683 7.820 0.243 0.962 672 4.095 4.148 7.271 0.176 1.220 509

LDA SDA

Nr. Obs. Med Max Min SD Nr. Obs. Mean Med Max Min SD Nr. Obs.

U.S. pharma c. 0.166 0.005 3.189 0.000 0.423 735 0.394 0.184 6.428 0.026 0.803 760

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TDE

Mean Med Max Min SD Nr. Obs.

U.S. pharma c. 0.575 0.395 13.091 -9.353 2.437 758

U.S. biotech c. 0.208 0.311 14.246 -18.742 3.466 670

CFA GROWTH

U.S. pharma c. -0.455 -0.197 0.615 -7.784 1.089 739 0.518 0.075 14.141 -1.000 2.152 639

U.S. biotech c. -0.618 -0.392 0.742 -6.086 0.981 639 1.137 0.095 37.914 -1.000 4.808 465

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4.4 Regression results and discussions

Both regressions results lead to a rejection of H1. Table 13 and Table 14 present the findings. In Table 13 Deals_D dummy has a negative coefficient and is insignificant when the dependent variable is ROA. Deals_D has a positive coefficient, but it is insignificant, as well when the dependent variable is ROE. Both results lead to the same conclusion: there is no relationship between performance and collaboration for U.S. pharmaceutical companies.

R&D1 is negative and significant when associated with ROA and with ROE. R&D2 has a positive sign and is significant when associated with ROA and is positive and insignificant when associated with ROE. R&D2 associated with ROA confirms the findings of Pattikawa (2007) and Lucius et al. (2006).

Size1 is negative and significant when associated with ROA and negative and insignificant in relation with ROE. Size2 has a positive sign and is significant when associated with ROA and ROE. Size2 offer the results which confirm the findings of Pattikawa (2007) and Lucius et al. (2006).

LDA is negative and significant in both cases, when linked with ROA and ROE. The relation between LDA and ROE is supported by other papers, as well: Abor (2005) and Shubita and Maroof alsawalhah (2012). SDA is positive and significant in association with ROA, and negative, but insignificant in relation with ROE. TDE is negative and is significant associated with ROA and ROE which confirms Pattikawa ’s (2007) findings.

CFA is positive and significant in relation with ROA and ROE, as I have predicted using Pattikawa (2007) suppositions.

Growth is negative and is not significant neither when it is associated with ROA, nor is it in relation with ROE. Therefore, this result is consistent with Pattikawa’s (2007) results and not with Shubita and Maroof alsawalhah’s (2012) outcomes.

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and positive associated with ROE. But, in both cases, this dummy is not significant, which implies that there is no relationship between performance and collaboration with pharmaceutical from U.S. and out of U.S., for U.S. biotech companies.

R&D1 is negative and significant when associated with ROA and with ROE. R&D2 has a positive sign and is insignificant when associated with ROA and is positive and significant when associated with ROE. R&D2 associated with ROE confirms the findings of Pattikawa (2007) and Lucius et al. (2006). There are many papers which show the positive relationship between R&D expenses and profitability, as some our results confirm this. However, in the literature there are explanations for a negative relationship in this respect. Our results present negative associations, as well. For instance, Wang (2011) explains a combination of a high demand uncertainty and a large investment, which is the case of pharmaceutical industry, R&D activities may not lead to a good performance. R&D activities combined with high risks are thought to affect the performance negatively, as there is a probability of high failure.

Size1 is negative and significant when associated with ROA and ROE. Size2 has a positive sign and is significant when associated with ROA and ROE. Therefore, for U.S. biotech companies, Size2 offers results which confirm the findings of Pattikawa (2007) and Lucius et al. (2006).

LDA is negative and significant when is in relation with ROA and negative and insignificant when ROE is the dependent variable. The association found between LDA and ROA as negative and significant, is sustained by Umar et al. (2012). SDA is negative and significant in association with ROA and ROE. The relation between SDA and ROE confirms the findings of Shubita and Maroof alsawalhah (2012). TDE is negative and significant associated with ROE which confirms Pattikawa’s (2007) findings.

CFA is positive and significant in relation with ROA and ROE, as I have predicted using Pattikawa ’s (2007) for U.S. biotech companies.

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Finally, Adj. R² values show a high value which indicates that the explanatory power of the regression model is high.

Table 12 - Results of regression for U.S. pharmaceutical companies for 2011-2013. The dependent variable is performance which is measured using ROA: Return on Assets - Net income before taxes divided by Total Assets and ROE: Return on Equity - Net income before taxes divided by Total Equity, C indicates the coefficient of the variable and P the level of significance. * indicates a significance level of 10%, ** of 5% and *** of 1%. Deals_D is a dummy which capture if U.S. pharmaceutical companies have collaborations with biotech companies or not, R&D1 – R&D expenditure divided by Total Assets, R&D2 – R&D expenditure divided by Sales, Size1 – Log(Total Assets), Size2 – Log(Sales), LDA – Long Term Debt divided by Total Assets, SDA - Short Term Debt divided by Total Assets, TDE – Total Debt divided by Total Equity, CFA – Cash Flow divided by Total Assets and Growth - Current year’s Sales minus Previous year’s Sales divided by Previous year’s Sales, Observ. is the total amount of observations for which information was available. Adj. R² is the adjusted R-squared. Prob. indicates the significance of the equation. All variables except for firm size are winsorized at the 1st and 99th percentiles.

ROA ROE

Variable Coefficient Prob. Coefficient Prob.

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Page 33 Table 13 - Results of regression for U.S. biotech companies for 2011-2013. The dependent variable is performance which is measured using ROA: Return on Assets - Net income before taxes divided by Total Assets and ROE: Return on Equity - Net income before taxes divided by Total Equity, C indicates the coefficient of the variable and P the level of significance. * indicates a significance level of 10%, ** of 5% and *** of 1%. Deals_US_D is a dummy which capture if U.S. biotech companies have collaborations with U.S. pharmaceutical firms or not, Deals_out_US_D is a dummy which capture if U.S. biotech companies have collaboration with foreign pharmaceutical firms or not, Deals_W_D is a dummy which capture if U.S. biotech companies have collaboration with worldwide pharmaceutical firms or not, R&D1 – R&D expenditure divided by Total Assets, R&D2 – R&D expenditure divided by Sales, Size1 – Log(Total Assets), Size2 – Log(Sales), LDA – Long Term Debt divided by Total Assets, SDA - Short Term Debt divided by Total Assets, TDE – Total Debt divided by Total Equity, CFA – Cash Flow divided by Total Assets and Growth - Current year’s Sales minus Previous year’s Sales divided by Previous year’s Sales, Observ. is the total amount of observations for which information was available. Adj. R² is the adjusted R-squared. Prob. indicates the significance of the equation. All variables except for firm size are winsorized at the 1st and 99th percentiles.

ROA ROE

Variable Coefficient Prob. Coefficient Prob.

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5 Conclusions

While many papers sustain that collaborations between pharma and biotech should have a positive impact on the performance of companies, my study aimed at exploring this relationship using an empirical analysis. The first assessment shows that U.S. pharmaceutical companies which prefer collaborations have a better performance than U.S. pharmaceutical entities which do not have deals with biotech firms. Furthermore, U.S. biotech companies show a negative performance on average in both cases. However, U.S. biotech companies preferring collaborations have less negative values then entities which chose to be stand-alone.

As a second step, a t-test was conducted in order to find out if there is a significant difference between the average of ROA and ROE values for entities with deals and entities without deals, for both pharmaceutical and biotech firms. Results have showed that for both types of U.S. companies, pharmaceutical and biotech and for both performance ratios: ROA and ROE, there is a significant difference between groups of companies which have deals and groups which do not.

Two regressions were conducted in order to identify if the choice to have collaborations represents a significant variable in relation with performance, measured using ROA and ROE. Findings have showed that the dummy used in the first regression in order to capture the use of collaboration for U.S. pharmaceutical, has a negative, not positive, as suggested in this study, and insignificant sign. Given the findings for U.S. biotech companies, dummies which were selected in the second regression had a positive sign, as expected, except the worldwide dummy in correlation with ROA. However, none of these dummies is significant. Therefore, H1 could be rejected which states the positive relationship between performance and collaborations.

A possible explanation for the non-existence of a positive relationship between collaborations and performance can be the profit sharing aspect which characterizes collaborations. In other words, the profit of two companies which collaborate in producing a certain product is shared and therefore, companies cannot retain entire revenue.

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biotech companies, but there is a lack of empirical analysis which can sustain these. By adding some empirical evidence to these theories, this paper offers a good starting point of further research.

This paper has two main limitations. First, regarding the research design for regressions, there are only dummies which capture the existence of the collaborations between pharmaceutical and biotech companies because other information was not available. Maybe an analysis on the estimated value of collaborations can show more insights. Second, the sample consists of information only for 2011, 2012 and 2013. For future research in this domain it is recommendable to build a sample including data from a larger amount of years.

Additional research can use evidence from countries which have high investments in pharmaceutical and biotech industries such as: Germany, Japan and China. An analysis between these countries and The U.S. in terms of collaborations and performance could add some interesting insights to the findings of this study.

6 References

Abor, J. (2005) The effect of capital structure on profitability: an empirical analysis of listed firms in Ghana, The Journal of Risk Finance, Vol. 6, No. 5, 438-445.

Agres, T. (2011) article: Partnering for Success...and Survival2

Battelle/BIO, (2012) State Bioscience Industry Development3

Baxamusa, M., Mohanty, S. & Rao, R. P. (2011) Why do firms issue debt and equity?,

Unpublished working paper

Beard, D. W. & Dess, G. G. (1981) Corporate-level Strategy, Business-level Strategy, and Firm Performance, Academy of Management Journal, 24(4), 663 – 668

2

http://www.dddmag.com/articles/2011/12/partnering-successand-survival

3

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Christensen, C. M., Grossman J. H. & Hwang, J. (2009) The Innovator’s Prescription A disruptive Solution for health Care’, Chapter 8 The Future of The Pharmaceutical Industry, pp. 261 – 309

Congressional Budget Office - CBO, (2006) Research and Development in the Pharmaceutical Industry4

Custódio, C., Ferreira, M. A. & Laureano, L. (2011) Why are US ﬁrms using more short-term debt?, Journal of Finance Economics

Howells, J. Gagliardi, D. & Khaleel, M. (2008) The growth and management of R&D outsourcing: evidence from UK pharmaceuticals, Unpublished working paper, pp 205 - 219

IMS Institute for Healthcare Informatics, (2010) Press release: IMS Health Reports U.S. Prescription Sales Grew 5.1 Percent in 2009, to $300.3 Billion.5

IMS Institute for Healthcare Informatics, (2013) Medicine Use and Shifting Costs of Healthcare: A Review of the Use of Medicines in the United States6

International Federation of Pharmaceutical Manufacturers & Associations (IFPMA), (2012) The pharmaceutical industry and global health, Facts and Figures, page 447

Jackson, S. E. & Schuler, R. S. (2003) Cross-Cultural Management: Foundations and Future, Chapter 7 Cultural Diversity in Cross-Border Alliances

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JSB Intelligence, (2005) Emerging Business Models in the Pharmaceutical Industries, Strategic Analysis of the Pharma Market, Future Revenue Models and Key Players

Kyle, M. (2005) The Role of Firm Characteristics in Pharmaceutical Product Launches.

Forthcoming in RAND Journal of Economics.

Lucius, H.W., Habte-Giorgis B. & Lee, J. (2006) Empirical study of the strategic impact of major marketing factors on firms accounting performance in the pharmaceutical industry,

Unpublished working paper page 5

Modigliani, F. & Miller, M. (1958) The Cost of Capital, Corporate Finance and the Theory of Investment, The American Economic Review, 48(3), 261-297

Pattikawa, L. A. (2007) PhD Thesis: Innovation in the Pharmaceutical Industry Evidence from Drug Introductions in the U.S., pp.17 – 19

Pharmaceutical Research and Manufacturers of America (PhRMA), (2013a) report: 2013 Biopharmaceutical Research Industry Profile.8

Pharmaceutical Research and Manufacturers of America (PhRMA), (2013b) press release: PhRMA Member Companies Invested $48.5 Billion in R&D in 2012.9

Price Waterhouse Coopers (PwC), (2008). Virtual R&D – Which path will you take?10

Public Citizen, (2002) Pharmaceutical Rank as the Most Profitable Industry. Again: “Druggernaut” Tops of All Three Measures Fortune 500 Report. Congress Watch.11

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Scherer F. M. (2001) The link between gross profitability and pharmaceutical R&D spending,

Health Affairs.20, pp. 216-220

Shubita, M.F. & Maroof alsawalhah, J. (2012) The Relationship between Capital Structure and Profitability, International Journal of Business and Social Science, Vol. 3 No. 16, pp. 104 -112

Tyebjee, T. & Hardin, J. (2004) Biotech-pharma alliances: strategies, structures and financing,

Journal of Commercial Biotechnology, Vol. 10, No. 4, pp.329–339.

Umar, M., Tanveer, Z., Aslam, S. & Saijd, M. (2012) Impact of Capital Structure on Firms’ Financial Performance: Evidence from Pakistan, Research Journal of Finance, ISSN 2222-1697 (Paper) ISSN 2222-2847 (Online), Vol. 3, No 9

U. S. Census Bureau, (2012) NAICS: 3254 - Pharmaceutical and medicine manufacturing12

U.S. Food and Drug Administration - FDA, (2014) New Molecular Entity Approvals for 201313

Veilleux, S. (2014) International strategic alliances of small biotechnology firms: a second-best option?, Int. J. Biotechnology, Vol. 13, Nos. 1/2/3

Wang, C. (2011) Clarifying the Effects of R&D on Performance: Evidence from the High Technology Industry, Asia Pacific Management Review 16(1), pp. 51-64

Zambuto, F., Billitteri, C. & Lo Nigro, G. (2011) Capital Structure Decisions in the Biopharmaceutical Industry, International Conference on Industrial Engineering and Operations

Management Kuala Lumpur, Malaysia, pp. 170 - 175

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Appendix 1 Creation of Dataset & Sample Description

A. The financial dataset was built using information from Bureau Van Dijk’s Orbis Database This was done in the following way:

1. Bureau Van Dijk’s Orbis: Gathering data about companies which have the following classification: ‘Manufacture of basic pharmaceutical products, ‘Manufacture of pharmaceutical preparations’, ‘Research and experimental development on biotechnology’, ‘Manufacture of irradiation, electro medical and electrotherapeutic equipment’, ‘Manufacture of medical and dental instruments and supplies and ‘Wholesale of pharmaceutical goods’.

2. Removal of companies without available information

3. Bureau Van Dijk’s Orbis: Collecting data for the following variables: Sales, Return on Assets, Return on Equity, Total assets, Research and Development Expenses, Cash Flow, Long Term Debt, Short Term Debt, Total Liabilities and Debt and Total Shareholders’ Equity. The data was collected over a period from 2011 to 2013.

B. A database which incorporates all deals between pharmaceutical and biotech companies was created using the Bioworld website.

1. Bioworld website: Accessing all publications named ‘Collaborations Between Biotech And Pharmaceutical Companies’ in 2011, 2012 and 2013

2. Removal of deals which were duplicated and for which the names of pharmaceutical companies were not possible to be identified (Table 14)

3. Taking in consideration if there were more than one biotech and one pharmaceutical company involved in the same deal. (Table 14)

4. Taking inconsideration the country for each pharmaceutical and biotech company 5. Building a pivot table providing a structured overview of deals data

C. Setting the area of research for this study: (A) Pharma and Biotech companies with deals, (B) Pharma and Biotech companies without deals

D. Dividing the companies in four groups: U.S. pharma companies with deals, U.S. biotech companies with deals, U.S. pharma companies without collaborations, U.S. biotech companies without collaborations

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were more than one pharmaceutical and one biotech were involve in the same collaboration. For instance, in 2011, there were 2 collaborations where two pharmaceuticals companies and a biotech company were involved. In this case, each pharmaceutical company has been assigned one deal.

Table 14 - Sample size for collaborations in details

Year Raw data

Unidentified Pharma companies Two Pharma companies and one Biotech company Two Biotech companies and one Pharma company Three Pharma companies and one Biotech company Final Data

2011 279 deals 11 deals 2 deals one deal no deals 204 deals 2012 268 deals 7 deals 3 deals one deal 2 deals 187 deals 2013 247 deals 9 deals 3 deals no deals no deals 179 deals

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Appendix 2 Explanations of variables

Name presents the name of each variable. Formula indicates the calculation which is used for each variable. Use is the function of each variable within the regressions. Exp. Sign is the expected direction of effect. TA stands for Total Assets and Log for the Natural Logarithm. Table 15 - Explanation of variables

Name Formula Use Type

Exp. Sign

ROA Net income before taxes/TA Performance Dependent positive

ROE Net income before taxes/Equity Performance Dependent positive

Deals_D Dummy Collaborations Independent positive

Deals_US_D Dummy Collaborations Independent positive

Deals_out_US_D Dummy Collaborations Independent positive

Deals_W_D Dummy Collaborations Independent positive

R&D1 R&D expenses/TA R&D intensity Control positive

R&D2 R&D expenses/Sales R&D intensity Control positive

Size1 Log(TA) Size Control positive

Size2 Log(Sales) Size Control positive

LDA Long Term Debt/TA Capital structure Control negative

SDA Short Term Debt/TA Capital structure Control negative

TDE Total Debt/Total Equity Capital structure Control negative

CFA Cash flow/TA

Cash Flow

efficiency Control positive

Growth

Current year’s Sales minus Previous year’s Sales divided by Previous year’s

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Appendix 3 Correlation matrices

Table 16 - Correlation matrices for U.S. pharmaceutical companies between independent variables

Deals_D R&D1 R&D2 SIZE1 SIZE2 LDA SDA TDE CFA GROWTH

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Page 43 CFA 0.196 -0.594 -0.294 0.416 0.582 -0.036 -0.160 -0.135 1.000 0.000 0.000 0.000 0.000 0.000 0.436 0.001 0.004 GROWTH -0.059 0.154 0.024 -0.060 -0.091 0.005 0.014 -0.003 -0.112 1.000 0.205 0.001 0.598 0.192 0.050 0.915 0.757 0.957 0.015

Table 17 - Correlation matrices for U.S. biotech companies between independent variables Deals_US _D Deals_out _US_D Deals_W

_D R&D1 R&D2 SIZE1 SIZE2 LDA SDA TDE CFA GROWTH

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The effect of collaborations on performance in pharmaceutical and biotech industries - Evidence from U.S.