Economics
College of Humanities and Social Sciences

Investigation in the Study of Innovation and Firm Dynamics: Computational Simulations, Declining Entrepreneurship, and Natural Language Processing

Nathan Goldschlag

Major Professor: Alex T Tabarrok, PhD, Department of Economics

Committee Members: Garett Jones, Robert Axtell

Carow Hall, #106
March 06, 2015, 10:30 AM to 07:30 AM

Abstract:

This dissertation describes several research projects aimed at better understanding innovation, technological change, and economic dynamism. 
 
Percolating Patents: Balancing the Effects of Patents on Innovation
The first chapter describes an alternative methodology in understanding the impacts of intellectual property rights on the innovation process. A computational simulation model is developed that structurally incorporates a variety of important empirical aspects of patents and technology as well as the role patents play in both incentivizing and stifling innovative activity. Simulation tests are then used to examine the conditions under which patents improve or suppress technological improvement. The results suggest that patents improve innovative outcomes when the technology space is complex and firms have limited monopoly power. Conversely, in the presence of first mover advantages and learning curves, and when innovations require relatively little investment, patents can deter innovation by reducing collaboration and forcing firms to invent around. 
 
Regulation and the Decline in American Entrepreneurship
The second chapter addresses the mounting evidence suggesting that economic dynamism and entrepreneurial activity are declining in the United States. Over the past thirty years, the annual number of new business startups and the pace of job reallocation have declined significantly. A variety of causes for these trends have been suggested, including an increasing ability of firms to respond to idiosyncratic shocks, technology induced changes in the costs of hiring and training, and increasing regulation. The second chapter combines data from the Statistics of U.S. Businesses, which contains measures of the decline in economic dynamism, with RegData, a novel dataset leveraging the textual content of the Code of Federal Regulations. RegData contains annual industry level measures of the stringency of regulation. The results suggest, somewhat surprisingly, that federal regulation is not to blame for the decline in economic dynamism. Under a variety of model specifications, federal regulations are unable to account for the decline in economic dynamism. 
 
The Knowledge Diffusion Effects of Patents
Finally, the third chapter analyses whether patents diffuse knowledge. Proponents of intellectual property rights have argued since the early 19th century that patents have positive diffusion effects. Despite the age of the argument, very little empirical evidence has been provided to either support or refute the claim that patents affect the diffusion of knowledge. This research investigates whether patents affect the diffusion of ideas by comparing the syntactic similarity of patents and a corpus of academic abstracts. The similarity between biotechnology, artificial intelligence, and machine learning patents are compared to relevant academic abstracts published before and after the grant date when the patent’s contents became public. The results suggest that the patent publication date has little to no impact on the textual content of academic discourse. In contrast, the similarity algorithm used does find diffusion effects for highly cited papers in the machine learning literature.

 

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