Data Competition

1. BUSC Data Competition
Kevin Choi

2. Introduction
● Linear Model of Innovation: Development
● Innovation Recommendation: Utility patents

3. Development
● Historical consistency
● Technological relevance

4. Development: Unique
SR → AR → development → P→ D
Brozen (1951b)
Research, engineering development, production, service

5. Development: Utility Patents
● “The use of the results of fundamental and applied
research directed to the introduction of useful materials,
devices, products, systems, and processes or the
improvement of existing ones.”
-OECD

6. Data Analysis
● Bloomberg Innovation Ranking System

10. ncvTest(model)
Non-constant Variance Score Test
Variance formula: ~ fitted.values
Chisquare = 4.806927
Df = 1
p = 0.02834556
ncvTest(model.transformed)
Non-constant Variance Score Test
Variance formula: ~ fitted.values
Chisquare = 0.2544199
Df = 1 p = 0.61398

11. Data Analysis
● Variables:
1. stem
2. scienceprof
3. utilitypatents
4. govrd
5. gsp
6. pubtechcomp

20. 2011 2012 2013 2014
Computer and electronic products 97.5 82.6 80.51851852 84.40740741
Chemical products 113.9230769 153.5806452 130.6666667 136.7692308
Publishing industries (includes software) 100.6363636 82.14285714 106.8181818 99.5
Motion pictures and sound recording
industries
318.5714286 565.25 469.4 486

21. Conclusion
1. Intensely evaluate government spending on research and development
2. Be conservative with government R&D spending
3. Consider evaluating the following industries: computer and electronics,
chemical products, publishing industries, and motion pictures
4. Invest in institutionalized innovation (education or programs) that can help
improve utility patent acceptance rate