Sequential Innovation and Intellectual Property. A Comparison of Empirical and Experimental Findings

Table of Contents

1. Introduction

2. Related Literature on Sequential Innovation

3. Theoretical Foundation

4. Modelling Sequential Innovation in a Laboratory Experiment

4.1 Experimental Design

4.2 Rules of the Game

4.3 Experimental Assumptions

4.4 Experimental Findings

5. Empirical Findings

5.1 Biopharmaceuticals

5.1.1 Empirical Evidence on a decreased welfare (Finding 1)

5.1.2 Empirical Evidence on less sophisticated innovation (Finding 2a)

5.1.3 Empirical Evidence on increased Self-reliance (Finding 2b)

5.2 Software

5.2.1 Empirical Evidence on a decreased welfare (Finding 1)

5.2.2 Empirical Evidence on less sophisticated innovation (Finding 2a)

5.2.3 Empirical Evidence on increased Self-reliance (Finding 2b)

5.3 Applicability of the empirical findings and critique of the experimental assumptions

6. Conclusion

Research Objectives and Core Themes

This thesis examines the impact of Intellectual Property (IP) rights on sequential innovation by comparing a theoretical laboratory experiment with empirical evidence from the biopharmaceutical and software industries. The study investigates whether patent protection effectively fosters innovation or whether it conversely leads to welfare losses and less sophisticated technological progress.

• The theoretical efficacy of IP rights within sequential innovation frameworks.
• Methodological analysis of laboratory experiments modeling innovation and imitation.
• Discrepancies in the effectiveness of patents between the software and biopharmaceutical sectors.
• Evaluation of experimental assumptions versus real-world industry characteristics.
• The welfare implications of patent-induced shifts from sophisticated to basic innovations.

Excerpt from the Book

Summary of Chapters

1. Introduction: Presents the motivation behind the research, questioning the effect of patents on sequential innovation while introducing the foundational literature and the thesis structure.

2. Related Literature on Sequential Innovation: Reviews existing academic perspectives, contrasting static models with dynamic approaches to innovation and highlighting the limitations of current research.

3. Theoretical Foundation: Analyzes the Bessen and Maskin (2009) model, which challenges the conventional wisdom that patents are always necessary for fostering sequential and complementary innovation.

4. Modelling Sequential Innovation in a Laboratory Experiment: Explains the experimental design based on word-creation games, detailing the treatments and specific assumptions used to replicate market conditions in a controlled laboratory setting.

5. Empirical Findings: Evaluates the experimental findings against real-world data from the biopharmaceutical and software industries to assess their practical applicability and robustness.

6. Conclusion: Summarizes that the laboratory experiment fits the software industry better than the biopharmaceutical sector, and calls for more industry-specific approaches to patent policy.

Keywords

Sequential Innovation, Intellectual Property, Patents, Laboratory Experiment, Software Industry, Biopharmaceuticals, Welfare Losses, R&D, Imitation, Market Efficiency, License Fees, Empirical Findings, Innovation Policy, Cumulative Innovation, Economic Theory.

Frequently Asked Questions

What is the fundamental objective of this thesis?

The thesis aims to determine whether Intellectual Property rights truly foster sequential innovation as traditionally assumed, or if they contribute to welfare losses and diminished innovation quality by comparing lab results with industry data.

Which scientific methodology is applied in this research?

The research uses a comparative approach, contrasting theoretical models from laboratory experiments—specifically the work of Brüggemann et al. (2016)—with quantitative empirical evidence from the software and biopharmaceutical branches.

What are the primary industry sectors analyzed?

The study centers on the biopharmaceutical industry, due to its reliance on high R&D costs and long development cycles, and the software industry, which is characterized by rapid, sequential technological progress.

What does the term "sequential innovation" refer to in this context?

In this paper, it refers to a process where current and future products are built upon existing technological foundations, a phenomenon famously described as "standing on the shoulders of giants."

How is the laboratory experiment of Brüggemann et al. (2016) structured?

The experiment utilizes a word-creation game where participants earn tokens by forming or extending words. It introduces treatments like patent-like license fees and communication opportunities to observe how these factors influence innovation outcomes.

What are the key findings regarding the effectiveness of patents?

The research suggests that patents often hinder sequential innovation by discouraging sophisticated research while incentivizing basic "me-too" developments. This effect is found to be particularly evident in the software sector.

Why does the laboratory model fit the software industry better than the pharmaceutical industry?

The experiment models a small, oligopolistic market with limited firm participation, which aligns more closely with the software market structure, whereas the pharmaceutical industry functions within a broader, more diverse ecosystem that the experimental design does not fully capture.

What role does "disclosure" play in the innovation process according to the text?

Disclosure is identified as crucial for follow-on innovation, as it allows innovators to build upon existing knowledge; however, the study notes that the software industry tends to avoid disclosure, thereby complicating patent-based innovation incentives.