The Geography of Markets for Technology: Evidence from Bio- Pharmaceuticals

Transcription

1 The Geography of Markets for Technology: Evidence from Bio- Pharmaceuticals Michelle Gittelman Department of Business and Management Rutgers Business School Newark-New Brunswick, New Jersey

2 The pharmaceutical industry value chain Old Organizational Paradigm Discovery Preclinical development Clinical development Approval Marketing 3-6 Years 6-7 Years ½ - 2 years 5,000-10,000 compounds 250 compounds 1 drug New Drugs 5 compounds

3 The pharmaceutical industry value chain New Organizational Paradigm Discovery Preclinical development Clinical development Approval Marketing New Drugs Source: Ed Holmes, UCSD, Presentation to Institute of Medicine 2005

4 Geography and licensing markets Biotech represents not only a technological and organizational shift, but also a shift in the geographic locus of R&D Biotech clusters are often outside the geographic footprint of traditional pharma Do licensing market operate effectively across space or does co-location matter for licensing between small biotech and large pharma? (Alcacer, Cantwell, Gittelman, 2010) Interviews with licensing execs Analysis of licensing deals What factors influence the choice of local vs. distant partnerships? (Gittelman, 2011) Serendipitous encounters in local spaces vs. scientific communities that span geographic distance

5 Does co-location matter for licensing? We know that proximity facilitates markets for upstream inputs in biotechnology (scientists, universities, venture capital)... We conceptualize licensing as downstream product markets for biotechnology firms and hypothesize that they may also be subject to similar co-location pressures. Problems in contracting for technology (Gambardella, 2008, Gans, Hsu, Stern 2008): Search costs Information asymmetries Opportunism, monitoring and coordination costs IP uncertainty Technological uncertainty Knowledge transfer If co-location reduces these frictions, we should see proximity increasing the likelihood of licensing 5

6 Interviews suggest that proximity is unimportant in licensing markets 1. Big pharma searches globally for in-licensing opportunities. Licensing a formal organizational activity Our experts know where the frontier is...we follow up on things that pique their interest. We are border blind as to where an asset comes from. There could be great scientists anywhere 2. Patent protection lower geographic bias We don t care if it s coming from the US or Brazil a good compound can come from anywhere - as long as the drug is patented in the US or Europe, where we intend to sell it 6

7 Interviews suggest that proximity is unimportant in licensing markets 3. Biotechs around the world sell globally to big pharma. If you re a small firm in India - all it takes is one BD guy sitting in Manhattan. 4. Post-license collaboration frequently discouraged by pharma Patents give detailed roadmap to technological and legal landscape Extensive due diligence before the license mitigates need for post-license collaboration Big pharma seeks to take control of the property we know how to do it [compound development], they think they do -- but they don t Virtual research teams can conduct modularized projects with little need for frequent interaction or coordination

8 Does localization matter to licensing?

9 Data 36,646 drug compounds (Pharmaprojects) Late 1980s-2006, including failed drugs Ceased (80%), Currently Active (17%), Currently Launched (3%) All licenses Type of firm/organizations developing and licensing the drug Global Pharma Biotech firms Small and mid-sized firms Universities and tech transfer Co-location - Any geographic overlap between two firms at the time of license at the city level 9

10 Selection model Colocation Model, Conditional Logit: Pr(Licenseijk) = f(co-location, city leveljk) i compound j biotech firm developing compound i k one of 25 global pharma Selection model, was drug ever licensed: Pr(Ever Licensedi) = f(compound Age, development status, co-location with any big pharma, fixed effects for therapy codes and each of the 25 Big pharma firms) 10

11 All compounds Licensed to firm j Co-location, firms i and j 0.17** (5.3) Ever licensed Co-locate, any big pharma 0.13** (14.9) Age 0.18** (120.8) Ceased -0.03** (3.7) Fully Launched -0.33** (14.7) Fixed effects, Therapy codes Obs Y Chi Sq ρ Robust Z statistics in parentheses, SE clustered on originating firm. 11

12 Does location matter? Pfizer's Research Technology Center (RTC) is located in Cambridge, MA, one of the richest scientific environments in the world, surrounded by more than 300 biotechnology companies and thousands of scientific innovators in worldleading research hospitals and academic institutions. A key element of our success is the growing, dynamic partnerships with these innovators to address the needs of patients worldwide. Pfizer website

13 Geography of knowledge flows When do firms partner locally for alliance partners, and when do they seek partners in distant locations? Conceptualize local and distant spaces as distinct opportunity sets for acquiring external technology: Distant locations Scientific networks are designed to span geographic distance. Distant partnerships reflect search of scientists for others working on similar problems: strategic, intentional search based on specific in-house R&D expertise. Local spaces Opportunities for serendipitous encounters between individuals/firms who otherwise might not know of eachother. Firms prior broadscope (general) knowledge enable them to exploit local opportunities.

14 Geography of knowledge flows The more a pharma firm has specific experience in a disease market, the more likely it will partner with a distant firm. Geographically distant partnerships will be more similar with respect to in-house R&D than local partnerships Geographically proximate partnerships correlated with general knowledge and technological variety

15 Data 1. Alliances 1038 alliances involving US biotechnology firms and 14 of the largest pharmaceutical companies, (Recombinant Capital) 2. Distance between alliance partners Minimum distance (in miles) between a biotechnology firm and the closest R&D lab of the pharma firm at the time of alliance 3. Drug portfolios of firms (Pharmaprojects) Measure in-house R&D prior to alliance Drugs by disease and by discovery platform (biotech vs. other)

16 Density Distances between alliance partners and non-alliance partners Mindist Dashed red line = distances betw. allied firms only; solid blue = distances all firms

17 Logit regressions of co-location Co-location - Alliance partners are within 100 miles of eachother Firm Disease Experience % of firm s drug portfolio in same disease as listed on alliance Firm Biotech experience % of firms drugs developed through biotech techniques Number of prior alliances by firm Need for collaboration - Co-development or Collaboration on description of alliance Stage of project: Research, Development, Preclinical, PhaseI, etc.

18 Correlation coefficient Figure 3. Correlations between alliances and in-house drugs Alliances < 100 miles and > 100 miles Alliance partners less than 100 miles Alliance partners greater than 100 miles Wyeth Eli Lilly Novartis GlaxoSmithKline Abbott Bristol-Myers Squibb Pfizer AstraZeneca Hoffmann-La Roche Sanofi-Aventis Merck & Co Schering-Plough Bayer Johnson & Johnson Firm

19 Logit models of co-location (all include fixed effects for pharma lab locations & random effects for pharma firms) Co-location=1 if firms are <100 miles Coeff Disease experience pharma firm -3.0*** 1.44 Biotech experience pharma firm 9.23*** 3.25 Disease experience biotech firm 0.60** 0.31 Biotech experience biotech firm 0.04*** 0.31 Alliance experience, Biotech firm Alliance experience, Pharma firm Early Stage -0.43* 0.2 Collaboration 0.32* 0.20 Controls for Prior Alliances, disease specified N 709 Wald Chi Square Log Likelihood SE

20 The geography of licensing Despite global search, co-location increases the probability of licensing Clusters that mix biotech firms & large pharma important nodes in markets for technology Distant and local partnerships leverage different in-house knowledge and generate different complementaries with respect to in-house R&D Distant partnerships deepen technological expertise in specific disease markets Local partnerships associated with general knowledge and technological variety speaks to the importance of proximity for serendipitous learning and exploration

21 Thank you!

22 Bio-regions most likely to out-license but - most drug compounds are never licensed Number of drugs developed, 1990s Outlicensed drugs Internationally licensed* Companies in California and Mass % 12% Global Pharma Firms (Top 14) % 10% Rest of World % 12% Total % 11% Source: Gittelman, Alcacer and Cantwell, *Internationally licensed drugs may also be domestically licensed 22