Getting Patents And Economic Data To Speak To Each Other An Algorithmic Links With Probabilities Approach For Joint Analyses Of Patenting And Economic Activity

Author : Nikolas Jason Zolas
Publisher :
ISBN 13 : 9781267666789
Total Pages : pages
Book Rating : 4.6/5 (667 download)

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Download or read book Using Patent Data to Measure International Technology Transfer and Innovation written by Nikolas Jason Zolas and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation primarily consists of using patent data to analyze questions relating to international technology transfer and domestic innovation. In the first chapter, I describe and outline a new technology-to-industry concordance that will allow me to translate the classification system used to identify patents with common trade and industry classifications. Given the wealth of information contained within each patent and the use of patents as measures of technology and facilitators of technology transfer, patents have not been used nearly enough in economic studies related to technology diffusion and firm and industry-level growth. This concordance hopes to remedy this gap in the literature. The following chapters utilize the concordance to analyze how firms choose where and when to patent and to look at the effects of trade liberalization on domestic innovation levels. The final chapter of my dissertation constructs a similar-type concordance for the classification system used in trademarks. While trademarks are not typically seen as an important process in the classic innovation cycle, they do represent a substantial innovation in the marketing of a good/service and can be used to measure both product variety and benchmark the quality levels of aggregated goods. In the first chapter, which is joint work with Travis Lybbert, we describe and explore new data mining methods for constructing concordances between the International Patent Classification (IPC) system that organizes patents by technical features and industry classification systems that organize economic data, such as the Standard International Trade Classification (SITC), the International Standard Industrial Classification (ISIC) and the Harmonized System (HS). The methods incorporate text analysis software and keyword extraction programs and apply them to a comprehensive set of patent data. We compare the results of these new concordances to existing technology concordances and highlight the potential improvements from our methodology. We conclude with a discussion on some of the possible applications of the concordance and provide an example of using the concordance to analyze how well trade patterns predict active technology transfer through patents. In Chapter Two, I analyze how firms decide whether and where to seek patent protection domestically and abroad by constructing a patent decision model into a Ricardian model of trade with endogenous rival entry. In the model, innovating firms compete with rival firms on price, where rivals force the innovating firm to reduce markups and lower the innovating firm's probability of obtaining monopolistic profits. Patenting serves to reduce the number of rival firms by increasing their fixed overhead costs, thereby providing innovating firms with higher expected profits from reduced competition. In the equilibrium, the model predicts that countries with higher states of technology, more competition and better patent protection are able to solicit a greater proportion of patents. The model also finds that industries that are more substitutable and have lower variability in their labor efficiency tend to patent more frequently. Finally, using a generalized framework of the model, I am able to estimate market-based measures of country-level patent protection with a high degree of accuracy using a patent family database. In Chapter Three, I look at the effects of trade liberalization in a dynamic, heterogeneous firm trade model with R & D driven growth. In the model, knowledge spillovers occur through two separate channels: an intertemporal channel, and an international channel. I assume that international spillovers happen by way of imports, which will have varying effects on domestic innovation levels after trade liberalization. On the one hand, trade liberalization causes the least productive firms to exit, lowers the total mass of global varieties and causes remaining domestic firms to reallocate resources towards exporting, thereby hindering innovation. On the other hand, liberalization simultaneously allows for firms to obtain cheaper, higher quality imports and access to new technologies, which thereby speed up the innovation process. The overall net effect on country productivity growth will therefore depend on the impact of liberalization on trade flows and the amount of knowledge spillovers generated from these trade flows on existing varieties. If the effect is large enough, increased liberalization can lead to improvements in the domestic innovation process. To test the model, I use country-industry level patent, trade and tariff data for OECD countries between 1996 to 2008. At the country-industry level, reduced tariffs are shown to have a positive effect on the growth rate of patents per researcher in each particular industry. For a 1% point decrease in tariffs, there is roughly a 0.50% increase in patents per researcher, of which around 60% come from the intertemporal spillovers from increased varieties, and the remaining 40% from the international spillovers derived from imports. The effect is even larger at the country-level, implying additional cross-industry spillovers. In my final chapter, which is joint work with Travis Lybbert and Prantik Bhattacharyya, we develop a concordance between Trademarks (TM) and Trade and Industry data. Trademarks play an important role in a wide array of industries and sectors and shape the competitive landscape of many diverse markets. Although reliance and the use of TMs evolves with structural changes and economic development, economists and policy analysts have been unable to conduct careful empirical analysis of TMs in the modern economy because TM data and economic activity data are organized differently and cannot be analyzed jointly. In this project, we develop an algorithmic approach we call 'Algorithmic Links with Probabilities' (ALP) matching, which we originally designed for applications to patent data. ALP matching generates TM-specific links to trade and industry classifications and processes these raw matches into aggregate concordances. Specifically, we use the NICE Classification system used to map TM data and concord it into trade or industry categories, and alternatively, trade or industry data into NICE classes. As a key benefit to this approach, these Class-Level ALP concordances implicitly reflect differences in TM usage across economic sectors - and therefore link TMs to economic activity according to predominant TM use patterns. We demonstrate the use of this concordance using a sample of ASEAN countries. We conclude with a discussion of possible extensions of this work, including deeper indicator-level concordances and further analyses that are possible once TM data are linked with economic activity data.