Full paper here https://www.nber.org/papers/w25202

1. Moving Beyond the Valley of Death
Regulation and Venture Capital Investments in Early-Stage Biopharmaceutical Firms
Yujin Kim Chirantan Chatterjee Matthew J. Higgins
ShanghaiTech University Hoover Institution (Stanford) & IIMA Georgia Institute of Technology & NBER
Bates White Lifesciences Symposium 2019

2. Introduction

3. Introduction

4. Introduction
• Funding early stage ventures has increasingly become difficult. (Kerr & Nanda 2011).
• Novel Scientific Approaches not being explored by firms. (Budish, Roin & Williams 2015).
• Lack of verifiable measures (products, patents, publications).
• Rely on signals for quality (Hsu and Ziedonis 2013, Higgins et al. 2011, Nicholson et al. 2005, Audretsch and
Stephan 1996, Podolny 1993 and Nicholson et al. 2005 among others).
• Can Regulation Reduce These Information Asymmetries And Drive Investment Towards Early
Stage Firms?
• Lerner (2000) looks at relationship between SBIR grants and subsequent VC Funding
• Samila and Sorenson (2011) show that federal R&D funding and VC funding are complements
• Islam et al (2018) study government awards and its effects on VC funding
• Our case is novel in that regulation just provides an information signal, which it seems to indirectly shifting VC
funding.
• Context: EU Orphan Drug Act 2000 and Venture Capital Investments from VentureExpert

5. Summary of Evidence
1) EUODA increased early stage VC investments by 3-5% for treated
biopharmaceutical subfields relative to control subfields affected by
the policy. Translates to about 2000 new early-stage investments.
2) Evidence of regulatory spillovers & cross-border venture
investing. EUODA helped US VCs than EU VCs.
3) EUODA results in lowering of syndication activity by VCs in early
stage investments, though they don’t lower amount raised in an
average round of investment.
4) EUODA also improves exit performance, more IPOs than
acquisitions and no significant effect on bankruptcies for treated firms.

6. Descriptive Evidence from VC Investments

7. Institutional Background on EUODA (EU Regulation 141/200)
• Below 5 per 10000.
• Exceptions allowed for life threatening drugs or for existing treatments.
• Scientific Rationale and Medical Plausibility is factored in.
• Anytime during development till marketing authorization (8-12 months before usually though).
• Protocol assistance reduced/waiver in regulatory fees, accelerated approval pathways, extensions to
medical exclusivity, special benefits for smaller firms.
• ME: 8+2+2 years (5+2 in US).
• Regulation was to incentivize treatment (not financing of firms), in that sense regulation is exogenous.
• Both science-based and market-based signal.
• E(profits) = ρ (TR - TC )

8. Dataset
• VentureXpert Database, 1989-2011 in medical, health and life-sciences.
• 44867 investments in 6643 start-ups by 3072 investing firms.
• Unit of analysis: Investor-Investee-Round.
• Startups on an average are receiving 3.9 rounds.
• Each round 4.8 investors, raises $12.8 million on an average.
• Average time difference between founding and investment 2119 days (about 6 years).
• 21% early stage start-ups, 67% of investors and 80% of start-ups are located in US.

9. Identification Strategy
• Outcomes: whether early-stage; timing of investment; number of investors and invested amount per round; exit
performance.
• i indexes round of investment, j is industry subfield, t is year of analysis from 1989 to 2011.
• Treated Group: sub-fields Biotech-Human, Med/Health Products, Medical Diagnostics, Medical Therapeutics and
Pharmaceuticals.
• Control Group: Biosensors, Biotech Equipment, Biotech Other, Biotech Research, Biotech Animal, Biotech Industrial
and Med/Health Services.
• AfterODA is 1 for investment occurring after 2000, zero otherwise.
• Specifications control for type of VC, location of start-up/investor, includes technology, investor and time fixed effects.
• Results robust if:
• We include Biotech Research and Biotech Other in treatment group from control group.
• Medical Diagnostics are in treatment because of biomarkers (complements to drug development) but if we remove them from treatment
group, results are robust.
• Also if we change time-frames and specifications.

10. A Pre-Trends Test With Sample 1989-1999 (Artificial Treatment:1994)

11. VCs are more likely to invest & earlier in early stage with EUODA
• VCs are 3-5% more likely to invest in early
stage investments.
• Translates to 2000 newer early stage
investments.
• VCs shift their investments by 1-2 years
earlier.
• Models have controls in appendix.
• Results hold with logit specifications.
• Robust to alternative time-frames (3/5/7
years before/after) -> Dynamics.
• Robust to alternative definition of
treatment and control groups.

12. Regulatory Spillovers Effects & Cross-Border Venture Investing

13. Herding Effects Decline with EUODA in Early-Stage

14. But with a Rotation of Invested Amount Increase in Late-Stage
Evidence of Risk Mitigation for Early Stage?

15. Exit Performance: More IPOs, Less M&As, Bankruptcies Unimpacted
In a nutshell more efficient exit performance

16. Summary & Policy Implications
• VCs are more likely to invest in early-stage biopharmaceutical start-ups in EU with EUODA.
• They seem to be doing it earlier too, moving beyond the valley of death.
• Evidence of regulatory spillovers & cross-border venture investing.
• Syndication increases in aggregate, driven by late-stage, and herding reduces in early-stage.
• Amount invested per rounds don’t change in early-stage however, suggesting risk mitigation.
• IPOs more likely than M&As with EUODA for early-stage start-ups.
• Important unintended consequence of regulation for incentivizing science-based risky
entrepreneurial start-ups through an information signal that mitigates risk.
• Pharma/Health innovation financing lessons for antibiotics, precision-medicine, AI-healthcare-
devices.
• Emerging economies can take a cue from the cross-border venture investing results for
climate change, autonomous vehicles, or AI.
• Future work: Drill down for ODA certification and transition probability.

17. Thank you for your kind attention
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